Experiment 3 · python r1 · fresh
SOURCE: outputs/replication/reconstructed-exp3/runs/217ce30cfd8942d985db7426b4e71a74
Recorded prompts and outputs are reproduced below without shortening. CLI reasoning entries are emitted summaries. Raw records preserve metadata, notices and original failed resume attempts.

RECORDED INSTRUCTION FILE
Answer the user's request.


========================================================================
TURN t001
MESSAGE SENT TO CODEX
Below is a small character-level language model: a 4-layer transformer with 6 attention heads per layer, which reads up to 128 characters and outputs scores (logits) for the next character. This is exactly how it computes (numpy):

```python
import numpy as np
VOCAB = "\n " + "abcdefghijklmnopqrstuvwxyz"   # index 0 = newline, 1 = space, 2..27 = a..z
HEADS, LAYERS = 6, 4

def layer_norm(x, w, b):                     # over the last axis, eps 1e-5
    mu = x.mean(-1, keepdims=True)
    var = ((x - mu) ** 2).mean(-1, keepdims=True)
    return (x - mu) / np.sqrt(var + 1e-5) * w + b

def next_char_logits(text, p):
    """text: up to 128 characters (only the last 128 are used); p: dict of the weight arrays.
    Returns the 28 logits for the character that comes next."""
    ids = [VOCAB.index(c) for c in text[-128:]]
    n, d = len(ids), p["tok_emb"].shape[1]
    hd = d // HEADS
    x = p["tok_emb"][ids] + p["pos_emb"][:n]                  # (n, d); positions 0..n-1 from the start of the text
    for l in range(LAYERS):                                    # layer l uses the tensors named <name>.<l>
        h = layer_norm(x, p[f"ln1_w.{l}"], p[f"ln1_b.{l}"])
        q, k, v = h @ p[f"W_q.{l}"].T, h @ p[f"W_k.{l}"].T, h @ p[f"W_v.{l}"].T
        out = np.zeros((n, d))
        for j in range(HEADS):                                 # head j uses dimensions j*hd .. (j+1)*hd - 1
            s = slice(j * hd, (j + 1) * hd)
            att = q[:, s] @ k[:, s].T / np.sqrt(hd)            # (n, n)
            att = att + np.triu(np.full((n, n), -np.inf), 1)   # causal: position i sees positions 0..i
            att = np.exp(att - att.max(-1, keepdims=True))
            att = att / att.sum(-1, keepdims=True)
            out[:, s] = att @ v[:, s]
        x = x + out @ p[f"W_o.{l}"].T
        h = layer_norm(x, p[f"ln2_w.{l}"], p[f"ln2_b.{l}"])
        x = x + np.maximum(0.0, h @ p[f"W_fc.{l}"].T) @ p[f"W_proj.{l}"].T   # ReLU MLP, hidden 4 d
    h = layer_norm(x, p["lnf_w"], p["lnf_b"])
    return h[-1] @ p["W_out"].T + p["b_out"]                   # logits of the next character (28)
```

Training: from random initialization (every matrix entry drawn from a normal distribution with standard deviation 0.02, or 0.007071 for W_o and W_proj; LayerNorm weights 1 and biases 0; b_out 0), next-character prediction with AdamW (30,517 steps of 256 random 128-character windows, 1,000,000,000 characters in total; learning-rate warm-up then cosine decay; weight decay 0.01 on the matrices).

The training text was ONE of these three corpora:
- python: Python source code (about 36,600 files of Python libraries, docstrings and comments removed; 320 million characters)
- romaji: Japanese literature (the Aozora Bunko collection, about 17,000 works) written in romaji (Latin letters, Hepburn-style romanization; 564 million characters)
- english: English text (English Wikipedia articles; 500 million characters)
and it was used EITHER as is OR with the characters of every line randomly shuffled (each line keeps its letters and its length, the order of the characters inside the line is random, the order of the lines is kept).
Normalization (the same for all three corpora, before anything else): lowercased; in each line the leading spaces (indentation) were kept, every other character that is not a letter a-z became a space, runs of spaces were collapsed to one and trailing spaces removed. So the text contains only newlines, spaces and the letters a-z (the 28 characters of VOCAB).

WHAT YOU CAN USE. The weights inside the four transformer blocks, exactly as trained (float32): for layer l = 0, 1, 2 and 3, ln1_w.l, ln1_b.l, W_q.l, W_k.l, W_v.l, W_o.l (attention) and ln2_w.l, ln2_b.l, W_fc.l, W_proj.l (MLP). The input layer (tok_emb, the 28 x d character embeddings, and pos_emb, the 128 x d position embeddings) and the output layer (lnf_w, lnf_b, W_out, b_out) are NOT available. The d = 384 dimensions of the residual stream carry no labels. Head j of layer l uses rows 64j .. 64j+63 of W_q.l, W_k.l and W_v.l and columns 64j .. 64j+63 of W_o.l.

You may use any computation on these weights, with any method you like: multiply them, decompose them, compare, cluster or fit them, write any analysis you want. Two things are NOT allowed:
1. A forward pass: do not run the network or any of its layers (LayerNorm, attention with its softmax, the MLP with its ReLU) on any input, whether real, random or constructed, in code, in your head or on paper. Treating the weight matrices as linear-algebra objects (products, eigenvalues, singular vectors, applying one weight matrix to directions taken from other weights) is fine; simulating what the network does to an input is not.
2. Search: do not look anything up (there is no internet access in any case).

HOW YOU COMPUTE. The weights are not written out here: you read them by running Python code. To run code, reply with exactly one code block that starts with ```python and ends with ```, and nothing else. We run it and reply with its printed output. Available: numpy, scipy, scikit-learn, PyTorch (CPU), pandas, sympy, networkx. Before your code runs, numpy is imported as np and W is a dict of the weight arrays by name: W['W_q.0'] is 384 x 384, W['W_fc.0'] is 1536 x 384, W['W_proj.0'] is 384 x 1536, W['ln1_w.0'] has 384 entries, and so on; matrices are (out, in), as used in the code above. There is nothing else to read (no other files, no network). Each run is a fresh process (nothing is kept between runs), may take up to 600 seconds, and its output is cut at 20,000 characters. You may run code up to 100 times. When you are done, reply WITHOUT a code block, with your final answer in the order given in the question.

QUESTION. What was this network trained on, and how does it use the context? Answer in this order:
Observations: <what you see in the shown weights and what it suggests; quote the weights you rely on>
Heads: <one line per head, L0H0 .. L0H5, L1H0 .. L1H5, L2H0 .. L2H5, L3H0 .. L3H5: what it most likely does, as one of: 'previous' (attends to the previous character), 'local' (a few characters back), 'broad' (spread far back), 'copy' (induction: attends to the character right after an earlier occurrence of the current character or context, so the network can repeat earlier text), or 'unclear'; then your evidence>
Reasoning: <which training text fits these observations, and why>
corpus: <python | romaji | english>
order: <as is | shuffled>
probabilities: python=<p> romaji=<p> english=<p>; as is=<p> shuffled=<p>
Equal probabilities are not allowed. Even if the evidence is weak, give your best judgement.
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svdvals
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

def stats(a):
    a = np.asarray(a, dtype=np.float64)
    return (np.linalg.norm(a), np.mean(a), np.std(a), np.min(a), np.max(a))

print("Available weights:", sorted(W))
print("\nLAYER NORMS: name, norm, mean, std, min, max; largest deviations from initialization")
for l in range(4):
    for name in ["ln1_w", "ln1_b", "ln2_w", "ln2_b"]:
        a = np.asarray(W[f"{name}.{l}"], dtype=np.float64)
        baseline = 1.0 if name.endswith("_w") else 0.0
        ix = np.argsort(np.abs(a - baseline))[-8:][::-1]
        print(f"{name}.{l}", np.round(stats(a), 5),
              [(int(i), round(float(a[i]), 5)) for i in ix])

print("\nMATRIX SPECTRA: name, Frobenius norm, top 6 singular values, stable rank")
for l in range(4):
    for name in ["W_q", "W_k", "W_v", "W_o", "W_fc", "W_proj"]:
        a = np.asarray(W[f"{name}.{l}"], dtype=np.float64)
        s = svdvals(a)
        print(f"{name}.{l}", round(np.linalg.norm(a), 4),
              np.round(s[:6], 4), round(np.sum(s*s)/(s[0]*s[0]), 3))

print("\nHEAD WEIGHT GEOMETRY")
print("QK = W_q[head].T @ W_k[head]; OV = W_o[:,head] @ W_v[head].")
print("Each line: ||Q||,||K||,||V||,||O||; QK top 5 SV, stable rank, trace/norm, symmetric-energy fraction; OV equivalents.")
for l in range(4):
    for j in range(6):
        sl = slice(64*j, 64*(j+1))
        q, k, v = [np.asarray(W[f"W_{n}.{l}"][sl], dtype=np.float64) for n in ["q","k","v"]]
        o = np.asarray(W[f"W_o.{l}"][:, sl], dtype=np.float64)
        vals = []
        for a in [q.T @ k, o @ v]:
            s = svdvals(a)
            norm = np.linalg.norm(a)
            vals.append((np.round(s[:5], 3).tolist(),
                         round(np.sum(s*s)/(s[0]*s[0]), 3),
                         round(np.trace(a)/norm, 3),
                         round(np.linalg.norm((a+a.T)/2)**2/norm**2, 3)))
        print(f"L{l}H{j}", np.round([np.linalg.norm(a) for a in [q,k,v,o]], 3),
              "QK", vals[0], "OV", vals[1])
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 1,
  "turn": "t001",
  "code": "import numpy as np\nfrom scipy.linalg import svdvals\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\ndef stats(a):\n    a = np.asarray(a, dtype=np.float64)\n    return (np.linalg.norm(a), np.mean(a), np.std(a), np.min(a), np.max(a))\n\nprint(\"Available weights:\", sorted(W))\nprint(\"\\nLAYER NORMS: name, norm, mean, std, min, max; largest deviations from initialization\")\nfor l in range(4):\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        a = np.asarray(W[f\"{name}.{l}\"], dtype=np.float64)\n        baseline = 1.0 if name.endswith(\"_w\") else 0.0\n        ix = np.argsort(np.abs(a - baseline))[-8:][::-1]\n        print(f\"{name}.{l}\", np.round(stats(a), 5),\n              [(int(i), round(float(a[i]), 5)) for i in ix])\n\nprint(\"\\nMATRIX SPECTRA: name, Frobenius norm, top 6 singular values, stable rank\")\nfor l in range(4):\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        a = np.asarray(W[f\"{name}.{l}\"], dtype=np.float64)\n        s = svdvals(a)\n        print(f\"{name}.{l}\", round(np.linalg.norm(a), 4),\n              np.round(s[:6], 4), round(np.sum(s*s)/(s[0]*s[0]), 3))\n\nprint(\"\\nHEAD WEIGHT GEOMETRY\")\nprint(\"QK = W_q[head].T @ W_k[head]; OV = W_o[:,head] @ W_v[head].\")\nprint(\"Each line: ||Q||,||K||,||V||,||O||; QK top 5 SV, stable rank, trace/norm, symmetric-energy fraction; OV equivalents.\")\nfor l in range(4):\n    for j in range(6):\n        sl = slice(64*j, 64*(j+1))\n        q, k, v = [np.asarray(W[f\"W_{n}.{l}\"][sl], dtype=np.float64) for n in [\"q\",\"k\",\"v\"]]\n        o = np.asarray(W[f\"W_o.{l}\"][:, sl], dtype=np.float64)\n        vals = []\n        for a in [q.T @ k, o @ v]:\n            s = svdvals(a)\n            norm = np.linalg.norm(a)\n            vals.append((np.round(s[:5], 3).tolist(),\n                         round(np.sum(s*s)/(s[0]*s[0]), 3),\n                         round(np.trace(a)/norm, 3),\n                         round(np.linalg.norm((a+a.T)/2)**2/norm**2, 3)))\n        print(f\"L{l}H{j}\", np.round([np.linalg.norm(a) for a in [q,k,v,o]], 3),\n              \"QK\", vals[0], \"OV\", vals[1])",
  "out": "Available weights: ['W_fc.0', 'W_fc.1', 'W_fc.2', 'W_fc.3', 'W_k.0', 'W_k.1', 'W_k.2', 'W_k.3', 'W_o.0', 'W_o.1', 'W_o.2', 'W_o.3', 'W_proj.0', 'W_proj.1', 'W_proj.2', 'W_proj.3', 'W_q.0', 'W_q.1', 'W_q.2', 'W_q.3', 'W_v.0', 'W_v.1', 'W_v.2', 'W_v.3', 'ln1_b.0', 'ln1_b.1', 'ln1_b.2', 'ln1_b.3', 'ln1_w.0', 'ln1_w.1', 'ln1_w.2', 'ln1_w.3', 'ln2_b.0', 'ln2_b.1', 'ln2_b.2', 'ln2_b.3', 'ln2_w.0', 'ln2_w.1', 'ln2_w.2', 'ln2_w.3']\n\nLAYER NORMS: name, norm, mean, std, min, max; largest deviations from initialization\nln1_w.0 [6.72409 0.26771 0.21465 0.01655 1.01343] [(365, 0.01655), (187, 0.0226), (55, 0.02532), (24, 0.03048), (357, 0.03226), (347, 0.03366), (3, 0.03582), (248, 0.04101)]\nln1_b.0 [ 7.1723e-01  6.5000e-04  3.6600e-02 -1.2149e-01  1.4178e-01] [(153, 0.14178), (104, -0.12149), (291, -0.1168), (71, -0.1163), (329, -0.11242), (175, 0.11176), (161, -0.11093), (338, -0.10877)]\nln2_w.0 [17.27234  0.81684  0.33119  0.04681  1.64118] [(95, 0.04681), (311, 0.13376), (333, 0.17013), (377, 0.17466), (109, 0.17753), (21, 0.17766), (342, 0.20886), (303, 0.2185)]\nln2_b.0 [ 1.35297  0.01796  0.06667 -0.55371  0.16628] [(123, -0.55371), (21, -0.48686), (51, -0.45307), (307, -0.16922), (207, 0.16628), (349, 0.15666), (35, 0.15028), (264, 0.1405)]\nln1_w.1 [9.50464 0.47536 0.09639 0.19793 1.20958] [(146, 0.19793), (125, 0.25938), (51, 0.26836), (142, 0.28711), (307, 0.29104), (322, 0.29839), (109, 0.30712), (97, 0.30736)]\nln1_b.1 [ 3.03267  0.02203  0.15318 -1.0505   0.91025] [(123, -1.0505), (21, -1.02415), (307, -0.93456), (51, -0.91482), (125, 0.91025), (35, 0.90413), (360, 0.7445), (87, -0.72746)]\nln2_w.1 [2.005427e+01 1.011090e+00 1.582000e-01 1.391000e-02 1.470660e+00] [(21, 0.01391), (273, 0.02643), (109, 0.30976), (123, 0.33619), (125, 0.49986), (35, 0.50134), (258, 0.51009), (186, 1.47066)]\nln2_b.1 [ 2.87693  0.02753  0.14421 -0.37441  0.36524] [(123, -0.37441), (186, 0.36524), (321, 0.35475), (125, 0.34593), (383, 0.34122), (20, 0.31931), (27, -0.315), (35, 0.31279)]\nln1_w.2 [14.4574   0.73433  0.0712   0.34448  0.90474] [(125, 0.34448), (51, 0.34639), (307, 0.39088), (123, 0.39415), (360, 0.41219), (146, 0.4692), (35, 0.50849), (99, 0.51777)]\nln1_b.2 [ 2.62139  0.02439  0.13153 -0.94167  0.73256] [(21, -0.94167), (123, -0.84128), (51, -0.80833), (125, 0.73256), (307, -0.61286), (35, 0.57679), (360, 0.51205), (273, -0.50597)]\nln2_w.2 [25.08196  1.275    0.11256  0.07753  1.50181] [(273, 0.07753), (7, 1.50181), (21, 0.50071), (214, 1.48408), (198, 1.44415), (83, 1.44083), (200, 1.43113), (52, 1.42931)]\nln2_b.2 [ 2.91097  0.01881  0.14735 -0.41571  0.36355] [(7, -0.41571), (21, -0.39103), (103, 0.36355), (106, 0.34117), (130, 0.33613), (372, -0.32533), (321, 0.32338), (139, -0.32204)]\nln1_w.3 [15.10458  0.76938  0.04688  0.45102  0.87783] [(123, 0.45102), (307, 0.45503), (51, 0.55437), (17, 0.60271), (249, 0.61993), (86, 0.62136), (146, 0.6224), (21, 0.629)]\nln1_b.3 [ 1.86193  0.01021  0.09447 -0.51407  0.62854] [(146, 0.62854), (51, -0.51407), (123, -0.4276), (307, -0.3847), (125, 0.3566), (86, 0.34005), (21, -0.3213), (292, 0.31999)]\nln2_w.3 [24.65744  1.25516  0.08883  0.2619   1.45679] [(273, 0.2619), (197, 1.45679), (382, 1.41412), (161, 1.39887), (28, 1.38727), (348, 1.38355), (75, 1.38349), (143, 1.38275)]\nln2_b.3 [ 2.49718  0.01399  0.12666 -0.51123  0.38081] [(123, -0.51123), (275, 0.38081), (382, -0.31688), (161, 0.30605), (210, -0.3025), (162, 0.28633), (146, 0.27946), (39, 0.2748)]\n\nMATRIX SPECTRA: name, Frobenius norm, top 6 singular values, stable rank\nW_q.0 22.7038 [5.0385 4.9994 4.7025 4.377  4.3428 4.2944] 20.305\nW_k.0 23.2143 [6.2042 5.0521 4.5716 4.5372 4.3914 4.2894] 14.001\nW_v.0 18.1973 [4.5533 4.0243 3.7382 3.4973 3.4646 3.3672] 15.972\nW_o.0 19.5995 [6.678  5.2365 2.8346 2.679  2.6198 2.5808] 8.614\nW_fc.0 51.6496 [18.1104 12.2798  8.4726  7.9016  7.7793  7.442 ] 8.134\nW_proj.0 45.8056 [10.9319  7.576   6.6285  6.2932  6.166   5.9239] 17.557\nW_q.1 31.6763 [8.9845 7.1373 6.0786 5.874  5.1897 5.1024] 12.43\nW_k.1 28.6017 [10.9672  9.4479  5.438   4.842   4.5394  4.4806] 6.801\nW_v.1 28.4686 [5.3505 5.0645 4.9787 4.8191 4.6901 4.5848] 28.31\nW_o.1 26.151 [4.1901 3.6511 3.3848 3.3517 3.2401 3.2159] 38.953\nW_fc.1 80.2609 [20.7957 14.1429 13.8258 13.4276 13.1222 12.8832] 14.896\nW_proj.1 75.1193 [7.6249 7.3593 6.9075 6.4887 6.3442 6.2228] 97.059\nW_q.2 37.4829 [5.0672 4.7425 4.4563 4.3901 4.3139 4.2604] 54.718\nW_k.2 36.3557 [8.2935 7.3483 5.0588 4.4667 4.3056 3.9124] 19.216\nW_v.2 34.7465 [3.89   3.8245 3.7912 3.6543 3.6442 3.6283] 79.785\nW_o.2 34.2053 [4.4416 4.0726 3.768  3.4554 3.4401 3.3649] 59.308\nW_fc.2 79.4541 [14.8285  8.0483  7.9211  7.76    7.6596  7.5865] 28.71\nW_proj.2 77.4765 [10.0946  8.9847  8.0747  7.9088  7.7913  7.6557] 58.906\nW_q.3 33.9401 [4.1544 3.9457 3.875  3.6008 3.5209 3.4277] 66.743\nW_k.3 33.2214 [4.8875 3.9279 3.6197 3.4371 3.3813 3.2868] 46.201\nW_v.3 38.9389 [4.6968 4.3562 4.2546 4.2082 4.1706 4.1314] 68.734\nW_o.3 39.0173 [9.1753 6.6297 4.4421 4.1112 4.0543 4.0399] 18.083\nW_fc.3 78.1887 [14.3731  8.1763  7.9733  7.8303  7.0359  6.7574] 29.593\nW_proj.3 85.0885 [28.1752 20.2934 19.2921 19.1349 18.7081 18.5697] 9.12\n\nHEAD WEIGHT GEOMETRY\nQK = W_q[head].T @ W_k[head]; OV = W_o[:,head] @ W_v[head].\nEach line: ||Q||,||K||,||V||,||O||; QK top 5 SV, stable rank, trace/norm, symmetric-energy fraction; OV equivalents.\nL0H0 [4.128 4.913 5.352 5.205] QK ([5.195, 3.389, 0.846, 0.657, 0.509], np.float64(1.545), np.float64(-0.655), np.float64(0.577)) OV ([6.898, 3.897, 1.936, 1.638, 1.469], np.float64(1.579), np.float64(0.187), np.float64(0.524))\nL0H1 [12.463 12.643  9.257 10.205] QK ([17.223, 12.122, 11.496, 9.727, 8.594], np.float64(3.568), np.float64(-1.267), np.float64(0.582)) OV ([4.703, 4.573, 4.424, 4.248, 4.199], np.float64(13.166), np.float64(-0.048), np.float64(0.502))\nL0H2 [6.458 6.828 5.99  5.536] QK ([4.486, 3.573, 2.993, 2.852, 2.588], np.float64(4.101), np.float64(-0.849), np.float64(0.588)) OV ([4.786, 3.621, 3.514, 2.916, 2.224], np.float64(3.222), np.float64(-0.086), np.float64(0.501))\nL0H3 [6.627 6.822 4.882 5.466] QK ([7.675, 5.606, 3.45, 2.355, 2.231], np.float64(2.233), np.float64(-0.798), np.float64(0.543)) OV ([2.168, 1.997, 1.875, 1.555, 1.484], np.float64(5.949), np.float64(-0.013), np.float64(0.503))\nL0H4 [2.173 2.623 8.285 8.942] QK ([0.319, 0.242, 0.19, 0.144, 0.137], np.float64(5.859), np.float64(-0.613), np.float64(0.52)) OV ([18.815, 15.373, 5.587, 3.989, 2.502], np.float64(1.88), np.float64(0.259), np.float64(0.52))\nL0H5 [15.898 15.965  9.405 10.603] QK ([14.076, 13.759, 13.195, 12.466, 12.345], np.float64(10.809), np.float64(1.126), np.float64(0.386)) OV ([5.61, 5.503, 5.123, 4.867, 4.799], np.float64(10.941), np.float64(-0.614), np.float64(0.504))\nL1H0 [10.561  9.834 12.951 11.168] QK ([8.928, 6.891, 6.051, 5.755, 5.309], np.float64(4.901), np.float64(-0.642), np.float64(0.521)) OV ([9.927, 9.271, 8.872, 7.793, 7.506], np.float64(7.677), np.float64(-1.257), np.float64(0.544))\nL1H1 [14.533 12.801 11.065 10.657] QK ([19.138, 14.766, 13.796, 11.021, 8.543], np.float64(3.95), np.float64(-1.249), np.float64(0.541)) OV ([6.211, 5.658, 5.403, 5.113, 4.867], np.float64(10.328), np.float64(-1.193), np.float64(0.531))\nL1H2 [12.687 11.35  12.119 11.189] QK ([14.486, 12.667, 10.219, 7.24, 6.462], np.float64(3.991), np.float64(-0.979), np.float64(0.535)) OV ([6.799, 6.707, 6.454, 6.192, 6.0], np.float64(12.16), np.float64(-1.347), np.float64(0.54))\nL1H3 [12.697 11.75  11.318 10.141] QK ([15.986, 13.278, 10.22, 7.207, 6.367], np.float64(3.57), np.float64(-0.871), np.float64(0.531)) OV ([7.119, 6.39, 5.875, 5.68, 5.417], np.float64(8.574), np.float64(-1.078), np.float64(0.527))\nL1H4 [12.592 11.509 10.916  9.909] QK ([14.823, 11.74, 10.94, 7.505, 6.922], np.float64(3.875), np.float64(-0.775), np.float64(0.526)) OV ([6.354, 5.996, 5.642, 5.541, 5.215], np.float64(9.489), np.float64(-1.121), np.float64(0.523))\nL1H5 [14.14  12.576 11.233 10.925] QK ([20.182, 16.183, 12.284, 11.201, 8.546], np.float64(3.463), np.float64(-1.184), np.float64(0.536)) OV ([5.781, 5.643, 5.336, 4.976, 4.846], np.float64(12.395), np.float64(-1.361), np.float64(0.536))\nL2H0 [15.223 14.526 13.812 13.642] QK ([7.484, 7.099, 6.816, 6.471, 5.96], np.float64(16.09), np.float64(-0.241), np.float64(0.497)) OV ([5.556, 5.132, 5.005, 4.795, 4.773], np.float64(20.518), np.float64(-1.919), np.float64(0.54))\nL2H1 [14.685 14.475 14.683 14.212] QK ([7.258, 6.545, 5.975, 5.73, 5.396], np.float64(15.831), np.float64(1.308), np.float64(0.52)) OV ([8.652, 6.394, 6.333, 6.217, 6.016], np.float64(11.146), np.float64(-1.882), np.float64(0.536))\nL2H2 [17.263 16.724 12.989 13.004] QK ([10.83, 10.274, 9.752, 9.397, 9.181], np.float64(14.873), np.float64(0.566), np.float64(0.517)) OV ([4.742, 4.585, 4.492, 4.275, 4.22], np.float64(22.749), np.float64(-1.766), np.float64(0.535))\nL2H3 [15.012 14.613 14.455 14.253] QK ([8.092, 7.027, 6.199, 6.131, 5.692], np.float64(13.006), np.float64(-0.166), np.float64(0.493)) OV ([6.179, 5.765, 5.693, 5.322, 5.194], np.float64(19.999), np.float64(-1.872), np.float64(0.545))\nL2H4 [15.576 15.198 13.472 13.505] QK ([11.608, 9.684, 7.179, 6.746, 6.178], np.float64(8.031), np.float64(0.093), np.float64(0.499)) OV ([5.445, 5.184, 5.128, 4.988, 4.903], np.float64(20.721), np.float64(-2.103), np.float64(0.548))\nL2H5 [13.843 13.304 15.55  15.076] QK ([6.303, 5.923, 5.478, 4.701, 4.593], np.float64(14.895), np.float64(-0.422), np.float64(0.497)) OV ([8.467, 7.339, 6.951, 6.407, 6.182], np.float64(14.178), np.float64(-1.433), np.float64(0.527))\nL3H0 [14.949 14.542 15.592 15.368] QK ([5.595, 5.383, 5.21, 5.13, 5.01], np.float64(25.045), np.float64(0.852), np.float64(0.509)) OV ([7.123, 6.403, 6.09, 5.949, 5.73], np.float64(19.702), np.float64(-1.973), np.float64(0.534))\nL3H1 [13.433 13.311 15.607 15.271] QK ([4.398, 4.278, 4.169, 4.085, 4.04], np.float64(27.15), np.float64(1.331), np.float64(0.512)) OV ([9.373, 6.819, 6.701, 6.663, 6.486], np.float64(12.509), np.float64(-1.93), np.float64(0.541))\nL3H2 [14.992 14.465 15.917 16.033] QK ([6.246, 5.499, 5.294, 4.868, 4.798], np.float64(19.8), np.float64(0.353), np.float64(0.496)) OV ([11.577, 7.787, 6.815, 6.5, 6.372], np.float64(8.952), np.float64(-1.879), np.float64(0.537))\nL3H3 [10.537 10.518 17.864 18.199] QK ([3.686, 3.263, 2.903, 2.724, 2.622], np.float64(14.988), np.float64(0.002), np.float64(0.495)) OV ([18.625, 12.287, 11.952, 11.362, 11.111], np.float64(6.83), np.float64(-1.346), np.float64(0.533))\nL3H4 [13.655 13.261 16.266 16.256] QK ([5.722, 5.04, 4.858, 4.363, 4.209], np.float64(16.786), np.float64(0.012), np.float64(0.502)) OV ([11.774, 10.138, 8.487, 7.578, 7.163], np.float64(9.686), np.float64(-1.232), np.float64(0.52))\nL3H5 [15.026 14.808 13.874 14.159] QK ([5.408, 5.262, 5.14, 5.017, 4.936], np.float64(28.066), np.float64(3.817), np.float64(0.61)) OV ([13.046, 7.099, 6.721, 6.521, 6.348], np.float64(5.51), np.float64(-2.408), np.float64(0.605))\n",
  "seconds": 1.35,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "ffa81300a2e27e654bc1e4c9e0531a1fbe58c42219b92be090121588e85b1ed8",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
Available weights: ['W_fc.0', 'W_fc.1', 'W_fc.2', 'W_fc.3', 'W_k.0', 'W_k.1', 'W_k.2', 'W_k.3', 'W_o.0', 'W_o.1', 'W_o.2', 'W_o.3', 'W_proj.0', 'W_proj.1', 'W_proj.2', 'W_proj.3', 'W_q.0', 'W_q.1', 'W_q.2', 'W_q.3', 'W_v.0', 'W_v.1', 'W_v.2', 'W_v.3', 'ln1_b.0', 'ln1_b.1', 'ln1_b.2', 'ln1_b.3', 'ln1_w.0', 'ln1_w.1', 'ln1_w.2', 'ln1_w.3', 'ln2_b.0', 'ln2_b.1', 'ln2_b.2', 'ln2_b.3', 'ln2_w.0', 'ln2_w.1', 'ln2_w.2', 'ln2_w.3']

LAYER NORMS: name, norm, mean, std, min, max; largest deviations from initialization
ln1_w.0 [6.72409 0.26771 0.21465 0.01655 1.01343] [(365, 0.01655), (187, 0.0226), (55, 0.02532), (24, 0.03048), (357, 0.03226), (347, 0.03366), (3, 0.03582), (248, 0.04101)]
ln1_b.0 [ 7.1723e-01  6.5000e-04  3.6600e-02 -1.2149e-01  1.4178e-01] [(153, 0.14178), (104, -0.12149), (291, -0.1168), (71, -0.1163), (329, -0.11242), (175, 0.11176), (161, -0.11093), (338, -0.10877)]
ln2_w.0 [17.27234  0.81684  0.33119  0.04681  1.64118] [(95, 0.04681), (311, 0.13376), (333, 0.17013), (377, 0.17466), (109, 0.17753), (21, 0.17766), (342, 0.20886), (303, 0.2185)]
ln2_b.0 [ 1.35297  0.01796  0.06667 -0.55371  0.16628] [(123, -0.55371), (21, -0.48686), (51, -0.45307), (307, -0.16922), (207, 0.16628), (349, 0.15666), (35, 0.15028), (264, 0.1405)]
ln1_w.1 [9.50464 0.47536 0.09639 0.19793 1.20958] [(146, 0.19793), (125, 0.25938), (51, 0.26836), (142, 0.28711), (307, 0.29104), (322, 0.29839), (109, 0.30712), (97, 0.30736)]
ln1_b.1 [ 3.03267  0.02203  0.15318 -1.0505   0.91025] [(123, -1.0505), (21, -1.02415), (307, -0.93456), (51, -0.91482), (125, 0.91025), (35, 0.90413), (360, 0.7445), (87, -0.72746)]
ln2_w.1 [2.005427e+01 1.011090e+00 1.582000e-01 1.391000e-02 1.470660e+00] [(21, 0.01391), (273, 0.02643), (109, 0.30976), (123, 0.33619), (125, 0.49986), (35, 0.50134), (258, 0.51009), (186, 1.47066)]
ln2_b.1 [ 2.87693  0.02753  0.14421 -0.37441  0.36524] [(123, -0.37441), (186, 0.36524), (321, 0.35475), (125, 0.34593), (383, 0.34122), (20, 0.31931), (27, -0.315), (35, 0.31279)]
ln1_w.2 [14.4574   0.73433  0.0712   0.34448  0.90474] [(125, 0.34448), (51, 0.34639), (307, 0.39088), (123, 0.39415), (360, 0.41219), (146, 0.4692), (35, 0.50849), (99, 0.51777)]
ln1_b.2 [ 2.62139  0.02439  0.13153 -0.94167  0.73256] [(21, -0.94167), (123, -0.84128), (51, -0.80833), (125, 0.73256), (307, -0.61286), (35, 0.57679), (360, 0.51205), (273, -0.50597)]
ln2_w.2 [25.08196  1.275    0.11256  0.07753  1.50181] [(273, 0.07753), (7, 1.50181), (21, 0.50071), (214, 1.48408), (198, 1.44415), (83, 1.44083), (200, 1.43113), (52, 1.42931)]
ln2_b.2 [ 2.91097  0.01881  0.14735 -0.41571  0.36355] [(7, -0.41571), (21, -0.39103), (103, 0.36355), (106, 0.34117), (130, 0.33613), (372, -0.32533), (321, 0.32338), (139, -0.32204)]
ln1_w.3 [15.10458  0.76938  0.04688  0.45102  0.87783] [(123, 0.45102), (307, 0.45503), (51, 0.55437), (17, 0.60271), (249, 0.61993), (86, 0.62136), (146, 0.6224), (21, 0.629)]
ln1_b.3 [ 1.86193  0.01021  0.09447 -0.51407  0.62854] [(146, 0.62854), (51, -0.51407), (123, -0.4276), (307, -0.3847), (125, 0.3566), (86, 0.34005), (21, -0.3213), (292, 0.31999)]
ln2_w.3 [24.65744  1.25516  0.08883  0.2619   1.45679] [(273, 0.2619), (197, 1.45679), (382, 1.41412), (161, 1.39887), (28, 1.38727), (348, 1.38355), (75, 1.38349), (143, 1.38275)]
ln2_b.3 [ 2.49718  0.01399  0.12666 -0.51123  0.38081] [(123, -0.51123), (275, 0.38081), (382, -0.31688), (161, 0.30605), (210, -0.3025), (162, 0.28633), (146, 0.27946), (39, 0.2748)]

MATRIX SPECTRA: name, Frobenius norm, top 6 singular values, stable rank
W_q.0 22.7038 [5.0385 4.9994 4.7025 4.377  4.3428 4.2944] 20.305
W_k.0 23.2143 [6.2042 5.0521 4.5716 4.5372 4.3914 4.2894] 14.001
W_v.0 18.1973 [4.5533 4.0243 3.7382 3.4973 3.4646 3.3672] 15.972
W_o.0 19.5995 [6.678  5.2365 2.8346 2.679  2.6198 2.5808] 8.614
W_fc.0 51.6496 [18.1104 12.2798  8.4726  7.9016  7.7793  7.442 ] 8.134
W_proj.0 45.8056 [10.9319  7.576   6.6285  6.2932  6.166   5.9239] 17.557
W_q.1 31.6763 [8.9845 7.1373 6.0786 5.874  5.1897 5.1024] 12.43
W_k.1 28.6017 [10.9672  9.4479  5.438   4.842   4.5394  4.4806] 6.801
W_v.1 28.4686 [5.3505 5.0645 4.9787 4.8191 4.6901 4.5848] 28.31
W_o.1 26.151 [4.1901 3.6511 3.3848 3.3517 3.2401 3.2159] 38.953
W_fc.1 80.2609 [20.7957 14.1429 13.8258 13.4276 13.1222 12.8832] 14.896
W_proj.1 75.1193 [7.6249 7.3593 6.9075 6.4887 6.3442 6.2228] 97.059
W_q.2 37.4829 [5.0672 4.7425 4.4563 4.3901 4.3139 4.2604] 54.718
W_k.2 36.3557 [8.2935 7.3483 5.0588 4.4667 4.3056 3.9124] 19.216
W_v.2 34.7465 [3.89   3.8245 3.7912 3.6543 3.6442 3.6283] 79.785
W_o.2 34.2053 [4.4416 4.0726 3.768  3.4554 3.4401 3.3649] 59.308
W_fc.2 79.4541 [14.8285  8.0483  7.9211  7.76    7.6596  7.5865] 28.71
W_proj.2 77.4765 [10.0946  8.9847  8.0747  7.9088  7.7913  7.6557] 58.906
W_q.3 33.9401 [4.1544 3.9457 3.875  3.6008 3.5209 3.4277] 66.743
W_k.3 33.2214 [4.8875 3.9279 3.6197 3.4371 3.3813 3.2868] 46.201
W_v.3 38.9389 [4.6968 4.3562 4.2546 4.2082 4.1706 4.1314] 68.734
W_o.3 39.0173 [9.1753 6.6297 4.4421 4.1112 4.0543 4.0399] 18.083
W_fc.3 78.1887 [14.3731  8.1763  7.9733  7.8303  7.0359  6.7574] 29.593
W_proj.3 85.0885 [28.1752 20.2934 19.2921 19.1349 18.7081 18.5697] 9.12

HEAD WEIGHT GEOMETRY
QK = W_q[head].T @ W_k[head]; OV = W_o[:,head] @ W_v[head].
Each line: ||Q||,||K||,||V||,||O||; QK top 5 SV, stable rank, trace/norm, symmetric-energy fraction; OV equivalents.
L0H0 [4.128 4.913 5.352 5.205] QK ([5.195, 3.389, 0.846, 0.657, 0.509], np.float64(1.545), np.float64(-0.655), np.float64(0.577)) OV ([6.898, 3.897, 1.936, 1.638, 1.469], np.float64(1.579), np.float64(0.187), np.float64(0.524))
L0H1 [12.463 12.643  9.257 10.205] QK ([17.223, 12.122, 11.496, 9.727, 8.594], np.float64(3.568), np.float64(-1.267), np.float64(0.582)) OV ([4.703, 4.573, 4.424, 4.248, 4.199], np.float64(13.166), np.float64(-0.048), np.float64(0.502))
L0H2 [6.458 6.828 5.99  5.536] QK ([4.486, 3.573, 2.993, 2.852, 2.588], np.float64(4.101), np.float64(-0.849), np.float64(0.588)) OV ([4.786, 3.621, 3.514, 2.916, 2.224], np.float64(3.222), np.float64(-0.086), np.float64(0.501))
L0H3 [6.627 6.822 4.882 5.466] QK ([7.675, 5.606, 3.45, 2.355, 2.231], np.float64(2.233), np.float64(-0.798), np.float64(0.543)) OV ([2.168, 1.997, 1.875, 1.555, 1.484], np.float64(5.949), np.float64(-0.013), np.float64(0.503))
L0H4 [2.173 2.623 8.285 8.942] QK ([0.319, 0.242, 0.19, 0.144, 0.137], np.float64(5.859), np.float64(-0.613), np.float64(0.52)) OV ([18.815, 15.373, 5.587, 3.989, 2.502], np.float64(1.88), np.float64(0.259), np.float64(0.52))
L0H5 [15.898 15.965  9.405 10.603] QK ([14.076, 13.759, 13.195, 12.466, 12.345], np.float64(10.809), np.float64(1.126), np.float64(0.386)) OV ([5.61, 5.503, 5.123, 4.867, 4.799], np.float64(10.941), np.float64(-0.614), np.float64(0.504))
L1H0 [10.561  9.834 12.951 11.168] QK ([8.928, 6.891, 6.051, 5.755, 5.309], np.float64(4.901), np.float64(-0.642), np.float64(0.521)) OV ([9.927, 9.271, 8.872, 7.793, 7.506], np.float64(7.677), np.float64(-1.257), np.float64(0.544))
L1H1 [14.533 12.801 11.065 10.657] QK ([19.138, 14.766, 13.796, 11.021, 8.543], np.float64(3.95), np.float64(-1.249), np.float64(0.541)) OV ([6.211, 5.658, 5.403, 5.113, 4.867], np.float64(10.328), np.float64(-1.193), np.float64(0.531))
L1H2 [12.687 11.35  12.119 11.189] QK ([14.486, 12.667, 10.219, 7.24, 6.462], np.float64(3.991), np.float64(-0.979), np.float64(0.535)) OV ([6.799, 6.707, 6.454, 6.192, 6.0], np.float64(12.16), np.float64(-1.347), np.float64(0.54))
L1H3 [12.697 11.75  11.318 10.141] QK ([15.986, 13.278, 10.22, 7.207, 6.367], np.float64(3.57), np.float64(-0.871), np.float64(0.531)) OV ([7.119, 6.39, 5.875, 5.68, 5.417], np.float64(8.574), np.float64(-1.078), np.float64(0.527))
L1H4 [12.592 11.509 10.916  9.909] QK ([14.823, 11.74, 10.94, 7.505, 6.922], np.float64(3.875), np.float64(-0.775), np.float64(0.526)) OV ([6.354, 5.996, 5.642, 5.541, 5.215], np.float64(9.489), np.float64(-1.121), np.float64(0.523))
L1H5 [14.14  12.576 11.233 10.925] QK ([20.182, 16.183, 12.284, 11.201, 8.546], np.float64(3.463), np.float64(-1.184), np.float64(0.536)) OV ([5.781, 5.643, 5.336, 4.976, 4.846], np.float64(12.395), np.float64(-1.361), np.float64(0.536))
L2H0 [15.223 14.526 13.812 13.642] QK ([7.484, 7.099, 6.816, 6.471, 5.96], np.float64(16.09), np.float64(-0.241), np.float64(0.497)) OV ([5.556, 5.132, 5.005, 4.795, 4.773], np.float64(20.518), np.float64(-1.919), np.float64(0.54))
L2H1 [14.685 14.475 14.683 14.212] QK ([7.258, 6.545, 5.975, 5.73, 5.396], np.float64(15.831), np.float64(1.308), np.float64(0.52)) OV ([8.652, 6.394, 6.333, 6.217, 6.016], np.float64(11.146), np.float64(-1.882), np.float64(0.536))
L2H2 [17.263 16.724 12.989 13.004] QK ([10.83, 10.274, 9.752, 9.397, 9.181], np.float64(14.873), np.float64(0.566), np.float64(0.517)) OV ([4.742, 4.585, 4.492, 4.275, 4.22], np.float64(22.749), np.float64(-1.766), np.float64(0.535))
L2H3 [15.012 14.613 14.455 14.253] QK ([8.092, 7.027, 6.199, 6.131, 5.692], np.float64(13.006), np.float64(-0.166), np.float64(0.493)) OV ([6.179, 5.765, 5.693, 5.322, 5.194], np.float64(19.999), np.float64(-1.872), np.float64(0.545))
L2H4 [15.576 15.198 13.472 13.505] QK ([11.608, 9.684, 7.179, 6.746, 6.178], np.float64(8.031), np.float64(0.093), np.float64(0.499)) OV ([5.445, 5.184, 5.128, 4.988, 4.903], np.float64(20.721), np.float64(-2.103), np.float64(0.548))
L2H5 [13.843 13.304 15.55  15.076] QK ([6.303, 5.923, 5.478, 4.701, 4.593], np.float64(14.895), np.float64(-0.422), np.float64(0.497)) OV ([8.467, 7.339, 6.951, 6.407, 6.182], np.float64(14.178), np.float64(-1.433), np.float64(0.527))
L3H0 [14.949 14.542 15.592 15.368] QK ([5.595, 5.383, 5.21, 5.13, 5.01], np.float64(25.045), np.float64(0.852), np.float64(0.509)) OV ([7.123, 6.403, 6.09, 5.949, 5.73], np.float64(19.702), np.float64(-1.973), np.float64(0.534))
L3H1 [13.433 13.311 15.607 15.271] QK ([4.398, 4.278, 4.169, 4.085, 4.04], np.float64(27.15), np.float64(1.331), np.float64(0.512)) OV ([9.373, 6.819, 6.701, 6.663, 6.486], np.float64(12.509), np.float64(-1.93), np.float64(0.541))
L3H2 [14.992 14.465 15.917 16.033] QK ([6.246, 5.499, 5.294, 4.868, 4.798], np.float64(19.8), np.float64(0.353), np.float64(0.496)) OV ([11.577, 7.787, 6.815, 6.5, 6.372], np.float64(8.952), np.float64(-1.879), np.float64(0.537))
L3H3 [10.537 10.518 17.864 18.199] QK ([3.686, 3.263, 2.903, 2.724, 2.622], np.float64(14.988), np.float64(0.002), np.float64(0.495)) OV ([18.625, 12.287, 11.952, 11.362, 11.111], np.float64(6.83), np.float64(-1.346), np.float64(0.533))
L3H4 [13.655 13.261 16.266 16.256] QK ([5.722, 5.04, 4.858, 4.363, 4.209], np.float64(16.786), np.float64(0.012), np.float64(0.502)) OV ([11.774, 10.138, 8.487, 7.578, 7.163], np.float64(9.686), np.float64(-1.232), np.float64(0.52))
L3H5 [15.026 14.808 13.874 14.159] QK ([5.408, 5.262, 5.14, 5.017, 4.936], np.float64(28.066), np.float64(3.817), np.float64(0.61)) OV ([13.046, 7.099, 6.721, 6.521, 6.348], np.float64(5.51), np.float64(-2.408), np.float64(0.605))


========================================================================
TURN t002
MESSAGE SENT TO CODEX
OUTPUT of run 1 of at most 100:
```
Available weights: ['W_fc.0', 'W_fc.1', 'W_fc.2', 'W_fc.3', 'W_k.0', 'W_k.1', 'W_k.2', 'W_k.3', 'W_o.0', 'W_o.1', 'W_o.2', 'W_o.3', 'W_proj.0', 'W_proj.1', 'W_proj.2', 'W_proj.3', 'W_q.0', 'W_q.1', 'W_q.2', 'W_q.3', 'W_v.0', 'W_v.1', 'W_v.2', 'W_v.3', 'ln1_b.0', 'ln1_b.1', 'ln1_b.2', 'ln1_b.3', 'ln1_w.0', 'ln1_w.1', 'ln1_w.2', 'ln1_w.3', 'ln2_b.0', 'ln2_b.1', 'ln2_b.2', 'ln2_b.3', 'ln2_w.0', 'ln2_w.1', 'ln2_w.2', 'ln2_w.3']

LAYER NORMS: name, norm, mean, std, min, max; largest deviations from initialization
ln1_w.0 [6.72409 0.26771 0.21465 0.01655 1.01343] [(365, 0.01655), (187, 0.0226), (55, 0.02532), (24, 0.03048), (357, 0.03226), (347, 0.03366), (3, 0.03582), (248, 0.04101)]
ln1_b.0 [ 7.1723e-01  6.5000e-04  3.6600e-02 -1.2149e-01  1.4178e-01] [(153, 0.14178), (104, -0.12149), (291, -0.1168), (71, -0.1163), (329, -0.11242), (175, 0.11176), (161, -0.11093), (338, -0.10877)]
ln2_w.0 [17.27234  0.81684  0.33119  0.04681  1.64118] [(95, 0.04681), (311, 0.13376), (333, 0.17013), (377, 0.17466), (109, 0.17753), (21, 0.17766), (342, 0.20886), (303, 0.2185)]
ln2_b.0 [ 1.35297  0.01796  0.06667 -0.55371  0.16628] [(123, -0.55371), (21, -0.48686), (51, -0.45307), (307, -0.16922), (207, 0.16628), (349, 0.15666), (35, 0.15028), (264, 0.1405)]
ln1_w.1 [9.50464 0.47536 0.09639 0.19793 1.20958] [(146, 0.19793), (125, 0.25938), (51, 0.26836), (142, 0.28711), (307, 0.29104), (322, 0.29839), (109, 0.30712), (97, 0.30736)]
ln1_b.1 [ 3.03267  0.02203  0.15318 -1.0505   0.91025] [(123, -1.0505), (21, -1.02415), (307, -0.93456), (51, -0.91482), (125, 0.91025), (35, 0.90413), (360, 0.7445), (87, -0.72746)]
ln2_w.1 [2.005427e+01 1.011090e+00 1.582000e-01 1.391000e-02 1.470660e+00] [(21, 0.01391), (273, 0.02643), (109, 0.30976), (123, 0.33619), (125, 0.49986), (35, 0.50134), (258, 0.51009), (186, 1.47066)]
ln2_b.1 [ 2.87693  0.02753  0.14421 -0.37441  0.36524] [(123, -0.37441), (186, 0.36524), (321, 0.35475), (125, 0.34593), (383, 0.34122), (20, 0.31931), (27, -0.315), (35, 0.31279)]
ln1_w.2 [14.4574   0.73433  0.0712   0.34448  0.90474] [(125, 0.34448), (51, 0.34639), (307, 0.39088), (123, 0.39415), (360, 0.41219), (146, 0.4692), (35, 0.50849), (99, 0.51777)]
ln1_b.2 [ 2.62139  0.02439  0.13153 -0.94167  0.73256] [(21, -0.94167), (123, -0.84128), (51, -0.80833), (125, 0.73256), (307, -0.61286), (35, 0.57679), (360, 0.51205), (273, -0.50597)]
ln2_w.2 [25.08196  1.275    0.11256  0.07753  1.50181] [(273, 0.07753), (7, 1.50181), (21, 0.50071), (214, 1.48408), (198, 1.44415), (83, 1.44083), (200, 1.43113), (52, 1.42931)]
ln2_b.2 [ 2.91097  0.01881  0.14735 -0.41571  0.36355] [(7, -0.41571), (21, -0.39103), (103, 0.36355), (106, 0.34117), (130, 0.33613), (372, -0.32533), (321, 0.32338), (139, -0.32204)]
ln1_w.3 [15.10458  0.76938  0.04688  0.45102  0.87783] [(123, 0.45102), (307, 0.45503), (51, 0.55437), (17, 0.60271), (249, 0.61993), (86, 0.62136), (146, 0.6224), (21, 0.629)]
ln1_b.3 [ 1.86193  0.01021  0.09447 -0.51407  0.62854] [(146, 0.62854), (51, -0.51407), (123, -0.4276), (307, -0.3847), (125, 0.3566), (86, 0.34005), (21, -0.3213), (292, 0.31999)]
ln2_w.3 [24.65744  1.25516  0.08883  0.2619   1.45679] [(273, 0.2619), (197, 1.45679), (382, 1.41412), (161, 1.39887), (28, 1.38727), (348, 1.38355), (75, 1.38349), (143, 1.38275)]
ln2_b.3 [ 2.49718  0.01399  0.12666 -0.51123  0.38081] [(123, -0.51123), (275, 0.38081), (382, -0.31688), (161, 0.30605), (210, -0.3025), (162, 0.28633), (146, 0.27946), (39, 0.2748)]

MATRIX SPECTRA: name, Frobenius norm, top 6 singular values, stable rank
W_q.0 22.7038 [5.0385 4.9994 4.7025 4.377  4.3428 4.2944] 20.305
W_k.0 23.2143 [6.2042 5.0521 4.5716 4.5372 4.3914 4.2894] 14.001
W_v.0 18.1973 [4.5533 4.0243 3.7382 3.4973 3.4646 3.3672] 15.972
W_o.0 19.5995 [6.678  5.2365 2.8346 2.679  2.6198 2.5808] 8.614
W_fc.0 51.6496 [18.1104 12.2798  8.4726  7.9016  7.7793  7.442 ] 8.134
W_proj.0 45.8056 [10.9319  7.576   6.6285  6.2932  6.166   5.9239] 17.557
W_q.1 31.6763 [8.9845 7.1373 6.0786 5.874  5.1897 5.1024] 12.43
W_k.1 28.6017 [10.9672  9.4479  5.438   4.842   4.5394  4.4806] 6.801
W_v.1 28.4686 [5.3505 5.0645 4.9787 4.8191 4.6901 4.5848] 28.31
W_o.1 26.151 [4.1901 3.6511 3.3848 3.3517 3.2401 3.2159] 38.953
W_fc.1 80.2609 [20.7957 14.1429 13.8258 13.4276 13.1222 12.8832] 14.896
W_proj.1 75.1193 [7.6249 7.3593 6.9075 6.4887 6.3442 6.2228] 97.059
W_q.2 37.4829 [5.0672 4.7425 4.4563 4.3901 4.3139 4.2604] 54.718
W_k.2 36.3557 [8.2935 7.3483 5.0588 4.4667 4.3056 3.9124] 19.216
W_v.2 34.7465 [3.89   3.8245 3.7912 3.6543 3.6442 3.6283] 79.785
W_o.2 34.2053 [4.4416 4.0726 3.768  3.4554 3.4401 3.3649] 59.308
W_fc.2 79.4541 [14.8285  8.0483  7.9211  7.76    7.6596  7.5865] 28.71
W_proj.2 77.4765 [10.0946  8.9847  8.0747  7.9088  7.7913  7.6557] 58.906
W_q.3 33.9401 [4.1544 3.9457 3.875  3.6008 3.5209 3.4277] 66.743
W_k.3 33.2214 [4.8875 3.9279 3.6197 3.4371 3.3813 3.2868] 46.201
W_v.3 38.9389 [4.6968 4.3562 4.2546 4.2082 4.1706 4.1314] 68.734
W_o.3 39.0173 [9.1753 6.6297 4.4421 4.1112 4.0543 4.0399] 18.083
W_fc.3 78.1887 [14.3731  8.1763  7.9733  7.8303  7.0359  6.7574] 29.593
W_proj.3 85.0885 [28.1752 20.2934 19.2921 19.1349 18.7081 18.5697] 9.12

HEAD WEIGHT GEOMETRY
QK = W_q[head].T @ W_k[head]; OV = W_o[:,head] @ W_v[head].
Each line: ||Q||,||K||,||V||,||O||; QK top 5 SV, stable rank, trace/norm, symmetric-energy fraction; OV equivalents.
L0H0 [4.128 4.913 5.352 5.205] QK ([5.195, 3.389, 0.846, 0.657, 0.509], np.float64(1.545), np.float64(-0.655), np.float64(0.577)) OV ([6.898, 3.897, 1.936, 1.638, 1.469], np.float64(1.579), np.float64(0.187), np.float64(0.524))
L0H1 [12.463 12.643  9.257 10.205] QK ([17.223, 12.122, 11.496, 9.727, 8.594], np.float64(3.568), np.float64(-1.267), np.float64(0.582)) OV ([4.703, 4.573, 4.424, 4.248, 4.199], np.float64(13.166), np.float64(-0.048), np.float64(0.502))
L0H2 [6.458 6.828 5.99  5.536] QK ([4.486, 3.573, 2.993, 2.852, 2.588], np.float64(4.101), np.float64(-0.849), np.float64(0.588)) OV ([4.786, 3.621, 3.514, 2.916, 2.224], np.float64(3.222), np.float64(-0.086), np.float64(0.501))
L0H3 [6.627 6.822 4.882 5.466] QK ([7.675, 5.606, 3.45, 2.355, 2.231], np.float64(2.233), np.float64(-0.798), np.float64(0.543)) OV ([2.168, 1.997, 1.875, 1.555, 1.484], np.float64(5.949), np.float64(-0.013), np.float64(0.503))
L0H4 [2.173 2.623 8.285 8.942] QK ([0.319, 0.242, 0.19, 0.144, 0.137], np.float64(5.859), np.float64(-0.613), np.float64(0.52)) OV ([18.815, 15.373, 5.587, 3.989, 2.502], np.float64(1.88), np.float64(0.259), np.float64(0.52))
L0H5 [15.898 15.965  9.405 10.603] QK ([14.076, 13.759, 13.195, 12.466, 12.345], np.float64(10.809), np.float64(1.126), np.float64(0.386)) OV ([5.61, 5.503, 5.123, 4.867, 4.799], np.float64(10.941), np.float64(-0.614), np.float64(0.504))
L1H0 [10.561  9.834 12.951 11.168] QK ([8.928, 6.891, 6.051, 5.755, 5.309], np.float64(4.901), np.float64(-0.642), np.float64(0.521)) OV ([9.927, 9.271, 8.872, 7.793, 7.506], np.float64(7.677), np.float64(-1.257), np.float64(0.544))
L1H1 [14.533 12.801 11.065 10.657] QK ([19.138, 14.766, 13.796, 11.021, 8.543], np.float64(3.95), np.float64(-1.249), np.float64(0.541)) OV ([6.211, 5.658, 5.403, 5.113, 4.867], np.float64(10.328), np.float64(-1.193), np.float64(0.531))
L1H2 [12.687 11.35  12.119 11.189] QK ([14.486, 12.667, 10.219, 7.24, 6.462], np.float64(3.991), np.float64(-0.979), np.float64(0.535)) OV ([6.799, 6.707, 6.454, 6.192, 6.0], np.float64(12.16), np.float64(-1.347), np.float64(0.54))
L1H3 [12.697 11.75  11.318 10.141] QK ([15.986, 13.278, 10.22, 7.207, 6.367], np.float64(3.57), np.float64(-0.871), np.float64(0.531)) OV ([7.119, 6.39, 5.875, 5.68, 5.417], np.float64(8.574), np.float64(-1.078), np.float64(0.527))
L1H4 [12.592 11.509 10.916  9.909] QK ([14.823, 11.74, 10.94, 7.505, 6.922], np.float64(3.875), np.float64(-0.775), np.float64(0.526)) OV ([6.354, 5.996, 5.642, 5.541, 5.215], np.float64(9.489), np.float64(-1.121), np.float64(0.523))
L1H5 [14.14  12.576 11.233 10.925] QK ([20.182, 16.183, 12.284, 11.201, 8.546], np.float64(3.463), np.float64(-1.184), np.float64(0.536)) OV ([5.781, 5.643, 5.336, 4.976, 4.846], np.float64(12.395), np.float64(-1.361), np.float64(0.536))
L2H0 [15.223 14.526 13.812 13.642] QK ([7.484, 7.099, 6.816, 6.471, 5.96], np.float64(16.09), np.float64(-0.241), np.float64(0.497)) OV ([5.556, 5.132, 5.005, 4.795, 4.773], np.float64(20.518), np.float64(-1.919), np.float64(0.54))
L2H1 [14.685 14.475 14.683 14.212] QK ([7.258, 6.545, 5.975, 5.73, 5.396], np.float64(15.831), np.float64(1.308), np.float64(0.52)) OV ([8.652, 6.394, 6.333, 6.217, 6.016], np.float64(11.146), np.float64(-1.882), np.float64(0.536))
L2H2 [17.263 16.724 12.989 13.004] QK ([10.83, 10.274, 9.752, 9.397, 9.181], np.float64(14.873), np.float64(0.566), np.float64(0.517)) OV ([4.742, 4.585, 4.492, 4.275, 4.22], np.float64(22.749), np.float64(-1.766), np.float64(0.535))
L2H3 [15.012 14.613 14.455 14.253] QK ([8.092, 7.027, 6.199, 6.131, 5.692], np.float64(13.006), np.float64(-0.166), np.float64(0.493)) OV ([6.179, 5.765, 5.693, 5.322, 5.194], np.float64(19.999), np.float64(-1.872), np.float64(0.545))
L2H4 [15.576 15.198 13.472 13.505] QK ([11.608, 9.684, 7.179, 6.746, 6.178], np.float64(8.031), np.float64(0.093), np.float64(0.499)) OV ([5.445, 5.184, 5.128, 4.988, 4.903], np.float64(20.721), np.float64(-2.103), np.float64(0.548))
L2H5 [13.843 13.304 15.55  15.076] QK ([6.303, 5.923, 5.478, 4.701, 4.593], np.float64(14.895), np.float64(-0.422), np.float64(0.497)) OV ([8.467, 7.339, 6.951, 6.407, 6.182], np.float64(14.178), np.float64(-1.433), np.float64(0.527))
L3H0 [14.949 14.542 15.592 15.368] QK ([5.595, 5.383, 5.21, 5.13, 5.01], np.float64(25.045), np.float64(0.852), np.float64(0.509)) OV ([7.123, 6.403, 6.09, 5.949, 5.73], np.float64(19.702), np.float64(-1.973), np.float64(0.534))
L3H1 [13.433 13.311 15.607 15.271] QK ([4.398, 4.278, 4.169, 4.085, 4.04], np.float64(27.15), np.float64(1.331), np.float64(0.512)) OV ([9.373, 6.819, 6.701, 6.663, 6.486], np.float64(12.509), np.float64(-1.93), np.float64(0.541))
L3H2 [14.992 14.465 15.917 16.033] QK ([6.246, 5.499, 5.294, 4.868, 4.798], np.float64(19.8), np.float64(0.353), np.float64(0.496)) OV ([11.577, 7.787, 6.815, 6.5, 6.372], np.float64(8.952), np.float64(-1.879), np.float64(0.537))
L3H3 [10.537 10.518 17.864 18.199] QK ([3.686, 3.263, 2.903, 2.724, 2.622], np.float64(14.988), np.float64(0.002), np.float64(0.495)) OV ([18.625, 12.287, 11.952, 11.362, 11.111], np.float64(6.83), np.float64(-1.346), np.float64(0.533))
L3H4 [13.655 13.261 16.266 16.256] QK ([5.722, 5.04, 4.858, 4.363, 4.209], np.float64(16.786), np.float64(0.012), np.float64(0.502)) OV ([11.774, 10.138, 8.487, 7.578, 7.163], np.float64(9.686), np.float64(-1.232), np.float64(0.52))
L3H5 [15.026 14.808 13.874 14.159] QK ([5.408, 5.262, 5.14, 5.017, 4.936], np.float64(28.066), np.float64(3.817), np.float64(0.61)) OV ([13.046, 7.099, 6.721, 6.521, 6.348], np.float64(5.51), np.float64(-2.408), np.float64(0.605))

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd, svdvals, eigvals
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

D = 384
P = np.eye(D) - np.ones((D, D))/D

def eff(name, l, norm="ln1"):
    # Linear weight geometry only: absorb learned coordinate scaling and
    # project away the all-ones direction. No normalization or network evaluation.
    return (np.asarray(W[f"{name}.{l}"], dtype=float)
            * np.asarray(W[f"{norm}_w.{l}"], dtype=float)[None, :]) @ P

QK, OV = {}, {}
for l in range(4):
    q, k, v = [eff("W_"+n, l) for n in ["q","k","v"]]
    for j in range(6):
        s = slice(64*j,64*(j+1))
        QK[l,j] = q[s].T @ k[s]
        OV[l,j] = np.asarray(W[f"W_o.{l}"][:,s],dtype=float) @ v[s]

print("L0 ln1_w: largest 45 coordinates")
a = W["ln1_w.0"]
ix = np.argsort(a)[-45:][::-1]
print([(int(i), round(float(a[i]),4)) for i in ix])

print("\nEffective QK spectra and largest-magnitude eigenvalues")
for l in range(4):
    for j in range(6):
        a = QK[l,j]
        s = svdvals(a)
        ev = eigvals(a)
        ev = ev[np.argsort(np.abs(ev))[::-1]][:8]
        print(f"L{l}H{j}", "norm", round(np.linalg.norm(a),3),
              "SV", np.round(s[:8],3), "eig", np.round(ev,3),
              "symfrac", round(np.linalg.norm((a+a.T)/2)**2/np.linalg.norm(a)**2,3))

print("\nFull singular spectra at selected quantiles")
for l in [0,3]:
    for name in ["W_fc","W_proj"]:
        a = np.asarray(W[f"{name}.{l}"],dtype=float)
        s = svdvals(a)
        idx = [0,1,2,3,4,5,9,14,19,24,25,26,27,28,29,31,39,49,63,79,99,127,191,255,319,383]
        print(name,l,[(i+1,round(s[i],3)) for i in idx])

print("\nL0 QK pairwise Frobenius cosine")
print(np.round([[np.sum(QK[0,j]*QK[0,k])/
                 (np.linalg.norm(QK[0,j])*np.linalg.norm(QK[0,k]))
                 for k in range(6)] for j in range(6)],3))

print("\nWeight-only key-composition enrichment")
print("For later head B and earlier head A: ||QK_B OV_A||_F / (||QK_B||_F ||OV_A||_F/sqrt(384)).")
for l in range(1,4):
    for j in range(6):
        vals = []
        a = QK[l,j]
        for m in range(l):
            for k in range(6):
                b = OV[m,k]
                c = np.linalg.norm(a @ b)*np.sqrt(D)/(np.linalg.norm(a)*np.linalg.norm(b))
                vals.append((c, f"L{m}H{k}"))
        vals.sort(reverse=True)
        print(f"L{l}H{j}", [(h,round(c,3)) for c,h in vals[:6]])
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 2,
  "turn": "t002",
  "code": "import numpy as np\nfrom scipy.linalg import svd, svdvals, eigvals\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nD = 384\nP = np.eye(D) - np.ones((D, D))/D\n\ndef eff(name, l, norm=\"ln1\"):\n    # Linear weight geometry only: absorb learned coordinate scaling and\n    # project away the all-ones direction. No normalization or network evaluation.\n    return (np.asarray(W[f\"{name}.{l}\"], dtype=float)\n            * np.asarray(W[f\"{norm}_w.{l}\"], dtype=float)[None, :]) @ P\n\nQK, OV = {}, {}\nfor l in range(4):\n    q, k, v = [eff(\"W_\"+n, l) for n in [\"q\",\"k\",\"v\"]]\n    for j in range(6):\n        s = slice(64*j,64*(j+1))\n        QK[l,j] = q[s].T @ k[s]\n        OV[l,j] = np.asarray(W[f\"W_o.{l}\"][:,s],dtype=float) @ v[s]\n\nprint(\"L0 ln1_w: largest 45 coordinates\")\na = W[\"ln1_w.0\"]\nix = np.argsort(a)[-45:][::-1]\nprint([(int(i), round(float(a[i]),4)) for i in ix])\n\nprint(\"\\nEffective QK spectra and largest-magnitude eigenvalues\")\nfor l in range(4):\n    for j in range(6):\n        a = QK[l,j]\n        s = svdvals(a)\n        ev = eigvals(a)\n        ev = ev[np.argsort(np.abs(ev))[::-1]][:8]\n        print(f\"L{l}H{j}\", \"norm\", round(np.linalg.norm(a),3),\n              \"SV\", np.round(s[:8],3), \"eig\", np.round(ev,3),\n              \"symfrac\", round(np.linalg.norm((a+a.T)/2)**2/np.linalg.norm(a)**2,3))\n\nprint(\"\\nFull singular spectra at selected quantiles\")\nfor l in [0,3]:\n    for name in [\"W_fc\",\"W_proj\"]:\n        a = np.asarray(W[f\"{name}.{l}\"],dtype=float)\n        s = svdvals(a)\n        idx = [0,1,2,3,4,5,9,14,19,24,25,26,27,28,29,31,39,49,63,79,99,127,191,255,319,383]\n        print(name,l,[(i+1,round(s[i],3)) for i in idx])\n\nprint(\"\\nL0 QK pairwise Frobenius cosine\")\nprint(np.round([[np.sum(QK[0,j]*QK[0,k])/\n                 (np.linalg.norm(QK[0,j])*np.linalg.norm(QK[0,k]))\n                 for k in range(6)] for j in range(6)],3))\n\nprint(\"\\nWeight-only key-composition enrichment\")\nprint(\"For later head B and earlier head A: ||QK_B OV_A||_F / (||QK_B||_F ||OV_A||_F/sqrt(384)).\")\nfor l in range(1,4):\n    for j in range(6):\n        vals = []\n        a = QK[l,j]\n        for m in range(l):\n            for k in range(6):\n                b = OV[m,k]\n                c = np.linalg.norm(a @ b)*np.sqrt(D)/(np.linalg.norm(a)*np.linalg.norm(b))\n                vals.append((c, f\"L{m}H{k}\"))\n        vals.sort(reverse=True)\n        print(f\"L{l}H{j}\", [(h,round(c,3)) for c,h in vals[:6]])",
  "out": "L0 ln1_w: largest 45 coordinates\n[(211, 1.0134), (273, 0.9813), (223, 0.9731), (180, 0.9483), (50, 0.9335), (162, 0.9129), (60, 0.9031), (228, 0.9005), (48, 0.8725), (307, 0.8441), (359, 0.8331), (184, 0.8285), (240, 0.8178), (125, 0.8091), (132, 0.8068), (169, 0.8059), (314, 0.7991), (317, 0.7904), (300, 0.7823), (152, 0.7762), (158, 0.773), (246, 0.7606), (256, 0.7391), (59, 0.7363), (351, 0.7351), (356, 0.7144), (191, 0.6965), (224, 0.6901), (102, 0.6864), (15, 0.6845), (178, 0.6671), (151, 0.654), (320, 0.6535), (12, 0.6526), (136, 0.6417), (56, 0.6349), (239, 0.6336), (207, 0.6255), (35, 0.6168), (21, 0.6076), (28, 0.5958), (373, 0.5931), (360, 0.5884), (47, 0.5863), (204, 0.5858)]\n\nEffective QK spectra and largest-magnitude eigenvalues\nL0H0 norm 0.91 SV [0.709 0.541 0.106 0.064 0.045 0.04  0.033 0.033] eig [-0.392+0.j     0.048+0.069j  0.048-0.069j -0.023+0.j    -0.009+0.013j\n -0.009-0.013j -0.013+0.002j -0.013-0.002j] symfrac 0.591\nL0H1 norm 7.02 SV [4.811 2.985 2.375 1.692 1.591 1.239 1.142 0.718] eig [ 1.742+2.761j  1.742-2.761j -2.275+0.j    -0.985+1.196j -0.985-1.196j\n -0.778+0.784j -0.778-0.784j -0.254-0.33j ] symfrac 0.45\nL0H2 norm 1.396 SV [1.056 0.638 0.35  0.247 0.224 0.202 0.169 0.147] eig [ 0.725+0.j     0.426+0.j    -0.176+0.j    -0.106+0.006j -0.106-0.006j\n  0.091+0.j    -0.08 +0.j    -0.045+0.033j] symfrac 0.701\nL0H3 norm 1.916 SV [1.316 1.12  0.649 0.249 0.234 0.169 0.147 0.132] eig [-0.62 +0.044j -0.62 -0.044j -0.159+0.j     0.048+0.133j  0.048-0.133j\n  0.06 +0.j    -0.049+0.013j -0.049-0.013j] symfrac 0.605\nL0H4 norm 0.074 SV [0.036 0.022 0.02  0.016 0.016 0.015 0.013 0.013] eig [-0.01 +0.005j -0.01 -0.005j -0.004+0.004j -0.004-0.004j  0.001+0.005j\n  0.001-0.005j -0.005+0.j     0.005+0.j   ] symfrac 0.512\nL0H5 norm 7.764 SV [3.279 2.892 2.188 2.032 1.958 1.78  1.726 1.636] eig [1.656+2.197j 1.656-2.197j 0.911+1.375j 0.911-1.375j 1.427+0.j\n 1.237+0.675j 1.237-0.675j 0.278+1.326j] symfrac 0.386\nL1H0 norm 5.152 SV [2.83  2.014 1.745 1.34  1.261 1.111 0.964 0.946] eig [ 0.226+0.41j   0.226-0.41j  -0.41 +0.186j -0.41 -0.186j -0.406+0.j\n -0.328+0.106j -0.328-0.106j -0.334+0.j   ] symfrac 0.51\nL1H1 norm 12.675 SV [9.164 5.291 3.674 2.61  1.989 1.719 1.705 1.488] eig [ 0.642+2.083j  0.642-2.083j -1.152+0.068j -1.152-0.068j -0.74 +0.631j\n -0.74 -0.631j -0.816+0.j    -0.794+0.153j] symfrac 0.498\nL1H2 norm 9.886 SV [6.455 5.544 2.657 1.642 1.444 1.27  1.219 1.143] eig [ 0.457+1.616j  0.457-1.616j -0.864+0.j    -0.559+0.397j -0.559-0.397j\n -0.628+0.036j -0.628-0.036j -0.511+0.j   ] symfrac 0.492\nL1H3 norm 10.475 SV [7.302 5.595 2.473 1.619 1.423 1.339 1.2   1.157] eig [ 0.609+2.177j  0.609-2.177j -0.755+0.j     0.735+0.j    -0.64 +0.193j\n -0.64 -0.193j -0.615+0.j    -0.576+0.j   ] symfrac 0.478\nL1H4 norm 9.993 SV [6.784 5.233 2.719 1.705 1.53  1.294 1.237 1.209] eig [ 0.567+1.91j   0.567-1.91j  -0.677+0.113j -0.677-0.113j -0.621+0.j\n -0.516+0.225j -0.516-0.225j -0.556+0.j   ] symfrac 0.481\nL1H5 norm 11.691 SV [8.071 4.438 3.852 3.6   1.939 1.717 1.585 1.367] eig [ 0.577+1.634j  0.577-1.634j -1.251+0.j    -0.988+0.j    -0.662+0.422j\n -0.662-0.422j -0.641-0.242j -0.641+0.242j] symfrac 0.505\nL2H0 norm 16.364 SV [4.147 3.88  3.67  3.328 3.255 3.174 3.11  3.023] eig [ 0.263+1.058j  0.263-1.058j -0.936+0.j     0.724+0.593j  0.724-0.593j\n  0.295+0.832j  0.295-0.832j -0.823-0.248j] symfrac 0.499\nL2H1 norm 15.563 SV [3.666 3.366 3.17  3.148 2.939 2.838 2.819 2.757] eig [1.137-0.284j 1.137+0.284j 1.136+0.j    1.128-0.088j 1.128+0.088j\n 1.051-0.175j 1.051+0.175j 0.821+0.634j] symfrac 0.52\nL2H2 norm 22.432 SV [5.671 5.545 5.406 5.075 4.889 4.782 4.468 4.387] eig [ 2.069+0.j     0.637+1.729j  0.637-1.729j  1.544+0.466j  1.544-0.466j\n  1.558+0.206j  1.558-0.206j -1.377+0.178j] symfrac 0.516\nL2H3 norm 15.807 SV [4.566 3.385 3.295 3.137 3.103 2.964 2.914 2.856] eig [ 0.46 +1.476j  0.46 -1.476j  0.012+0.849j  0.012-0.849j -0.437+0.683j\n -0.437-0.683j  0.774+0.209j  0.774-0.209j] symfrac 0.494\nL2H4 norm 17.612 SV [6.262 4.697 3.875 3.435 3.333 3.211 3.179 3.08 ] eig [ 0.643+1.761j  0.643-1.761j  1.229+0.j    -0.967+0.j     0.685+0.612j\n  0.685-0.612j -0.886+0.227j -0.886-0.227j] symfrac 0.5\nL2H5 norm 13.233 SV [3.355 3.314 2.873 2.56  2.517 2.371 2.35  2.307] eig [-0.854+0.j    -0.053+0.811j -0.053-0.811j -0.636+0.362j -0.636-0.362j\n  0.723+0.j    -0.536+0.441j -0.536-0.441j] symfrac 0.497\nL3H0 norm 16.621 SV [3.243 3.192 3.08  3.013 2.922 2.872 2.846 2.781] eig [1.044+0.j    0.948-0.317j 0.948+0.317j 0.954-0.142j 0.954+0.142j\n 0.87 +0.j    0.13 +0.838j 0.13 -0.838j] symfrac 0.51\nL3H1 norm 13.558 SV [2.578 2.516 2.469 2.429 2.398 2.363 2.321 2.249] eig [0.877+0.22j  0.877-0.22j  0.897+0.j    0.602+0.57j  0.602-0.57j\n 0.819+0.j    0.696+0.322j 0.696-0.322j] symfrac 0.511\nL3H2 norm 16.487 SV [3.47  3.288 3.133 2.897 2.867 2.821 2.736 2.7  ] eig [0.286+0.912j 0.286-0.912j 0.909-0.207j 0.909+0.207j 0.022+0.878j\n 0.022-0.878j 0.848+0.j    0.563-0.616j] symfrac 0.497\nL3H3 norm 8.424 SV [2.099 1.898 1.662 1.617 1.547 1.527 1.436 1.391] eig [ 0.216+0.491j  0.216-0.491j  0.476+0.j     0.208+0.388j  0.208-0.388j\n -0.018+0.406j -0.018-0.406j -0.335+0.205j] symfrac 0.496\nL3H4 norm 13.875 SV [3.205 2.956 2.854 2.588 2.501 2.451 2.351 2.32 ] eig [-0.843+0.j     0.746+0.j     0.524+0.473j  0.524-0.473j  0.232+0.633j\n  0.232-0.633j -0.018+0.666j -0.018-0.666j] symfrac 0.502\nL3H5 norm 16.935 SV [3.233 3.119 3.073 2.957 2.918 2.897 2.879 2.78 ] eig [1.556+0.937j 1.556-0.937j 1.702+0.591j 1.702-0.591j 1.599+0.83j\n 1.599-0.83j  1.615+0.609j 1.615-0.609j] symfrac 0.611\n\nFull singular spectra at selected quantiles\nW_fc 0 [(1, np.float64(18.11)), (2, np.float64(12.28)), (3, np.float64(8.473)), (4, np.float64(7.902)), (5, np.float64(7.779)), (6, np.float64(7.442)), (10, np.float64(6.805)), (15, np.float64(6.27)), (20, np.float64(5.944)), (25, np.float64(5.609)), (26, np.float64(5.58)), (27, np.float64(5.498)), (28, np.float64(5.447)), (29, np.float64(5.377)), (30, np.float64(5.334)), (32, np.float64(5.203)), (40, np.float64(4.716)), (50, np.float64(4.081)), (64, np.float64(3.281)), (80, np.float64(2.205)), (100, np.float64(1.622)), (128, np.float64(1.264)), (192, np.float64(0.94)), (256, np.float64(0.75)), (320, np.float64(0.596)), (384, np.float64(0.421))]\nW_proj 0 [(1, np.float64(10.932)), (2, np.float64(7.576)), (3, np.float64(6.628)), (4, np.float64(6.293)), (5, np.float64(6.166)), (6, np.float64(5.924)), (10, np.float64(5.61)), (15, np.float64(5.193)), (20, np.float64(4.937)), (25, np.float64(4.651)), (26, np.float64(4.637)), (27, np.float64(4.548)), (28, np.float64(4.526)), (29, np.float64(4.487)), (30, np.float64(4.404)), (32, np.float64(4.283)), (40, np.float64(4.023)), (50, np.float64(3.694)), (64, np.float64(3.268)), (80, np.float64(2.868)), (100, np.float64(2.413)), (128, np.float64(1.936)), (192, np.float64(1.172)), (256, np.float64(0.708)), (320, np.float64(0.432)), (384, np.float64(0.244))]\nW_fc 3 [(1, np.float64(14.373)), (2, np.float64(8.176)), (3, np.float64(7.973)), (4, np.float64(7.83)), (5, np.float64(7.036)), (6, np.float64(6.757)), (10, np.float64(6.387)), (15, np.float64(6.162)), (20, np.float64(5.895)), (25, np.float64(5.753)), (26, np.float64(5.699)), (27, np.float64(5.681)), (28, np.float64(5.652)), (29, np.float64(5.641)), (30, np.float64(5.641)), (32, np.float64(5.564)), (40, np.float64(5.357)), (50, np.float64(5.178)), (64, np.float64(4.945)), (80, np.float64(4.733)), (100, np.float64(4.483)), (128, np.float64(4.184)), (192, np.float64(3.606)), (256, np.float64(3.093)), (320, np.float64(2.545)), (384, np.float64(0.708))]\nW_proj 3 [(1, np.float64(28.175)), (2, np.float64(20.293)), (3, np.float64(19.292)), (4, np.float64(19.135)), (5, np.float64(18.708)), (6, np.float64(18.57)), (10, np.float64(16.54)), (15, np.float64(14.417)), (20, np.float64(12.37)), (25, np.float64(10.769)), (26, np.float64(9.227)), (27, np.float64(8.573)), (28, np.float64(7.929)), (29, np.float64(6.5)), (30, np.float64(5.271)), (32, np.float64(3.184)), (40, np.float64(2.29)), (50, np.float64(1.926)), (64, np.float64(1.692)), (80, np.float64(1.508)), (100, np.float64(1.319)), (128, np.float64(1.138)), (192, np.float64(0.832)), (256, np.float64(0.632)), (320, np.float64(0.481)), (384, np.float64(0.324))]\n\nL0 QK pairwise Frobenius cosine\n[[ 1.     0.179  0.112  0.206  0.063  0.093]\n [ 0.179  1.     0.21  -0.15   0.005  0.414]\n [ 0.112  0.21   1.    -0.247 -0.006  0.114]\n [ 0.206 -0.15  -0.247  1.    -0.027 -0.098]\n [ 0.063  0.005 -0.006 -0.027  1.    -0.004]\n [ 0.093  0.414  0.114 -0.098 -0.004  1.   ]]\n\nWeight-only key-composition enrichment\nFor later head B and earlier head A: ||QK_B OV_A||_F / (||QK_B||_F ||OV_A||_F/sqrt(384)).\nL1H0 [('L0H4', np.float64(7.74)), ('L0H0', np.float64(6.237)), ('L0H3', np.float64(2.815)), ('L0H2', np.float64(1.686)), ('L0H5', np.float64(1.307)), ('L0H1', np.float64(1.109))]\nL1H1 [('L0H4', np.float64(10.672)), ('L0H0', np.float64(8.436)), ('L0H3', np.float64(3.299)), ('L0H2', np.float64(2.049)), ('L0H5', np.float64(1.007)), ('L0H1', np.float64(0.58))]\nL1H2 [('L0H4', np.float64(10.165)), ('L0H0', np.float64(8.084)), ('L0H3', np.float64(3.365)), ('L0H2', np.float64(1.976)), ('L0H5', np.float64(0.944)), ('L0H1', np.float64(0.611))]\nL1H3 [('L0H4', np.float64(10.707)), ('L0H0', np.float64(8.609)), ('L0H3', np.float64(3.634)), ('L0H2', np.float64(2.128)), ('L0H5', np.float64(0.957)), ('L0H1', np.float64(0.567))]\nL1H4 [('L0H4', np.float64(10.328)), ('L0H0', np.float64(8.356)), ('L0H3', np.float64(3.51)), ('L0H2', np.float64(2.123)), ('L0H5', np.float64(1.02)), ('L0H1', np.float64(0.592))]\nL1H5 [('L0H4', np.float64(9.908)), ('L0H0', np.float64(7.846)), ('L0H3', np.float64(2.812)), ('L0H2', np.float64(1.79)), ('L0H5', np.float64(1.195)), ('L0H1', np.float64(0.664))]\nL2H0 [('L0H4', np.float64(3.137)), ('L0H0', np.float64(2.5)), ('L0H3', np.float64(1.296)), ('L1H0', np.float64(1.277)), ('L0H5', np.float64(1.233)), ('L0H2', np.float64(1.227))]\nL2H1 [('L0H5', np.float64(1.492)), ('L1H3', np.float64(1.389)), ('L1H2', np.float64(1.355)), ('L1H4', np.float64(1.349)), ('L1H0', np.float64(1.3)), ('L1H1', np.float64(1.259))]\nL2H2 [('L0H4', np.float64(3.585)), ('L0H0', np.float64(2.872)), ('L0H3', np.float64(1.499)), ('L0H5', np.float64(1.384)), ('L1H0', np.float64(1.2)), ('L0H2', np.float64(1.176))]\nL2H3 [('L0H4', np.float64(4.201)), ('L0H0', np.float64(3.344)), ('L0H3', np.float64(1.71)), ('L0H2', np.float64(1.236)), ('L0H5', np.float64(1.171)), ('L1H0', np.float64(1.158))]\nL2H4 [('L0H4', np.float64(4.245)), ('L0H0', np.float64(3.401)), ('L0H2', np.float64(1.808)), ('L0H3', np.float64(1.718)), ('L1H0', np.float64(1.584)), ('L0H5', np.float64(1.195))]\nL2H5 [('L0H4', np.float64(2.867)), ('L0H0', np.float64(2.252)), ('L0H2', np.float64(1.27)), ('L0H3', np.float64(1.134)), ('L1H0', np.float64(1.123)), ('L0H5', np.float64(1.057))]\nL3H0 [('L0H4', np.float64(1.839)), ('L0H0', np.float64(1.405)), ('L0H2', np.float64(1.145)), ('L0H5', np.float64(1.113)), ('L1H0', np.float64(1.108)), ('L1H2', np.float64(1.04))]\nL3H1 [('L0H5', np.float64(1.359)), ('L0H4', np.float64(1.222)), ('L2H2', np.float64(1.201)), ('L1H2', np.float64(1.178)), ('L1H4', np.float64(1.16)), ('L0H2', np.float64(1.137))]\nL3H2 [('L0H4', np.float64(2.236)), ('L0H0', np.float64(1.658)), ('L0H2', np.float64(1.108)), ('L0H5', np.float64(1.088)), ('L1H0', np.float64(1.076)), ('L1H4', np.float64(1.004))]\nL3H3 [('L0H4', np.float64(1.282)), ('L0H2', np.float64(1.135)), ('L2H1', np.float64(1.099)), ('L1H0', np.float64(1.099)), ('L0H0', np.float64(1.076)), ('L1H4', np.float64(1.025))]\nL3H4 [('L0H4', np.float64(2.286)), ('L0H0', np.float64(1.736)), ('L0H2', np.float64(1.144)), ('L1H0', np.float64(1.106)), ('L1H2', np.float64(1.034)), ('L2H5', np.float64(0.99))]\nL3H5 [('L0H5', np.float64(1.534)), ('L2H2', np.float64(1.477)), ('L1H3', np.float64(1.38)), ('L1H4', np.float64(1.368)), ('L2H3', np.float64(1.347)), ('L1H2', np.float64(1.345))]\n",
  "seconds": 2.234,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "510ebf4ae08b4651012cda6965af8055600af838918231173777bbb2f1b8f254",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
L0 ln1_w: largest 45 coordinates
[(211, 1.0134), (273, 0.9813), (223, 0.9731), (180, 0.9483), (50, 0.9335), (162, 0.9129), (60, 0.9031), (228, 0.9005), (48, 0.8725), (307, 0.8441), (359, 0.8331), (184, 0.8285), (240, 0.8178), (125, 0.8091), (132, 0.8068), (169, 0.8059), (314, 0.7991), (317, 0.7904), (300, 0.7823), (152, 0.7762), (158, 0.773), (246, 0.7606), (256, 0.7391), (59, 0.7363), (351, 0.7351), (356, 0.7144), (191, 0.6965), (224, 0.6901), (102, 0.6864), (15, 0.6845), (178, 0.6671), (151, 0.654), (320, 0.6535), (12, 0.6526), (136, 0.6417), (56, 0.6349), (239, 0.6336), (207, 0.6255), (35, 0.6168), (21, 0.6076), (28, 0.5958), (373, 0.5931), (360, 0.5884), (47, 0.5863), (204, 0.5858)]

Effective QK spectra and largest-magnitude eigenvalues
L0H0 norm 0.91 SV [0.709 0.541 0.106 0.064 0.045 0.04  0.033 0.033] eig [-0.392+0.j     0.048+0.069j  0.048-0.069j -0.023+0.j    -0.009+0.013j
 -0.009-0.013j -0.013+0.002j -0.013-0.002j] symfrac 0.591
L0H1 norm 7.02 SV [4.811 2.985 2.375 1.692 1.591 1.239 1.142 0.718] eig [ 1.742+2.761j  1.742-2.761j -2.275+0.j    -0.985+1.196j -0.985-1.196j
 -0.778+0.784j -0.778-0.784j -0.254-0.33j ] symfrac 0.45
L0H2 norm 1.396 SV [1.056 0.638 0.35  0.247 0.224 0.202 0.169 0.147] eig [ 0.725+0.j     0.426+0.j    -0.176+0.j    -0.106+0.006j -0.106-0.006j
  0.091+0.j    -0.08 +0.j    -0.045+0.033j] symfrac 0.701
L0H3 norm 1.916 SV [1.316 1.12  0.649 0.249 0.234 0.169 0.147 0.132] eig [-0.62 +0.044j -0.62 -0.044j -0.159+0.j     0.048+0.133j  0.048-0.133j
  0.06 +0.j    -0.049+0.013j -0.049-0.013j] symfrac 0.605
L0H4 norm 0.074 SV [0.036 0.022 0.02  0.016 0.016 0.015 0.013 0.013] eig [-0.01 +0.005j -0.01 -0.005j -0.004+0.004j -0.004-0.004j  0.001+0.005j
  0.001-0.005j -0.005+0.j     0.005+0.j   ] symfrac 0.512
L0H5 norm 7.764 SV [3.279 2.892 2.188 2.032 1.958 1.78  1.726 1.636] eig [1.656+2.197j 1.656-2.197j 0.911+1.375j 0.911-1.375j 1.427+0.j
 1.237+0.675j 1.237-0.675j 0.278+1.326j] symfrac 0.386
L1H0 norm 5.152 SV [2.83  2.014 1.745 1.34  1.261 1.111 0.964 0.946] eig [ 0.226+0.41j   0.226-0.41j  -0.41 +0.186j -0.41 -0.186j -0.406+0.j
 -0.328+0.106j -0.328-0.106j -0.334+0.j   ] symfrac 0.51
L1H1 norm 12.675 SV [9.164 5.291 3.674 2.61  1.989 1.719 1.705 1.488] eig [ 0.642+2.083j  0.642-2.083j -1.152+0.068j -1.152-0.068j -0.74 +0.631j
 -0.74 -0.631j -0.816+0.j    -0.794+0.153j] symfrac 0.498
L1H2 norm 9.886 SV [6.455 5.544 2.657 1.642 1.444 1.27  1.219 1.143] eig [ 0.457+1.616j  0.457-1.616j -0.864+0.j    -0.559+0.397j -0.559-0.397j
 -0.628+0.036j -0.628-0.036j -0.511+0.j   ] symfrac 0.492
L1H3 norm 10.475 SV [7.302 5.595 2.473 1.619 1.423 1.339 1.2   1.157] eig [ 0.609+2.177j  0.609-2.177j -0.755+0.j     0.735+0.j    -0.64 +0.193j
 -0.64 -0.193j -0.615+0.j    -0.576+0.j   ] symfrac 0.478
L1H4 norm 9.993 SV [6.784 5.233 2.719 1.705 1.53  1.294 1.237 1.209] eig [ 0.567+1.91j   0.567-1.91j  -0.677+0.113j -0.677-0.113j -0.621+0.j
 -0.516+0.225j -0.516-0.225j -0.556+0.j   ] symfrac 0.481
L1H5 norm 11.691 SV [8.071 4.438 3.852 3.6   1.939 1.717 1.585 1.367] eig [ 0.577+1.634j  0.577-1.634j -1.251+0.j    -0.988+0.j    -0.662+0.422j
 -0.662-0.422j -0.641-0.242j -0.641+0.242j] symfrac 0.505
L2H0 norm 16.364 SV [4.147 3.88  3.67  3.328 3.255 3.174 3.11  3.023] eig [ 0.263+1.058j  0.263-1.058j -0.936+0.j     0.724+0.593j  0.724-0.593j
  0.295+0.832j  0.295-0.832j -0.823-0.248j] symfrac 0.499
L2H1 norm 15.563 SV [3.666 3.366 3.17  3.148 2.939 2.838 2.819 2.757] eig [1.137-0.284j 1.137+0.284j 1.136+0.j    1.128-0.088j 1.128+0.088j
 1.051-0.175j 1.051+0.175j 0.821+0.634j] symfrac 0.52
L2H2 norm 22.432 SV [5.671 5.545 5.406 5.075 4.889 4.782 4.468 4.387] eig [ 2.069+0.j     0.637+1.729j  0.637-1.729j  1.544+0.466j  1.544-0.466j
  1.558+0.206j  1.558-0.206j -1.377+0.178j] symfrac 0.516
L2H3 norm 15.807 SV [4.566 3.385 3.295 3.137 3.103 2.964 2.914 2.856] eig [ 0.46 +1.476j  0.46 -1.476j  0.012+0.849j  0.012-0.849j -0.437+0.683j
 -0.437-0.683j  0.774+0.209j  0.774-0.209j] symfrac 0.494
L2H4 norm 17.612 SV [6.262 4.697 3.875 3.435 3.333 3.211 3.179 3.08 ] eig [ 0.643+1.761j  0.643-1.761j  1.229+0.j    -0.967+0.j     0.685+0.612j
  0.685-0.612j -0.886+0.227j -0.886-0.227j] symfrac 0.5
L2H5 norm 13.233 SV [3.355 3.314 2.873 2.56  2.517 2.371 2.35  2.307] eig [-0.854+0.j    -0.053+0.811j -0.053-0.811j -0.636+0.362j -0.636-0.362j
  0.723+0.j    -0.536+0.441j -0.536-0.441j] symfrac 0.497
L3H0 norm 16.621 SV [3.243 3.192 3.08  3.013 2.922 2.872 2.846 2.781] eig [1.044+0.j    0.948-0.317j 0.948+0.317j 0.954-0.142j 0.954+0.142j
 0.87 +0.j    0.13 +0.838j 0.13 -0.838j] symfrac 0.51
L3H1 norm 13.558 SV [2.578 2.516 2.469 2.429 2.398 2.363 2.321 2.249] eig [0.877+0.22j  0.877-0.22j  0.897+0.j    0.602+0.57j  0.602-0.57j
 0.819+0.j    0.696+0.322j 0.696-0.322j] symfrac 0.511
L3H2 norm 16.487 SV [3.47  3.288 3.133 2.897 2.867 2.821 2.736 2.7  ] eig [0.286+0.912j 0.286-0.912j 0.909-0.207j 0.909+0.207j 0.022+0.878j
 0.022-0.878j 0.848+0.j    0.563-0.616j] symfrac 0.497
L3H3 norm 8.424 SV [2.099 1.898 1.662 1.617 1.547 1.527 1.436 1.391] eig [ 0.216+0.491j  0.216-0.491j  0.476+0.j     0.208+0.388j  0.208-0.388j
 -0.018+0.406j -0.018-0.406j -0.335+0.205j] symfrac 0.496
L3H4 norm 13.875 SV [3.205 2.956 2.854 2.588 2.501 2.451 2.351 2.32 ] eig [-0.843+0.j     0.746+0.j     0.524+0.473j  0.524-0.473j  0.232+0.633j
  0.232-0.633j -0.018+0.666j -0.018-0.666j] symfrac 0.502
L3H5 norm 16.935 SV [3.233 3.119 3.073 2.957 2.918 2.897 2.879 2.78 ] eig [1.556+0.937j 1.556-0.937j 1.702+0.591j 1.702-0.591j 1.599+0.83j
 1.599-0.83j  1.615+0.609j 1.615-0.609j] symfrac 0.611

Full singular spectra at selected quantiles
W_fc 0 [(1, np.float64(18.11)), (2, np.float64(12.28)), (3, np.float64(8.473)), (4, np.float64(7.902)), (5, np.float64(7.779)), (6, np.float64(7.442)), (10, np.float64(6.805)), (15, np.float64(6.27)), (20, np.float64(5.944)), (25, np.float64(5.609)), (26, np.float64(5.58)), (27, np.float64(5.498)), (28, np.float64(5.447)), (29, np.float64(5.377)), (30, np.float64(5.334)), (32, np.float64(5.203)), (40, np.float64(4.716)), (50, np.float64(4.081)), (64, np.float64(3.281)), (80, np.float64(2.205)), (100, np.float64(1.622)), (128, np.float64(1.264)), (192, np.float64(0.94)), (256, np.float64(0.75)), (320, np.float64(0.596)), (384, np.float64(0.421))]
W_proj 0 [(1, np.float64(10.932)), (2, np.float64(7.576)), (3, np.float64(6.628)), (4, np.float64(6.293)), (5, np.float64(6.166)), (6, np.float64(5.924)), (10, np.float64(5.61)), (15, np.float64(5.193)), (20, np.float64(4.937)), (25, np.float64(4.651)), (26, np.float64(4.637)), (27, np.float64(4.548)), (28, np.float64(4.526)), (29, np.float64(4.487)), (30, np.float64(4.404)), (32, np.float64(4.283)), (40, np.float64(4.023)), (50, np.float64(3.694)), (64, np.float64(3.268)), (80, np.float64(2.868)), (100, np.float64(2.413)), (128, np.float64(1.936)), (192, np.float64(1.172)), (256, np.float64(0.708)), (320, np.float64(0.432)), (384, np.float64(0.244))]
W_fc 3 [(1, np.float64(14.373)), (2, np.float64(8.176)), (3, np.float64(7.973)), (4, np.float64(7.83)), (5, np.float64(7.036)), (6, np.float64(6.757)), (10, np.float64(6.387)), (15, np.float64(6.162)), (20, np.float64(5.895)), (25, np.float64(5.753)), (26, np.float64(5.699)), (27, np.float64(5.681)), (28, np.float64(5.652)), (29, np.float64(5.641)), (30, np.float64(5.641)), (32, np.float64(5.564)), (40, np.float64(5.357)), (50, np.float64(5.178)), (64, np.float64(4.945)), (80, np.float64(4.733)), (100, np.float64(4.483)), (128, np.float64(4.184)), (192, np.float64(3.606)), (256, np.float64(3.093)), (320, np.float64(2.545)), (384, np.float64(0.708))]
W_proj 3 [(1, np.float64(28.175)), (2, np.float64(20.293)), (3, np.float64(19.292)), (4, np.float64(19.135)), (5, np.float64(18.708)), (6, np.float64(18.57)), (10, np.float64(16.54)), (15, np.float64(14.417)), (20, np.float64(12.37)), (25, np.float64(10.769)), (26, np.float64(9.227)), (27, np.float64(8.573)), (28, np.float64(7.929)), (29, np.float64(6.5)), (30, np.float64(5.271)), (32, np.float64(3.184)), (40, np.float64(2.29)), (50, np.float64(1.926)), (64, np.float64(1.692)), (80, np.float64(1.508)), (100, np.float64(1.319)), (128, np.float64(1.138)), (192, np.float64(0.832)), (256, np.float64(0.632)), (320, np.float64(0.481)), (384, np.float64(0.324))]

L0 QK pairwise Frobenius cosine
[[ 1.     0.179  0.112  0.206  0.063  0.093]
 [ 0.179  1.     0.21  -0.15   0.005  0.414]
 [ 0.112  0.21   1.    -0.247 -0.006  0.114]
 [ 0.206 -0.15  -0.247  1.    -0.027 -0.098]
 [ 0.063  0.005 -0.006 -0.027  1.    -0.004]
 [ 0.093  0.414  0.114 -0.098 -0.004  1.   ]]

Weight-only key-composition enrichment
For later head B and earlier head A: ||QK_B OV_A||_F / (||QK_B||_F ||OV_A||_F/sqrt(384)).
L1H0 [('L0H4', np.float64(7.74)), ('L0H0', np.float64(6.237)), ('L0H3', np.float64(2.815)), ('L0H2', np.float64(1.686)), ('L0H5', np.float64(1.307)), ('L0H1', np.float64(1.109))]
L1H1 [('L0H4', np.float64(10.672)), ('L0H0', np.float64(8.436)), ('L0H3', np.float64(3.299)), ('L0H2', np.float64(2.049)), ('L0H5', np.float64(1.007)), ('L0H1', np.float64(0.58))]
L1H2 [('L0H4', np.float64(10.165)), ('L0H0', np.float64(8.084)), ('L0H3', np.float64(3.365)), ('L0H2', np.float64(1.976)), ('L0H5', np.float64(0.944)), ('L0H1', np.float64(0.611))]
L1H3 [('L0H4', np.float64(10.707)), ('L0H0', np.float64(8.609)), ('L0H3', np.float64(3.634)), ('L0H2', np.float64(2.128)), ('L0H5', np.float64(0.957)), ('L0H1', np.float64(0.567))]
L1H4 [('L0H4', np.float64(10.328)), ('L0H0', np.float64(8.356)), ('L0H3', np.float64(3.51)), ('L0H2', np.float64(2.123)), ('L0H5', np.float64(1.02)), ('L0H1', np.float64(0.592))]
L1H5 [('L0H4', np.float64(9.908)), ('L0H0', np.float64(7.846)), ('L0H3', np.float64(2.812)), ('L0H2', np.float64(1.79)), ('L0H5', np.float64(1.195)), ('L0H1', np.float64(0.664))]
L2H0 [('L0H4', np.float64(3.137)), ('L0H0', np.float64(2.5)), ('L0H3', np.float64(1.296)), ('L1H0', np.float64(1.277)), ('L0H5', np.float64(1.233)), ('L0H2', np.float64(1.227))]
L2H1 [('L0H5', np.float64(1.492)), ('L1H3', np.float64(1.389)), ('L1H2', np.float64(1.355)), ('L1H4', np.float64(1.349)), ('L1H0', np.float64(1.3)), ('L1H1', np.float64(1.259))]
L2H2 [('L0H4', np.float64(3.585)), ('L0H0', np.float64(2.872)), ('L0H3', np.float64(1.499)), ('L0H5', np.float64(1.384)), ('L1H0', np.float64(1.2)), ('L0H2', np.float64(1.176))]
L2H3 [('L0H4', np.float64(4.201)), ('L0H0', np.float64(3.344)), ('L0H3', np.float64(1.71)), ('L0H2', np.float64(1.236)), ('L0H5', np.float64(1.171)), ('L1H0', np.float64(1.158))]
L2H4 [('L0H4', np.float64(4.245)), ('L0H0', np.float64(3.401)), ('L0H2', np.float64(1.808)), ('L0H3', np.float64(1.718)), ('L1H0', np.float64(1.584)), ('L0H5', np.float64(1.195))]
L2H5 [('L0H4', np.float64(2.867)), ('L0H0', np.float64(2.252)), ('L0H2', np.float64(1.27)), ('L0H3', np.float64(1.134)), ('L1H0', np.float64(1.123)), ('L0H5', np.float64(1.057))]
L3H0 [('L0H4', np.float64(1.839)), ('L0H0', np.float64(1.405)), ('L0H2', np.float64(1.145)), ('L0H5', np.float64(1.113)), ('L1H0', np.float64(1.108)), ('L1H2', np.float64(1.04))]
L3H1 [('L0H5', np.float64(1.359)), ('L0H4', np.float64(1.222)), ('L2H2', np.float64(1.201)), ('L1H2', np.float64(1.178)), ('L1H4', np.float64(1.16)), ('L0H2', np.float64(1.137))]
L3H2 [('L0H4', np.float64(2.236)), ('L0H0', np.float64(1.658)), ('L0H2', np.float64(1.108)), ('L0H5', np.float64(1.088)), ('L1H0', np.float64(1.076)), ('L1H4', np.float64(1.004))]
L3H3 [('L0H4', np.float64(1.282)), ('L0H2', np.float64(1.135)), ('L2H1', np.float64(1.099)), ('L1H0', np.float64(1.099)), ('L0H0', np.float64(1.076)), ('L1H4', np.float64(1.025))]
L3H4 [('L0H4', np.float64(2.286)), ('L0H0', np.float64(1.736)), ('L0H2', np.float64(1.144)), ('L1H0', np.float64(1.106)), ('L1H2', np.float64(1.034)), ('L2H5', np.float64(0.99))]
L3H5 [('L0H5', np.float64(1.534)), ('L2H2', np.float64(1.477)), ('L1H3', np.float64(1.38)), ('L1H4', np.float64(1.368)), ('L2H3', np.float64(1.347)), ('L1H2', np.float64(1.345))]


========================================================================
TURN t003
MESSAGE SENT TO CODEX
OUTPUT of run 2 of at most 100:
```
L0 ln1_w: largest 45 coordinates
[(211, 1.0134), (273, 0.9813), (223, 0.9731), (180, 0.9483), (50, 0.9335), (162, 0.9129), (60, 0.9031), (228, 0.9005), (48, 0.8725), (307, 0.8441), (359, 0.8331), (184, 0.8285), (240, 0.8178), (125, 0.8091), (132, 0.8068), (169, 0.8059), (314, 0.7991), (317, 0.7904), (300, 0.7823), (152, 0.7762), (158, 0.773), (246, 0.7606), (256, 0.7391), (59, 0.7363), (351, 0.7351), (356, 0.7144), (191, 0.6965), (224, 0.6901), (102, 0.6864), (15, 0.6845), (178, 0.6671), (151, 0.654), (320, 0.6535), (12, 0.6526), (136, 0.6417), (56, 0.6349), (239, 0.6336), (207, 0.6255), (35, 0.6168), (21, 0.6076), (28, 0.5958), (373, 0.5931), (360, 0.5884), (47, 0.5863), (204, 0.5858)]

Effective QK spectra and largest-magnitude eigenvalues
L0H0 norm 0.91 SV [0.709 0.541 0.106 0.064 0.045 0.04  0.033 0.033] eig [-0.392+0.j     0.048+0.069j  0.048-0.069j -0.023+0.j    -0.009+0.013j
 -0.009-0.013j -0.013+0.002j -0.013-0.002j] symfrac 0.591
L0H1 norm 7.02 SV [4.811 2.985 2.375 1.692 1.591 1.239 1.142 0.718] eig [ 1.742+2.761j  1.742-2.761j -2.275+0.j    -0.985+1.196j -0.985-1.196j
 -0.778+0.784j -0.778-0.784j -0.254-0.33j ] symfrac 0.45
L0H2 norm 1.396 SV [1.056 0.638 0.35  0.247 0.224 0.202 0.169 0.147] eig [ 0.725+0.j     0.426+0.j    -0.176+0.j    -0.106+0.006j -0.106-0.006j
  0.091+0.j    -0.08 +0.j    -0.045+0.033j] symfrac 0.701
L0H3 norm 1.916 SV [1.316 1.12  0.649 0.249 0.234 0.169 0.147 0.132] eig [-0.62 +0.044j -0.62 -0.044j -0.159+0.j     0.048+0.133j  0.048-0.133j
  0.06 +0.j    -0.049+0.013j -0.049-0.013j] symfrac 0.605
L0H4 norm 0.074 SV [0.036 0.022 0.02  0.016 0.016 0.015 0.013 0.013] eig [-0.01 +0.005j -0.01 -0.005j -0.004+0.004j -0.004-0.004j  0.001+0.005j
  0.001-0.005j -0.005+0.j     0.005+0.j   ] symfrac 0.512
L0H5 norm 7.764 SV [3.279 2.892 2.188 2.032 1.958 1.78  1.726 1.636] eig [1.656+2.197j 1.656-2.197j 0.911+1.375j 0.911-1.375j 1.427+0.j
 1.237+0.675j 1.237-0.675j 0.278+1.326j] symfrac 0.386
L1H0 norm 5.152 SV [2.83  2.014 1.745 1.34  1.261 1.111 0.964 0.946] eig [ 0.226+0.41j   0.226-0.41j  -0.41 +0.186j -0.41 -0.186j -0.406+0.j
 -0.328+0.106j -0.328-0.106j -0.334+0.j   ] symfrac 0.51
L1H1 norm 12.675 SV [9.164 5.291 3.674 2.61  1.989 1.719 1.705 1.488] eig [ 0.642+2.083j  0.642-2.083j -1.152+0.068j -1.152-0.068j -0.74 +0.631j
 -0.74 -0.631j -0.816+0.j    -0.794+0.153j] symfrac 0.498
L1H2 norm 9.886 SV [6.455 5.544 2.657 1.642 1.444 1.27  1.219 1.143] eig [ 0.457+1.616j  0.457-1.616j -0.864+0.j    -0.559+0.397j -0.559-0.397j
 -0.628+0.036j -0.628-0.036j -0.511+0.j   ] symfrac 0.492
L1H3 norm 10.475 SV [7.302 5.595 2.473 1.619 1.423 1.339 1.2   1.157] eig [ 0.609+2.177j  0.609-2.177j -0.755+0.j     0.735+0.j    -0.64 +0.193j
 -0.64 -0.193j -0.615+0.j    -0.576+0.j   ] symfrac 0.478
L1H4 norm 9.993 SV [6.784 5.233 2.719 1.705 1.53  1.294 1.237 1.209] eig [ 0.567+1.91j   0.567-1.91j  -0.677+0.113j -0.677-0.113j -0.621+0.j
 -0.516+0.225j -0.516-0.225j -0.556+0.j   ] symfrac 0.481
L1H5 norm 11.691 SV [8.071 4.438 3.852 3.6   1.939 1.717 1.585 1.367] eig [ 0.577+1.634j  0.577-1.634j -1.251+0.j    -0.988+0.j    -0.662+0.422j
 -0.662-0.422j -0.641-0.242j -0.641+0.242j] symfrac 0.505
L2H0 norm 16.364 SV [4.147 3.88  3.67  3.328 3.255 3.174 3.11  3.023] eig [ 0.263+1.058j  0.263-1.058j -0.936+0.j     0.724+0.593j  0.724-0.593j
  0.295+0.832j  0.295-0.832j -0.823-0.248j] symfrac 0.499
L2H1 norm 15.563 SV [3.666 3.366 3.17  3.148 2.939 2.838 2.819 2.757] eig [1.137-0.284j 1.137+0.284j 1.136+0.j    1.128-0.088j 1.128+0.088j
 1.051-0.175j 1.051+0.175j 0.821+0.634j] symfrac 0.52
L2H2 norm 22.432 SV [5.671 5.545 5.406 5.075 4.889 4.782 4.468 4.387] eig [ 2.069+0.j     0.637+1.729j  0.637-1.729j  1.544+0.466j  1.544-0.466j
  1.558+0.206j  1.558-0.206j -1.377+0.178j] symfrac 0.516
L2H3 norm 15.807 SV [4.566 3.385 3.295 3.137 3.103 2.964 2.914 2.856] eig [ 0.46 +1.476j  0.46 -1.476j  0.012+0.849j  0.012-0.849j -0.437+0.683j
 -0.437-0.683j  0.774+0.209j  0.774-0.209j] symfrac 0.494
L2H4 norm 17.612 SV [6.262 4.697 3.875 3.435 3.333 3.211 3.179 3.08 ] eig [ 0.643+1.761j  0.643-1.761j  1.229+0.j    -0.967+0.j     0.685+0.612j
  0.685-0.612j -0.886+0.227j -0.886-0.227j] symfrac 0.5
L2H5 norm 13.233 SV [3.355 3.314 2.873 2.56  2.517 2.371 2.35  2.307] eig [-0.854+0.j    -0.053+0.811j -0.053-0.811j -0.636+0.362j -0.636-0.362j
  0.723+0.j    -0.536+0.441j -0.536-0.441j] symfrac 0.497
L3H0 norm 16.621 SV [3.243 3.192 3.08  3.013 2.922 2.872 2.846 2.781] eig [1.044+0.j    0.948-0.317j 0.948+0.317j 0.954-0.142j 0.954+0.142j
 0.87 +0.j    0.13 +0.838j 0.13 -0.838j] symfrac 0.51
L3H1 norm 13.558 SV [2.578 2.516 2.469 2.429 2.398 2.363 2.321 2.249] eig [0.877+0.22j  0.877-0.22j  0.897+0.j    0.602+0.57j  0.602-0.57j
 0.819+0.j    0.696+0.322j 0.696-0.322j] symfrac 0.511
L3H2 norm 16.487 SV [3.47  3.288 3.133 2.897 2.867 2.821 2.736 2.7  ] eig [0.286+0.912j 0.286-0.912j 0.909-0.207j 0.909+0.207j 0.022+0.878j
 0.022-0.878j 0.848+0.j    0.563-0.616j] symfrac 0.497
L3H3 norm 8.424 SV [2.099 1.898 1.662 1.617 1.547 1.527 1.436 1.391] eig [ 0.216+0.491j  0.216-0.491j  0.476+0.j     0.208+0.388j  0.208-0.388j
 -0.018+0.406j -0.018-0.406j -0.335+0.205j] symfrac 0.496
L3H4 norm 13.875 SV [3.205 2.956 2.854 2.588 2.501 2.451 2.351 2.32 ] eig [-0.843+0.j     0.746+0.j     0.524+0.473j  0.524-0.473j  0.232+0.633j
  0.232-0.633j -0.018+0.666j -0.018-0.666j] symfrac 0.502
L3H5 norm 16.935 SV [3.233 3.119 3.073 2.957 2.918 2.897 2.879 2.78 ] eig [1.556+0.937j 1.556-0.937j 1.702+0.591j 1.702-0.591j 1.599+0.83j
 1.599-0.83j  1.615+0.609j 1.615-0.609j] symfrac 0.611

Full singular spectra at selected quantiles
W_fc 0 [(1, np.float64(18.11)), (2, np.float64(12.28)), (3, np.float64(8.473)), (4, np.float64(7.902)), (5, np.float64(7.779)), (6, np.float64(7.442)), (10, np.float64(6.805)), (15, np.float64(6.27)), (20, np.float64(5.944)), (25, np.float64(5.609)), (26, np.float64(5.58)), (27, np.float64(5.498)), (28, np.float64(5.447)), (29, np.float64(5.377)), (30, np.float64(5.334)), (32, np.float64(5.203)), (40, np.float64(4.716)), (50, np.float64(4.081)), (64, np.float64(3.281)), (80, np.float64(2.205)), (100, np.float64(1.622)), (128, np.float64(1.264)), (192, np.float64(0.94)), (256, np.float64(0.75)), (320, np.float64(0.596)), (384, np.float64(0.421))]
W_proj 0 [(1, np.float64(10.932)), (2, np.float64(7.576)), (3, np.float64(6.628)), (4, np.float64(6.293)), (5, np.float64(6.166)), (6, np.float64(5.924)), (10, np.float64(5.61)), (15, np.float64(5.193)), (20, np.float64(4.937)), (25, np.float64(4.651)), (26, np.float64(4.637)), (27, np.float64(4.548)), (28, np.float64(4.526)), (29, np.float64(4.487)), (30, np.float64(4.404)), (32, np.float64(4.283)), (40, np.float64(4.023)), (50, np.float64(3.694)), (64, np.float64(3.268)), (80, np.float64(2.868)), (100, np.float64(2.413)), (128, np.float64(1.936)), (192, np.float64(1.172)), (256, np.float64(0.708)), (320, np.float64(0.432)), (384, np.float64(0.244))]
W_fc 3 [(1, np.float64(14.373)), (2, np.float64(8.176)), (3, np.float64(7.973)), (4, np.float64(7.83)), (5, np.float64(7.036)), (6, np.float64(6.757)), (10, np.float64(6.387)), (15, np.float64(6.162)), (20, np.float64(5.895)), (25, np.float64(5.753)), (26, np.float64(5.699)), (27, np.float64(5.681)), (28, np.float64(5.652)), (29, np.float64(5.641)), (30, np.float64(5.641)), (32, np.float64(5.564)), (40, np.float64(5.357)), (50, np.float64(5.178)), (64, np.float64(4.945)), (80, np.float64(4.733)), (100, np.float64(4.483)), (128, np.float64(4.184)), (192, np.float64(3.606)), (256, np.float64(3.093)), (320, np.float64(2.545)), (384, np.float64(0.708))]
W_proj 3 [(1, np.float64(28.175)), (2, np.float64(20.293)), (3, np.float64(19.292)), (4, np.float64(19.135)), (5, np.float64(18.708)), (6, np.float64(18.57)), (10, np.float64(16.54)), (15, np.float64(14.417)), (20, np.float64(12.37)), (25, np.float64(10.769)), (26, np.float64(9.227)), (27, np.float64(8.573)), (28, np.float64(7.929)), (29, np.float64(6.5)), (30, np.float64(5.271)), (32, np.float64(3.184)), (40, np.float64(2.29)), (50, np.float64(1.926)), (64, np.float64(1.692)), (80, np.float64(1.508)), (100, np.float64(1.319)), (128, np.float64(1.138)), (192, np.float64(0.832)), (256, np.float64(0.632)), (320, np.float64(0.481)), (384, np.float64(0.324))]

L0 QK pairwise Frobenius cosine
[[ 1.     0.179  0.112  0.206  0.063  0.093]
 [ 0.179  1.     0.21  -0.15   0.005  0.414]
 [ 0.112  0.21   1.    -0.247 -0.006  0.114]
 [ 0.206 -0.15  -0.247  1.    -0.027 -0.098]
 [ 0.063  0.005 -0.006 -0.027  1.    -0.004]
 [ 0.093  0.414  0.114 -0.098 -0.004  1.   ]]

Weight-only key-composition enrichment
For later head B and earlier head A: ||QK_B OV_A||_F / (||QK_B||_F ||OV_A||_F/sqrt(384)).
L1H0 [('L0H4', np.float64(7.74)), ('L0H0', np.float64(6.237)), ('L0H3', np.float64(2.815)), ('L0H2', np.float64(1.686)), ('L0H5', np.float64(1.307)), ('L0H1', np.float64(1.109))]
L1H1 [('L0H4', np.float64(10.672)), ('L0H0', np.float64(8.436)), ('L0H3', np.float64(3.299)), ('L0H2', np.float64(2.049)), ('L0H5', np.float64(1.007)), ('L0H1', np.float64(0.58))]
L1H2 [('L0H4', np.float64(10.165)), ('L0H0', np.float64(8.084)), ('L0H3', np.float64(3.365)), ('L0H2', np.float64(1.976)), ('L0H5', np.float64(0.944)), ('L0H1', np.float64(0.611))]
L1H3 [('L0H4', np.float64(10.707)), ('L0H0', np.float64(8.609)), ('L0H3', np.float64(3.634)), ('L0H2', np.float64(2.128)), ('L0H5', np.float64(0.957)), ('L0H1', np.float64(0.567))]
L1H4 [('L0H4', np.float64(10.328)), ('L0H0', np.float64(8.356)), ('L0H3', np.float64(3.51)), ('L0H2', np.float64(2.123)), ('L0H5', np.float64(1.02)), ('L0H1', np.float64(0.592))]
L1H5 [('L0H4', np.float64(9.908)), ('L0H0', np.float64(7.846)), ('L0H3', np.float64(2.812)), ('L0H2', np.float64(1.79)), ('L0H5', np.float64(1.195)), ('L0H1', np.float64(0.664))]
L2H0 [('L0H4', np.float64(3.137)), ('L0H0', np.float64(2.5)), ('L0H3', np.float64(1.296)), ('L1H0', np.float64(1.277)), ('L0H5', np.float64(1.233)), ('L0H2', np.float64(1.227))]
L2H1 [('L0H5', np.float64(1.492)), ('L1H3', np.float64(1.389)), ('L1H2', np.float64(1.355)), ('L1H4', np.float64(1.349)), ('L1H0', np.float64(1.3)), ('L1H1', np.float64(1.259))]
L2H2 [('L0H4', np.float64(3.585)), ('L0H0', np.float64(2.872)), ('L0H3', np.float64(1.499)), ('L0H5', np.float64(1.384)), ('L1H0', np.float64(1.2)), ('L0H2', np.float64(1.176))]
L2H3 [('L0H4', np.float64(4.201)), ('L0H0', np.float64(3.344)), ('L0H3', np.float64(1.71)), ('L0H2', np.float64(1.236)), ('L0H5', np.float64(1.171)), ('L1H0', np.float64(1.158))]
L2H4 [('L0H4', np.float64(4.245)), ('L0H0', np.float64(3.401)), ('L0H2', np.float64(1.808)), ('L0H3', np.float64(1.718)), ('L1H0', np.float64(1.584)), ('L0H5', np.float64(1.195))]
L2H5 [('L0H4', np.float64(2.867)), ('L0H0', np.float64(2.252)), ('L0H2', np.float64(1.27)), ('L0H3', np.float64(1.134)), ('L1H0', np.float64(1.123)), ('L0H5', np.float64(1.057))]
L3H0 [('L0H4', np.float64(1.839)), ('L0H0', np.float64(1.405)), ('L0H2', np.float64(1.145)), ('L0H5', np.float64(1.113)), ('L1H0', np.float64(1.108)), ('L1H2', np.float64(1.04))]
L3H1 [('L0H5', np.float64(1.359)), ('L0H4', np.float64(1.222)), ('L2H2', np.float64(1.201)), ('L1H2', np.float64(1.178)), ('L1H4', np.float64(1.16)), ('L0H2', np.float64(1.137))]
L3H2 [('L0H4', np.float64(2.236)), ('L0H0', np.float64(1.658)), ('L0H2', np.float64(1.108)), ('L0H5', np.float64(1.088)), ('L1H0', np.float64(1.076)), ('L1H4', np.float64(1.004))]
L3H3 [('L0H4', np.float64(1.282)), ('L0H2', np.float64(1.135)), ('L2H1', np.float64(1.099)), ('L1H0', np.float64(1.099)), ('L0H0', np.float64(1.076)), ('L1H4', np.float64(1.025))]
L3H4 [('L0H4', np.float64(2.286)), ('L0H0', np.float64(1.736)), ('L0H2', np.float64(1.144)), ('L1H0', np.float64(1.106)), ('L1H2', np.float64(1.034)), ('L2H5', np.float64(0.99))]
L3H5 [('L0H5', np.float64(1.534)), ('L2H2', np.float64(1.477)), ('L1H3', np.float64(1.38)), ('L1H4', np.float64(1.368)), ('L2H3', np.float64(1.347)), ('L1H2', np.float64(1.345))]

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svdvals, eigvals
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d = 384
P = np.eye(d) - np.ones((d,d))/d
A, B = {}, {}
for l in range(4):
    scale = np.asarray(W[f"ln1_w.{l}"],float)
    for h in range(6):
        s = slice(64*h,64*(h+1))
        q,k,v = [np.asarray(W[f"W_{n}.{l}"][s],float) for n in ["q","k","v"]]
        o = np.asarray(W[f"W_o.{l}"][:,s],float)
        A[l,h] = P @ (scale[:,None]*(q.T@k)*scale[None,:]) @ P
        B[l,h] = o @ (v*scale[None,:]) @ P

print("L0 per-head V and O singular values (first 35)")
for h in range(6):
    s = slice(h*64,(h+1)*64)
    for name,a in [
        ("V",np.asarray(W["W_v.0"][s],float)),
        ("Vscaled",np.asarray(W["W_v.0"][s],float)*W["ln1_w.0"][None,:]),
        ("O",np.asarray(W["W_o.0"][:,s],float))]:
        print(h,name,np.round(svdvals(a)[:35],3))

print("\nSigned key-composition trace: sqrt(d)*tr(A_B B_A)/(||A_B||F ||B_A||F)")
print("Columns are L0H0..L0H5. Large positive values are a matching-circuit clue, not an attention measurement.")
for l in range(1,4):
    for h in range(6):
        a=A[l,h]
        vals=[np.sqrt(d)*np.trace(a@B[0,k])/(np.linalg.norm(a)*np.linalg.norm(B[0,k]))
              for k in range(6)]
        print(f"L{l}H{h}",np.round(vals,3))

print("\nStrongest signed composition traces over all earlier heads")
for l in range(1,4):
    for h in range(6):
        a=A[l,h]
        vals=[]
        for m in range(l):
            for k in range(6):
                b=B[m,k]
                z=np.sqrt(d)*np.trace(a@b)/(np.linalg.norm(a)*np.linalg.norm(b))
                vals.append((z,f"L{m}H{k}"))
        vals.sort(reverse=True)
        print(f"L{l}H{h}",[(n,round(z,3)) for z,n in vals[:4]])

print("\nL0 QK/V subspace overlap")
for h in range(6):
    a=A[0,h]
    vals=[]
    for k in [1,4,5]:
        s=slice(64*k,64*(k+1))
        v=(np.asarray(W["W_v.0"][s],float)*W["ln1_w.0"][None,:])@P
        _,_,vt=np.linalg.svd(v,full_matrices=False)
        u=vt[:28].T
        vals.append((k,round(np.linalg.norm(a@u)**2/np.linalg.norm(a)**2,3),
                     round(np.linalg.norm(u.T@a)**2/np.linalg.norm(a)**2,3)))
    print(f"L0H{h}",vals)

print("\nAffine key-prior coefficient norm from ln1_b, relative to bilinear Frobenius norm")
for l in range(4):
    scale=np.asarray(W[f"ln1_w.{l}"],float)
    bias=np.asarray(W[f"ln1_b.{l}"],float)
    for h in range(6):
        s=slice(64*h,64*(h+1))
        q,k=[np.asarray(W[f"W_{n}.{l}"][s],float) for n in ["q","k"]]
        c=P@(scale*(k.T@(q@bias)))
        print(f"L{l}H{h}",round(np.linalg.norm(c),3),
              round(np.linalg.norm(c)/np.linalg.norm(A[l,h]),3))
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 3,
  "turn": "t003",
  "code": "import numpy as np\nfrom scipy.linalg import svdvals, eigvals\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd = 384\nP = np.eye(d) - np.ones((d,d))/d\nA, B = {}, {}\nfor l in range(4):\n    scale = np.asarray(W[f\"ln1_w.{l}\"],float)\n    for h in range(6):\n        s = slice(64*h,64*(h+1))\n        q,k,v = [np.asarray(W[f\"W_{n}.{l}\"][s],float) for n in [\"q\",\"k\",\"v\"]]\n        o = np.asarray(W[f\"W_o.{l}\"][:,s],float)\n        A[l,h] = P @ (scale[:,None]*(q.T@k)*scale[None,:]) @ P\n        B[l,h] = o @ (v*scale[None,:]) @ P\n\nprint(\"L0 per-head V and O singular values (first 35)\")\nfor h in range(6):\n    s = slice(h*64,(h+1)*64)\n    for name,a in [\n        (\"V\",np.asarray(W[\"W_v.0\"][s],float)),\n        (\"Vscaled\",np.asarray(W[\"W_v.0\"][s],float)*W[\"ln1_w.0\"][None,:]),\n        (\"O\",np.asarray(W[\"W_o.0\"][:,s],float))]:\n        print(h,name,np.round(svdvals(a)[:35],3))\n\nprint(\"\\nSigned key-composition trace: sqrt(d)*tr(A_B B_A)/(||A_B||F ||B_A||F)\")\nprint(\"Columns are L0H0..L0H5. Large positive values are a matching-circuit clue, not an attention measurement.\")\nfor l in range(1,4):\n    for h in range(6):\n        a=A[l,h]\n        vals=[np.sqrt(d)*np.trace(a@B[0,k])/(np.linalg.norm(a)*np.linalg.norm(B[0,k]))\n              for k in range(6)]\n        print(f\"L{l}H{h}\",np.round(vals,3))\n\nprint(\"\\nStrongest signed composition traces over all earlier heads\")\nfor l in range(1,4):\n    for h in range(6):\n        a=A[l,h]\n        vals=[]\n        for m in range(l):\n            for k in range(6):\n                b=B[m,k]\n                z=np.sqrt(d)*np.trace(a@b)/(np.linalg.norm(a)*np.linalg.norm(b))\n                vals.append((z,f\"L{m}H{k}\"))\n        vals.sort(reverse=True)\n        print(f\"L{l}H{h}\",[(n,round(z,3)) for z,n in vals[:4]])\n\nprint(\"\\nL0 QK/V subspace overlap\")\nfor h in range(6):\n    a=A[0,h]\n    vals=[]\n    for k in [1,4,5]:\n        s=slice(64*k,64*(k+1))\n        v=(np.asarray(W[\"W_v.0\"][s],float)*W[\"ln1_w.0\"][None,:])@P\n        _,_,vt=np.linalg.svd(v,full_matrices=False)\n        u=vt[:28].T\n        vals.append((k,round(np.linalg.norm(a@u)**2/np.linalg.norm(a)**2,3),\n                     round(np.linalg.norm(u.T@a)**2/np.linalg.norm(a)**2,3)))\n    print(f\"L0H{h}\",vals)\n\nprint(\"\\nAffine key-prior coefficient norm from ln1_b, relative to bilinear Frobenius norm\")\nfor l in range(4):\n    scale=np.asarray(W[f\"ln1_w.{l}\"],float)\n    bias=np.asarray(W[f\"ln1_b.{l}\"],float)\n    for h in range(6):\n        s=slice(64*h,64*(h+1))\n        q,k=[np.asarray(W[f\"W_{n}.{l}\"][s],float) for n in [\"q\",\"k\"]]\n        c=P@(scale*(k.T@(q@bias)))\n        print(f\"L{l}H{h}\",round(np.linalg.norm(c),3),\n              round(np.linalg.norm(c)/np.linalg.norm(A[l,h]),3))",
  "out": "L0 per-head V and O singular values (first 35)\n0 V [2.214 2.066 1.611 1.282 1.259 1.034 0.964 0.915 0.843 0.762 0.716 0.68\n 0.652 0.649 0.609 0.606 0.593 0.551 0.54  0.516 0.51  0.498 0.489 0.473\n 0.455 0.451 0.447 0.434 0.427 0.413 0.408 0.404 0.398 0.396 0.386]\n0 Vscaled [1.331 1.075 0.446 0.374 0.304 0.294 0.256 0.247 0.237 0.224 0.218 0.205\n 0.203 0.199 0.188 0.181 0.181 0.174 0.169 0.163 0.162 0.155 0.151 0.147\n 0.146 0.138 0.136 0.132 0.13  0.126 0.125 0.122 0.121 0.118 0.117]\n0 O [3.214 2.015 1.441 1.305 1.153 0.96  0.905 0.839 0.717 0.68  0.572 0.57\n 0.52  0.512 0.486 0.445 0.427 0.394 0.377 0.366 0.358 0.355 0.345 0.338\n 0.313 0.299 0.288 0.281 0.263 0.26  0.248 0.243 0.236 0.228 0.225]\n1 V [2.25  2.127 2.089 2.055 1.991 1.973 1.958 1.933 1.869 1.832 1.808 1.785\n 1.717 1.69  1.639 1.611 1.584 1.554 1.496 1.48  1.419 1.37  1.277 1.193\n 1.14  1.094 1.028 0.906 0.744 0.693 0.682 0.659 0.638 0.587 0.538]\n1 Vscaled [0.481 0.455 0.448 0.429 0.422 0.41  0.403 0.387 0.38  0.374 0.37  0.36\n 0.353 0.341 0.331 0.327 0.322 0.313 0.31  0.303 0.295 0.288 0.281 0.278\n 0.27  0.255 0.253 0.241 0.227 0.216 0.209 0.201 0.198 0.171 0.165]\n1 O [2.342 2.336 2.292 2.187 2.168 2.157 2.117 2.073 2.052 2.021 1.998 1.979\n 1.944 1.931 1.898 1.827 1.781 1.751 1.727 1.654 1.595 1.541 1.442 1.324\n 1.247 1.21  1.125 1.111 1.047 0.963 0.908 0.827 0.755 0.72  0.677]\n2 V [2.164 2.047 2.012 1.723 1.514 1.361 1.273 1.115 1.036 0.993 0.908 0.857\n 0.765 0.74  0.681 0.628 0.618 0.592 0.58  0.57  0.535 0.526 0.512 0.477\n 0.471 0.46  0.458 0.445 0.444 0.433 0.423 0.409 0.408 0.401 0.397]\n2 Vscaled [0.5   0.482 0.463 0.4   0.363 0.338 0.296 0.284 0.268 0.262 0.246 0.223\n 0.217 0.21  0.2   0.191 0.183 0.18  0.177 0.17  0.167 0.159 0.156 0.153\n 0.15  0.147 0.145 0.138 0.135 0.13  0.127 0.124 0.12  0.118 0.115]\n2 O [2.256 2.044 1.778 1.657 1.481 1.38  1.302 1.216 1.101 0.919 0.88  0.785\n 0.738 0.671 0.644 0.59  0.539 0.478 0.446 0.419 0.415 0.387 0.372 0.363\n 0.35  0.335 0.331 0.309 0.276 0.271 0.265 0.25  0.243 0.233 0.224]\n3 V [1.443 1.28  1.237 1.154 1.129 1.076 1.038 1.013 0.958 0.917 0.897 0.841\n 0.816 0.734 0.696 0.67  0.64  0.604 0.581 0.568 0.529 0.519 0.513 0.475\n 0.471 0.464 0.457 0.446 0.432 0.417 0.414 0.404 0.395 0.391 0.386]\n3 Vscaled [0.589 0.386 0.343 0.309 0.294 0.277 0.273 0.247 0.243 0.24  0.23  0.216\n 0.209 0.207 0.196 0.192 0.187 0.178 0.175 0.173 0.168 0.159 0.154 0.151\n 0.149 0.143 0.143 0.141 0.138 0.13  0.128 0.125 0.124 0.121 0.118]\n3 O [1.802 1.7   1.582 1.509 1.378 1.286 1.251 1.225 1.135 1.115 1.044 0.978\n 0.928 0.849 0.837 0.772 0.73  0.677 0.629 0.575 0.543 0.504 0.466 0.463\n 0.406 0.388 0.382 0.358 0.336 0.321 0.313 0.3   0.288 0.28  0.265]\n4 V [4.102 3.344 2.539 2.115 1.785 1.548 1.482 1.422 1.387 1.323 1.196 1.164\n 1.145 1.022 0.967 0.848 0.832 0.801 0.7   0.665 0.644 0.637 0.616 0.566\n 0.56  0.535 0.529 0.494 0.474 0.464 0.447 0.445 0.433 0.427 0.413]\n4 Vscaled [2.899 2.03  1.104 0.577 0.48  0.428 0.379 0.362 0.315 0.311 0.301 0.274\n 0.264 0.262 0.245 0.235 0.218 0.207 0.202 0.194 0.188 0.183 0.177 0.171\n 0.167 0.164 0.161 0.158 0.15  0.144 0.144 0.142 0.136 0.133 0.128]\n4 O [5.589 4.578 2.488 1.978 1.578 1.534 1.499 1.299 1.164 1.007 0.898 0.848\n 0.788 0.704 0.669 0.644 0.558 0.465 0.453 0.418 0.41  0.383 0.362 0.356\n 0.339 0.317 0.31  0.302 0.284 0.277 0.255 0.252 0.248 0.233 0.221]\n5 V [2.397 2.282 2.191 2.148 2.082 2.049 2.033 1.995 1.901 1.88  1.86  1.788\n 1.772 1.744 1.703 1.646 1.591 1.538 1.448 1.384 1.317 1.2   1.042 0.923\n 0.892 0.853 0.817 0.784 0.767 0.744 0.737 0.722 0.709 0.681 0.663]\n5 Vscaled [0.493 0.474 0.447 0.437 0.426 0.408 0.404 0.393 0.386 0.377 0.363 0.361\n 0.357 0.345 0.333 0.329 0.323 0.317 0.31  0.304 0.299 0.289 0.285 0.277\n 0.274 0.268 0.252 0.245 0.242 0.235 0.233 0.221 0.216 0.212 0.208]\n5 O [2.529 2.416 2.372 2.359 2.342 2.325 2.263 2.246 2.235 2.178 2.123 2.056\n 2.049 1.968 1.957 1.84  1.779 1.64  1.567 1.465 1.378 1.28  1.227 1.213\n 1.162 1.142 1.095 1.056 1.036 1.018 1.012 0.949 0.929 0.919 0.897]\n\nSigned key-composition trace: sqrt(d)*tr(A_B B_A)/(||A_B||F ||B_A||F)\nColumns are L0H0..L0H5. Large positive values are a matching-circuit clue, not an attention measurement.\nL1H0 [ 0.127 -0.002 -0.154  0.098  0.406 -0.067]\nL1H1 [-0.091  0.137 -0.316  0.108  0.374  0.302]\nL1H2 [ 0.077  0.013 -0.33   0.179  0.64  -0.053]\nL1H3 [ 0.15  -0.05  -0.258  0.124  0.825  0.243]\nL1H4 [ 0.183  0.043 -0.354  0.223  0.86   0.184]\nL1H5 [-0.382  0.088 -0.263 -0.008 -0.071  0.251]\nL2H0 [ 0.097 -0.012 -0.059  0.153  0.194 -0.09 ]\nL2H1 [0.128 0.028 0.211 0.049 0.074 1.52 ]\nL2H2 [ 0.222 -0.324 -0.106  0.029  0.391 -0.758]\nL2H3 [ 0.348 -0.016 -0.128  0.129  0.775 -0.05 ]\nL2H4 [ 0.156 -0.108 -0.079  0.198  0.481  0.036]\nL2H5 [0.177 0.011 0.002 0.091 0.369 0.134]\nL3H0 [ 0.192 -0.071  0.133  0.151  0.191  0.373]\nL3H1 [0.153 0.139 0.117 0.027 0.081 1.195]\nL3H2 [ 0.121 -0.062  0.03  -0.015  0.254 -0.024]\nL3H3 [0.087 0.062 0.093 0.052 0.143 0.019]\nL3H4 [ 0.167 -0.123  0.11   0.057  0.234  0.198]\nL3H5 [ 0.117 -0.174  0.009  0.052  0.049  1.397]\n\nStrongest signed composition traces over all earlier heads\nL1H0 [('L0H4', np.float64(0.406)), ('L0H0', np.float64(0.127)), ('L0H3', np.float64(0.098)), ('L0H1', np.float64(-0.002))]\nL1H1 [('L0H4', np.float64(0.374)), ('L0H5', np.float64(0.302)), ('L0H1', np.float64(0.137)), ('L0H3', np.float64(0.108))]\nL1H2 [('L0H4', np.float64(0.64)), ('L0H3', np.float64(0.179)), ('L0H0', np.float64(0.077)), ('L0H1', np.float64(0.013))]\nL1H3 [('L0H4', np.float64(0.825)), ('L0H5', np.float64(0.243)), ('L0H0', np.float64(0.15)), ('L0H3', np.float64(0.124))]\nL1H4 [('L0H4', np.float64(0.86)), ('L0H3', np.float64(0.223)), ('L0H5', np.float64(0.184)), ('L0H0', np.float64(0.183))]\nL1H5 [('L0H5', np.float64(0.251)), ('L0H1', np.float64(0.088)), ('L0H3', np.float64(-0.008)), ('L0H4', np.float64(-0.071))]\nL2H0 [('L0H4', np.float64(0.194)), ('L0H3', np.float64(0.153)), ('L1H5', np.float64(0.112)), ('L0H0', np.float64(0.097))]\nL2H1 [('L0H5', np.float64(1.52)), ('L1H3', np.float64(1.127)), ('L1H2', np.float64(0.944)), ('L1H1', np.float64(0.938))]\nL2H2 [('L0H4', np.float64(0.391)), ('L0H0', np.float64(0.222)), ('L0H3', np.float64(0.029)), ('L0H2', np.float64(-0.106))]\nL2H3 [('L0H4', np.float64(0.775)), ('L0H0', np.float64(0.348)), ('L0H3', np.float64(0.129)), ('L1H1', np.float64(0.095))]\nL2H4 [('L0H4', np.float64(0.481)), ('L0H3', np.float64(0.198)), ('L0H0', np.float64(0.156)), ('L0H5', np.float64(0.036))]\nL2H5 [('L0H4', np.float64(0.369)), ('L1H2', np.float64(0.185)), ('L0H0', np.float64(0.177)), ('L1H1', np.float64(0.162))]\nL3H0 [('L2H2', np.float64(0.466)), ('L0H5', np.float64(0.373)), ('L1H2', np.float64(0.198)), ('L0H0', np.float64(0.192))]\nL3H1 [('L2H2', np.float64(2.164)), ('L0H5', np.float64(1.195)), ('L1H4', np.float64(0.894)), ('L1H3', np.float64(0.887))]\nL3H2 [('L0H4', np.float64(0.254)), ('L0H0', np.float64(0.121)), ('L0H2', np.float64(0.03)), ('L1H1', np.float64(0.026))]\nL3H3 [('L2H4', np.float64(0.29)), ('L2H3', np.float64(0.202)), ('L2H2', np.float64(0.177)), ('L1H2', np.float64(0.155))]\nL3H4 [('L0H4', np.float64(0.234)), ('L2H4', np.float64(0.201)), ('L0H5', np.float64(0.198)), ('L0H0', np.float64(0.167))]\nL3H5 [('L2H2', np.float64(3.597)), ('L0H5', np.float64(1.397)), ('L1H3', np.float64(1.338)), ('L1H4', np.float64(1.12))]\n\nL0 QK/V subspace overlap\nL0H0 [(1, np.float64(0.328), np.float64(0.052)), (4, np.float64(0.615), np.float64(0.833)), (5, np.float64(0.408), np.float64(0.07))]\nL0H1 [(1, np.float64(0.126), np.float64(0.13)), (4, np.float64(0.709), np.float64(0.679)), (5, np.float64(0.183), np.float64(0.144))]\nL0H2 [(1, np.float64(0.228), np.float64(0.113)), (4, np.float64(0.693), np.float64(0.778)), (5, np.float64(0.309), np.float64(0.145))]\nL0H3 [(1, np.float64(0.268), np.float64(0.087)), (4, np.float64(0.735), np.float64(0.741)), (5, np.float64(0.269), np.float64(0.136))]\nL0H4 [(1, np.float64(0.121), np.float64(0.101)), (4, np.float64(0.26), np.float64(0.213)), (5, np.float64(0.119), np.float64(0.101))]\nL0H5 [(1, np.float64(0.089), np.float64(0.092)), (4, np.float64(0.432), np.float64(0.414)), (5, np.float64(0.201), np.float64(0.174))]\n\nAffine key-prior coefficient norm from ln1_b, relative to bilinear Frobenius norm\nL0H0 0.148 0.163\nL0H1 0.589 0.084\nL0H2 0.187 0.134\nL0H3 0.456 0.238\nL0H4 0.007 0.1\nL0H5 0.407 0.052\nL1H0 6.713 1.303\nL1H1 25.199 1.988\nL1H2 17.599 1.78\nL1H3 21.755 2.077\nL1H4 20.144 2.016\nL1H5 19.681 1.683\nL2H0 7.156 0.437\nL2H1 5.101 0.328\nL2H2 12.669 0.565\nL2H3 8.295 0.525\nL2H4 9.595 0.545\nL2H5 5.089 0.385\nL3H0 2.389 0.144\nL3H1 2.107 0.155\nL3H2 2.738 0.166\nL3H3 2.067 0.245\nL3H4 2.453 0.177\nL3H5 2.48 0.146\n",
  "seconds": 1.205,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "e74fc3ddad7e61d56e9db6f0f2c346502186a492133f524e6a46568f8641c49f",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
L0 per-head V and O singular values (first 35)
0 V [2.214 2.066 1.611 1.282 1.259 1.034 0.964 0.915 0.843 0.762 0.716 0.68
 0.652 0.649 0.609 0.606 0.593 0.551 0.54  0.516 0.51  0.498 0.489 0.473
 0.455 0.451 0.447 0.434 0.427 0.413 0.408 0.404 0.398 0.396 0.386]
0 Vscaled [1.331 1.075 0.446 0.374 0.304 0.294 0.256 0.247 0.237 0.224 0.218 0.205
 0.203 0.199 0.188 0.181 0.181 0.174 0.169 0.163 0.162 0.155 0.151 0.147
 0.146 0.138 0.136 0.132 0.13  0.126 0.125 0.122 0.121 0.118 0.117]
0 O [3.214 2.015 1.441 1.305 1.153 0.96  0.905 0.839 0.717 0.68  0.572 0.57
 0.52  0.512 0.486 0.445 0.427 0.394 0.377 0.366 0.358 0.355 0.345 0.338
 0.313 0.299 0.288 0.281 0.263 0.26  0.248 0.243 0.236 0.228 0.225]
1 V [2.25  2.127 2.089 2.055 1.991 1.973 1.958 1.933 1.869 1.832 1.808 1.785
 1.717 1.69  1.639 1.611 1.584 1.554 1.496 1.48  1.419 1.37  1.277 1.193
 1.14  1.094 1.028 0.906 0.744 0.693 0.682 0.659 0.638 0.587 0.538]
1 Vscaled [0.481 0.455 0.448 0.429 0.422 0.41  0.403 0.387 0.38  0.374 0.37  0.36
 0.353 0.341 0.331 0.327 0.322 0.313 0.31  0.303 0.295 0.288 0.281 0.278
 0.27  0.255 0.253 0.241 0.227 0.216 0.209 0.201 0.198 0.171 0.165]
1 O [2.342 2.336 2.292 2.187 2.168 2.157 2.117 2.073 2.052 2.021 1.998 1.979
 1.944 1.931 1.898 1.827 1.781 1.751 1.727 1.654 1.595 1.541 1.442 1.324
 1.247 1.21  1.125 1.111 1.047 0.963 0.908 0.827 0.755 0.72  0.677]
2 V [2.164 2.047 2.012 1.723 1.514 1.361 1.273 1.115 1.036 0.993 0.908 0.857
 0.765 0.74  0.681 0.628 0.618 0.592 0.58  0.57  0.535 0.526 0.512 0.477
 0.471 0.46  0.458 0.445 0.444 0.433 0.423 0.409 0.408 0.401 0.397]
2 Vscaled [0.5   0.482 0.463 0.4   0.363 0.338 0.296 0.284 0.268 0.262 0.246 0.223
 0.217 0.21  0.2   0.191 0.183 0.18  0.177 0.17  0.167 0.159 0.156 0.153
 0.15  0.147 0.145 0.138 0.135 0.13  0.127 0.124 0.12  0.118 0.115]
2 O [2.256 2.044 1.778 1.657 1.481 1.38  1.302 1.216 1.101 0.919 0.88  0.785
 0.738 0.671 0.644 0.59  0.539 0.478 0.446 0.419 0.415 0.387 0.372 0.363
 0.35  0.335 0.331 0.309 0.276 0.271 0.265 0.25  0.243 0.233 0.224]
3 V [1.443 1.28  1.237 1.154 1.129 1.076 1.038 1.013 0.958 0.917 0.897 0.841
 0.816 0.734 0.696 0.67  0.64  0.604 0.581 0.568 0.529 0.519 0.513 0.475
 0.471 0.464 0.457 0.446 0.432 0.417 0.414 0.404 0.395 0.391 0.386]
3 Vscaled [0.589 0.386 0.343 0.309 0.294 0.277 0.273 0.247 0.243 0.24  0.23  0.216
 0.209 0.207 0.196 0.192 0.187 0.178 0.175 0.173 0.168 0.159 0.154 0.151
 0.149 0.143 0.143 0.141 0.138 0.13  0.128 0.125 0.124 0.121 0.118]
3 O [1.802 1.7   1.582 1.509 1.378 1.286 1.251 1.225 1.135 1.115 1.044 0.978
 0.928 0.849 0.837 0.772 0.73  0.677 0.629 0.575 0.543 0.504 0.466 0.463
 0.406 0.388 0.382 0.358 0.336 0.321 0.313 0.3   0.288 0.28  0.265]
4 V [4.102 3.344 2.539 2.115 1.785 1.548 1.482 1.422 1.387 1.323 1.196 1.164
 1.145 1.022 0.967 0.848 0.832 0.801 0.7   0.665 0.644 0.637 0.616 0.566
 0.56  0.535 0.529 0.494 0.474 0.464 0.447 0.445 0.433 0.427 0.413]
4 Vscaled [2.899 2.03  1.104 0.577 0.48  0.428 0.379 0.362 0.315 0.311 0.301 0.274
 0.264 0.262 0.245 0.235 0.218 0.207 0.202 0.194 0.188 0.183 0.177 0.171
 0.167 0.164 0.161 0.158 0.15  0.144 0.144 0.142 0.136 0.133 0.128]
4 O [5.589 4.578 2.488 1.978 1.578 1.534 1.499 1.299 1.164 1.007 0.898 0.848
 0.788 0.704 0.669 0.644 0.558 0.465 0.453 0.418 0.41  0.383 0.362 0.356
 0.339 0.317 0.31  0.302 0.284 0.277 0.255 0.252 0.248 0.233 0.221]
5 V [2.397 2.282 2.191 2.148 2.082 2.049 2.033 1.995 1.901 1.88  1.86  1.788
 1.772 1.744 1.703 1.646 1.591 1.538 1.448 1.384 1.317 1.2   1.042 0.923
 0.892 0.853 0.817 0.784 0.767 0.744 0.737 0.722 0.709 0.681 0.663]
5 Vscaled [0.493 0.474 0.447 0.437 0.426 0.408 0.404 0.393 0.386 0.377 0.363 0.361
 0.357 0.345 0.333 0.329 0.323 0.317 0.31  0.304 0.299 0.289 0.285 0.277
 0.274 0.268 0.252 0.245 0.242 0.235 0.233 0.221 0.216 0.212 0.208]
5 O [2.529 2.416 2.372 2.359 2.342 2.325 2.263 2.246 2.235 2.178 2.123 2.056
 2.049 1.968 1.957 1.84  1.779 1.64  1.567 1.465 1.378 1.28  1.227 1.213
 1.162 1.142 1.095 1.056 1.036 1.018 1.012 0.949 0.929 0.919 0.897]

Signed key-composition trace: sqrt(d)*tr(A_B B_A)/(||A_B||F ||B_A||F)
Columns are L0H0..L0H5. Large positive values are a matching-circuit clue, not an attention measurement.
L1H0 [ 0.127 -0.002 -0.154  0.098  0.406 -0.067]
L1H1 [-0.091  0.137 -0.316  0.108  0.374  0.302]
L1H2 [ 0.077  0.013 -0.33   0.179  0.64  -0.053]
L1H3 [ 0.15  -0.05  -0.258  0.124  0.825  0.243]
L1H4 [ 0.183  0.043 -0.354  0.223  0.86   0.184]
L1H5 [-0.382  0.088 -0.263 -0.008 -0.071  0.251]
L2H0 [ 0.097 -0.012 -0.059  0.153  0.194 -0.09 ]
L2H1 [0.128 0.028 0.211 0.049 0.074 1.52 ]
L2H2 [ 0.222 -0.324 -0.106  0.029  0.391 -0.758]
L2H3 [ 0.348 -0.016 -0.128  0.129  0.775 -0.05 ]
L2H4 [ 0.156 -0.108 -0.079  0.198  0.481  0.036]
L2H5 [0.177 0.011 0.002 0.091 0.369 0.134]
L3H0 [ 0.192 -0.071  0.133  0.151  0.191  0.373]
L3H1 [0.153 0.139 0.117 0.027 0.081 1.195]
L3H2 [ 0.121 -0.062  0.03  -0.015  0.254 -0.024]
L3H3 [0.087 0.062 0.093 0.052 0.143 0.019]
L3H4 [ 0.167 -0.123  0.11   0.057  0.234  0.198]
L3H5 [ 0.117 -0.174  0.009  0.052  0.049  1.397]

Strongest signed composition traces over all earlier heads
L1H0 [('L0H4', np.float64(0.406)), ('L0H0', np.float64(0.127)), ('L0H3', np.float64(0.098)), ('L0H1', np.float64(-0.002))]
L1H1 [('L0H4', np.float64(0.374)), ('L0H5', np.float64(0.302)), ('L0H1', np.float64(0.137)), ('L0H3', np.float64(0.108))]
L1H2 [('L0H4', np.float64(0.64)), ('L0H3', np.float64(0.179)), ('L0H0', np.float64(0.077)), ('L0H1', np.float64(0.013))]
L1H3 [('L0H4', np.float64(0.825)), ('L0H5', np.float64(0.243)), ('L0H0', np.float64(0.15)), ('L0H3', np.float64(0.124))]
L1H4 [('L0H4', np.float64(0.86)), ('L0H3', np.float64(0.223)), ('L0H5', np.float64(0.184)), ('L0H0', np.float64(0.183))]
L1H5 [('L0H5', np.float64(0.251)), ('L0H1', np.float64(0.088)), ('L0H3', np.float64(-0.008)), ('L0H4', np.float64(-0.071))]
L2H0 [('L0H4', np.float64(0.194)), ('L0H3', np.float64(0.153)), ('L1H5', np.float64(0.112)), ('L0H0', np.float64(0.097))]
L2H1 [('L0H5', np.float64(1.52)), ('L1H3', np.float64(1.127)), ('L1H2', np.float64(0.944)), ('L1H1', np.float64(0.938))]
L2H2 [('L0H4', np.float64(0.391)), ('L0H0', np.float64(0.222)), ('L0H3', np.float64(0.029)), ('L0H2', np.float64(-0.106))]
L2H3 [('L0H4', np.float64(0.775)), ('L0H0', np.float64(0.348)), ('L0H3', np.float64(0.129)), ('L1H1', np.float64(0.095))]
L2H4 [('L0H4', np.float64(0.481)), ('L0H3', np.float64(0.198)), ('L0H0', np.float64(0.156)), ('L0H5', np.float64(0.036))]
L2H5 [('L0H4', np.float64(0.369)), ('L1H2', np.float64(0.185)), ('L0H0', np.float64(0.177)), ('L1H1', np.float64(0.162))]
L3H0 [('L2H2', np.float64(0.466)), ('L0H5', np.float64(0.373)), ('L1H2', np.float64(0.198)), ('L0H0', np.float64(0.192))]
L3H1 [('L2H2', np.float64(2.164)), ('L0H5', np.float64(1.195)), ('L1H4', np.float64(0.894)), ('L1H3', np.float64(0.887))]
L3H2 [('L0H4', np.float64(0.254)), ('L0H0', np.float64(0.121)), ('L0H2', np.float64(0.03)), ('L1H1', np.float64(0.026))]
L3H3 [('L2H4', np.float64(0.29)), ('L2H3', np.float64(0.202)), ('L2H2', np.float64(0.177)), ('L1H2', np.float64(0.155))]
L3H4 [('L0H4', np.float64(0.234)), ('L2H4', np.float64(0.201)), ('L0H5', np.float64(0.198)), ('L0H0', np.float64(0.167))]
L3H5 [('L2H2', np.float64(3.597)), ('L0H5', np.float64(1.397)), ('L1H3', np.float64(1.338)), ('L1H4', np.float64(1.12))]

L0 QK/V subspace overlap
L0H0 [(1, np.float64(0.328), np.float64(0.052)), (4, np.float64(0.615), np.float64(0.833)), (5, np.float64(0.408), np.float64(0.07))]
L0H1 [(1, np.float64(0.126), np.float64(0.13)), (4, np.float64(0.709), np.float64(0.679)), (5, np.float64(0.183), np.float64(0.144))]
L0H2 [(1, np.float64(0.228), np.float64(0.113)), (4, np.float64(0.693), np.float64(0.778)), (5, np.float64(0.309), np.float64(0.145))]
L0H3 [(1, np.float64(0.268), np.float64(0.087)), (4, np.float64(0.735), np.float64(0.741)), (5, np.float64(0.269), np.float64(0.136))]
L0H4 [(1, np.float64(0.121), np.float64(0.101)), (4, np.float64(0.26), np.float64(0.213)), (5, np.float64(0.119), np.float64(0.101))]
L0H5 [(1, np.float64(0.089), np.float64(0.092)), (4, np.float64(0.432), np.float64(0.414)), (5, np.float64(0.201), np.float64(0.174))]

Affine key-prior coefficient norm from ln1_b, relative to bilinear Frobenius norm
L0H0 0.148 0.163
L0H1 0.589 0.084
L0H2 0.187 0.134
L0H3 0.456 0.238
L0H4 0.007 0.1
L0H5 0.407 0.052
L1H0 6.713 1.303
L1H1 25.199 1.988
L1H2 17.599 1.78
L1H3 21.755 2.077
L1H4 20.144 2.016
L1H5 19.681 1.683
L2H0 7.156 0.437
L2H1 5.101 0.328
L2H2 12.669 0.565
L2H3 8.295 0.525
L2H4 9.595 0.545
L2H5 5.089 0.385
L3H0 2.389 0.144
L3H1 2.107 0.155
L3H2 2.738 0.166
L3H3 2.067 0.245
L3H4 2.453 0.177
L3H5 2.48 0.146


========================================================================
TURN t004
MESSAGE SENT TO CODEX
OUTPUT of run 3 of at most 100:
```
L0 per-head V and O singular values (first 35)
0 V [2.214 2.066 1.611 1.282 1.259 1.034 0.964 0.915 0.843 0.762 0.716 0.68
 0.652 0.649 0.609 0.606 0.593 0.551 0.54  0.516 0.51  0.498 0.489 0.473
 0.455 0.451 0.447 0.434 0.427 0.413 0.408 0.404 0.398 0.396 0.386]
0 Vscaled [1.331 1.075 0.446 0.374 0.304 0.294 0.256 0.247 0.237 0.224 0.218 0.205
 0.203 0.199 0.188 0.181 0.181 0.174 0.169 0.163 0.162 0.155 0.151 0.147
 0.146 0.138 0.136 0.132 0.13  0.126 0.125 0.122 0.121 0.118 0.117]
0 O [3.214 2.015 1.441 1.305 1.153 0.96  0.905 0.839 0.717 0.68  0.572 0.57
 0.52  0.512 0.486 0.445 0.427 0.394 0.377 0.366 0.358 0.355 0.345 0.338
 0.313 0.299 0.288 0.281 0.263 0.26  0.248 0.243 0.236 0.228 0.225]
1 V [2.25  2.127 2.089 2.055 1.991 1.973 1.958 1.933 1.869 1.832 1.808 1.785
 1.717 1.69  1.639 1.611 1.584 1.554 1.496 1.48  1.419 1.37  1.277 1.193
 1.14  1.094 1.028 0.906 0.744 0.693 0.682 0.659 0.638 0.587 0.538]
1 Vscaled [0.481 0.455 0.448 0.429 0.422 0.41  0.403 0.387 0.38  0.374 0.37  0.36
 0.353 0.341 0.331 0.327 0.322 0.313 0.31  0.303 0.295 0.288 0.281 0.278
 0.27  0.255 0.253 0.241 0.227 0.216 0.209 0.201 0.198 0.171 0.165]
1 O [2.342 2.336 2.292 2.187 2.168 2.157 2.117 2.073 2.052 2.021 1.998 1.979
 1.944 1.931 1.898 1.827 1.781 1.751 1.727 1.654 1.595 1.541 1.442 1.324
 1.247 1.21  1.125 1.111 1.047 0.963 0.908 0.827 0.755 0.72  0.677]
2 V [2.164 2.047 2.012 1.723 1.514 1.361 1.273 1.115 1.036 0.993 0.908 0.857
 0.765 0.74  0.681 0.628 0.618 0.592 0.58  0.57  0.535 0.526 0.512 0.477
 0.471 0.46  0.458 0.445 0.444 0.433 0.423 0.409 0.408 0.401 0.397]
2 Vscaled [0.5   0.482 0.463 0.4   0.363 0.338 0.296 0.284 0.268 0.262 0.246 0.223
 0.217 0.21  0.2   0.191 0.183 0.18  0.177 0.17  0.167 0.159 0.156 0.153
 0.15  0.147 0.145 0.138 0.135 0.13  0.127 0.124 0.12  0.118 0.115]
2 O [2.256 2.044 1.778 1.657 1.481 1.38  1.302 1.216 1.101 0.919 0.88  0.785
 0.738 0.671 0.644 0.59  0.539 0.478 0.446 0.419 0.415 0.387 0.372 0.363
 0.35  0.335 0.331 0.309 0.276 0.271 0.265 0.25  0.243 0.233 0.224]
3 V [1.443 1.28  1.237 1.154 1.129 1.076 1.038 1.013 0.958 0.917 0.897 0.841
 0.816 0.734 0.696 0.67  0.64  0.604 0.581 0.568 0.529 0.519 0.513 0.475
 0.471 0.464 0.457 0.446 0.432 0.417 0.414 0.404 0.395 0.391 0.386]
3 Vscaled [0.589 0.386 0.343 0.309 0.294 0.277 0.273 0.247 0.243 0.24  0.23  0.216
 0.209 0.207 0.196 0.192 0.187 0.178 0.175 0.173 0.168 0.159 0.154 0.151
 0.149 0.143 0.143 0.141 0.138 0.13  0.128 0.125 0.124 0.121 0.118]
3 O [1.802 1.7   1.582 1.509 1.378 1.286 1.251 1.225 1.135 1.115 1.044 0.978
 0.928 0.849 0.837 0.772 0.73  0.677 0.629 0.575 0.543 0.504 0.466 0.463
 0.406 0.388 0.382 0.358 0.336 0.321 0.313 0.3   0.288 0.28  0.265]
4 V [4.102 3.344 2.539 2.115 1.785 1.548 1.482 1.422 1.387 1.323 1.196 1.164
 1.145 1.022 0.967 0.848 0.832 0.801 0.7   0.665 0.644 0.637 0.616 0.566
 0.56  0.535 0.529 0.494 0.474 0.464 0.447 0.445 0.433 0.427 0.413]
4 Vscaled [2.899 2.03  1.104 0.577 0.48  0.428 0.379 0.362 0.315 0.311 0.301 0.274
 0.264 0.262 0.245 0.235 0.218 0.207 0.202 0.194 0.188 0.183 0.177 0.171
 0.167 0.164 0.161 0.158 0.15  0.144 0.144 0.142 0.136 0.133 0.128]
4 O [5.589 4.578 2.488 1.978 1.578 1.534 1.499 1.299 1.164 1.007 0.898 0.848
 0.788 0.704 0.669 0.644 0.558 0.465 0.453 0.418 0.41  0.383 0.362 0.356
 0.339 0.317 0.31  0.302 0.284 0.277 0.255 0.252 0.248 0.233 0.221]
5 V [2.397 2.282 2.191 2.148 2.082 2.049 2.033 1.995 1.901 1.88  1.86  1.788
 1.772 1.744 1.703 1.646 1.591 1.538 1.448 1.384 1.317 1.2   1.042 0.923
 0.892 0.853 0.817 0.784 0.767 0.744 0.737 0.722 0.709 0.681 0.663]
5 Vscaled [0.493 0.474 0.447 0.437 0.426 0.408 0.404 0.393 0.386 0.377 0.363 0.361
 0.357 0.345 0.333 0.329 0.323 0.317 0.31  0.304 0.299 0.289 0.285 0.277
 0.274 0.268 0.252 0.245 0.242 0.235 0.233 0.221 0.216 0.212 0.208]
5 O [2.529 2.416 2.372 2.359 2.342 2.325 2.263 2.246 2.235 2.178 2.123 2.056
 2.049 1.968 1.957 1.84  1.779 1.64  1.567 1.465 1.378 1.28  1.227 1.213
 1.162 1.142 1.095 1.056 1.036 1.018 1.012 0.949 0.929 0.919 0.897]

Signed key-composition trace: sqrt(d)*tr(A_B B_A)/(||A_B||F ||B_A||F)
Columns are L0H0..L0H5. Large positive values are a matching-circuit clue, not an attention measurement.
L1H0 [ 0.127 -0.002 -0.154  0.098  0.406 -0.067]
L1H1 [-0.091  0.137 -0.316  0.108  0.374  0.302]
L1H2 [ 0.077  0.013 -0.33   0.179  0.64  -0.053]
L1H3 [ 0.15  -0.05  -0.258  0.124  0.825  0.243]
L1H4 [ 0.183  0.043 -0.354  0.223  0.86   0.184]
L1H5 [-0.382  0.088 -0.263 -0.008 -0.071  0.251]
L2H0 [ 0.097 -0.012 -0.059  0.153  0.194 -0.09 ]
L2H1 [0.128 0.028 0.211 0.049 0.074 1.52 ]
L2H2 [ 0.222 -0.324 -0.106  0.029  0.391 -0.758]
L2H3 [ 0.348 -0.016 -0.128  0.129  0.775 -0.05 ]
L2H4 [ 0.156 -0.108 -0.079  0.198  0.481  0.036]
L2H5 [0.177 0.011 0.002 0.091 0.369 0.134]
L3H0 [ 0.192 -0.071  0.133  0.151  0.191  0.373]
L3H1 [0.153 0.139 0.117 0.027 0.081 1.195]
L3H2 [ 0.121 -0.062  0.03  -0.015  0.254 -0.024]
L3H3 [0.087 0.062 0.093 0.052 0.143 0.019]
L3H4 [ 0.167 -0.123  0.11   0.057  0.234  0.198]
L3H5 [ 0.117 -0.174  0.009  0.052  0.049  1.397]

Strongest signed composition traces over all earlier heads
L1H0 [('L0H4', np.float64(0.406)), ('L0H0', np.float64(0.127)), ('L0H3', np.float64(0.098)), ('L0H1', np.float64(-0.002))]
L1H1 [('L0H4', np.float64(0.374)), ('L0H5', np.float64(0.302)), ('L0H1', np.float64(0.137)), ('L0H3', np.float64(0.108))]
L1H2 [('L0H4', np.float64(0.64)), ('L0H3', np.float64(0.179)), ('L0H0', np.float64(0.077)), ('L0H1', np.float64(0.013))]
L1H3 [('L0H4', np.float64(0.825)), ('L0H5', np.float64(0.243)), ('L0H0', np.float64(0.15)), ('L0H3', np.float64(0.124))]
L1H4 [('L0H4', np.float64(0.86)), ('L0H3', np.float64(0.223)), ('L0H5', np.float64(0.184)), ('L0H0', np.float64(0.183))]
L1H5 [('L0H5', np.float64(0.251)), ('L0H1', np.float64(0.088)), ('L0H3', np.float64(-0.008)), ('L0H4', np.float64(-0.071))]
L2H0 [('L0H4', np.float64(0.194)), ('L0H3', np.float64(0.153)), ('L1H5', np.float64(0.112)), ('L0H0', np.float64(0.097))]
L2H1 [('L0H5', np.float64(1.52)), ('L1H3', np.float64(1.127)), ('L1H2', np.float64(0.944)), ('L1H1', np.float64(0.938))]
L2H2 [('L0H4', np.float64(0.391)), ('L0H0', np.float64(0.222)), ('L0H3', np.float64(0.029)), ('L0H2', np.float64(-0.106))]
L2H3 [('L0H4', np.float64(0.775)), ('L0H0', np.float64(0.348)), ('L0H3', np.float64(0.129)), ('L1H1', np.float64(0.095))]
L2H4 [('L0H4', np.float64(0.481)), ('L0H3', np.float64(0.198)), ('L0H0', np.float64(0.156)), ('L0H5', np.float64(0.036))]
L2H5 [('L0H4', np.float64(0.369)), ('L1H2', np.float64(0.185)), ('L0H0', np.float64(0.177)), ('L1H1', np.float64(0.162))]
L3H0 [('L2H2', np.float64(0.466)), ('L0H5', np.float64(0.373)), ('L1H2', np.float64(0.198)), ('L0H0', np.float64(0.192))]
L3H1 [('L2H2', np.float64(2.164)), ('L0H5', np.float64(1.195)), ('L1H4', np.float64(0.894)), ('L1H3', np.float64(0.887))]
L3H2 [('L0H4', np.float64(0.254)), ('L0H0', np.float64(0.121)), ('L0H2', np.float64(0.03)), ('L1H1', np.float64(0.026))]
L3H3 [('L2H4', np.float64(0.29)), ('L2H3', np.float64(0.202)), ('L2H2', np.float64(0.177)), ('L1H2', np.float64(0.155))]
L3H4 [('L0H4', np.float64(0.234)), ('L2H4', np.float64(0.201)), ('L0H5', np.float64(0.198)), ('L0H0', np.float64(0.167))]
L3H5 [('L2H2', np.float64(3.597)), ('L0H5', np.float64(1.397)), ('L1H3', np.float64(1.338)), ('L1H4', np.float64(1.12))]

L0 QK/V subspace overlap
L0H0 [(1, np.float64(0.328), np.float64(0.052)), (4, np.float64(0.615), np.float64(0.833)), (5, np.float64(0.408), np.float64(0.07))]
L0H1 [(1, np.float64(0.126), np.float64(0.13)), (4, np.float64(0.709), np.float64(0.679)), (5, np.float64(0.183), np.float64(0.144))]
L0H2 [(1, np.float64(0.228), np.float64(0.113)), (4, np.float64(0.693), np.float64(0.778)), (5, np.float64(0.309), np.float64(0.145))]
L0H3 [(1, np.float64(0.268), np.float64(0.087)), (4, np.float64(0.735), np.float64(0.741)), (5, np.float64(0.269), np.float64(0.136))]
L0H4 [(1, np.float64(0.121), np.float64(0.101)), (4, np.float64(0.26), np.float64(0.213)), (5, np.float64(0.119), np.float64(0.101))]
L0H5 [(1, np.float64(0.089), np.float64(0.092)), (4, np.float64(0.432), np.float64(0.414)), (5, np.float64(0.201), np.float64(0.174))]

Affine key-prior coefficient norm from ln1_b, relative to bilinear Frobenius norm
L0H0 0.148 0.163
L0H1 0.589 0.084
L0H2 0.187 0.134
L0H3 0.456 0.238
L0H4 0.007 0.1
L0H5 0.407 0.052
L1H0 6.713 1.303
L1H1 25.199 1.988
L1H2 17.599 1.78
L1H3 21.755 2.077
L1H4 20.144 2.016
L1H5 19.681 1.683
L2H0 7.156 0.437
L2H1 5.101 0.328
L2H2 12.669 0.565
L2H3 8.295 0.525
L2H4 9.595 0.545
L2H5 5.089 0.385
L3H0 2.389 0.144
L3H1 2.107 0.155
L3H2 2.738 0.166
L3H3 2.067 0.245
L3H4 2.453 0.177
L3H5 2.48 0.146

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd, svdvals
from scipy.stats import kurtosis, skew
from sklearn.decomposition import FastICA
from sklearn.cluster import KMeans
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d=384
P=np.eye(d)-np.ones((d,d))/d
v1=np.asarray(W["W_v.0"][64:128],float)@P
v5=np.asarray(W["W_v.0"][320:384],float)@P
_,s1,r1=svd(v1,full_matrices=False)
_,s5,r5=svd(v5,full_matrices=False)
print("Principal cosines between top-28 L0H1 and L0H5 value row spaces:")
print(np.round(svdvals(r1[:28]@r5[:28].T),3))

# Consensus directions read by both value matrices.
G=r1[:28].T@r1[:28]+r5[:28].T@r5[:28]
e,u=np.linalg.eigh(G)
u=u[:,np.argsort(e)[::-1][:28]]
print("Consensus eigenvalues:",np.round(np.sort(e)[::-1][:35],3))

print("\nL0 QK energy inside the consensus content subspace (unscaled matrices)")
for h in range(6):
    s=slice(64*h,64*(h+1))
    a=np.asarray(W["W_q.0"][s],float).T@np.asarray(W["W_k.0"][s],float)
    print(h,"query",round(np.linalg.norm(u.T@a)**2/np.linalg.norm(a)**2,3),
          "key",round(np.linalg.norm(a@u)**2/np.linalg.norm(a)**2,3))

print("\nICA of final MLP output WEIGHT columns, not activations")
Y=np.asarray(W["W_proj.3"],float).T
ica=FastICA(n_components=28,whiten="unit-variance",random_state=17,
            max_iter=2000,tol=0.0005)
Z=ica.fit_transform(Y)
ku=kurtosis(Z,axis=0,fisher=True)
sk=skew(Z,axis=0)
print("Sorted component kurtoses:",np.round(np.sort(ku)[::-1],2))
print("Sorted absolute component skewness:",np.round(np.sort(np.abs(sk))[::-1],2))
mix=ica.mixing_
# Report component directions' relation to the initial value-read subspace.
frac=np.sum((u.T@mix)**2,axis=0)/np.sum(mix**2,axis=0)
print("Output component overlap with initial content subspace:",np.round(np.sort(frac)[::-1],3))
print("Component norms:",np.round(np.sort(np.linalg.norm(mix,axis=0))[::-1],3))

print("\nInitial MLP input-weight geometry in consensus content coordinates")
F=np.asarray(W["W_fc.0"],float)*W["ln2_w.0"][None,:]
X=F@u
ica2=FastICA(n_components=28,whiten="unit-variance",random_state=17,
             max_iter=2000,tol=0.0005)
Z2=ica2.fit_transform(X)
print("Sorted component kurtoses:",np.round(np.sort(kurtosis(Z2,axis=0))[::-1],2))
print("Sorted absolute component skewness:",np.round(np.sort(np.abs(skew(Z2,axis=0)))[::-1],2))

print("\nFinal MLP output-column directional clustering")
norm=np.linalg.norm(Y,axis=1)
Ysmall=Y[norm>=np.quantile(norm,.5)]
U,S,VT=svd(Ysmall-Ysmall.mean(0),full_matrices=False)
X=U[:,:30]*S[:30]
X=X/np.linalg.norm(X,axis=1,keepdims=True)
for k in [20,24,28,32,40]:
    km=KMeans(n_clusters=k,n_init=10,random_state=17).fit(X)
    sizes=np.bincount(km.labels_,minlength=k)
    print("k",k,"inertia",round(km.inertia_,2),
          "cluster sizes",np.sort(sizes)[::-1].tolist())
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 4,
  "turn": "t004",
  "code": "import numpy as np\nfrom scipy.linalg import svd, svdvals\nfrom scipy.stats import kurtosis, skew\nfrom sklearn.decomposition import FastICA\nfrom sklearn.cluster import KMeans\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nv1=np.asarray(W[\"W_v.0\"][64:128],float)@P\nv5=np.asarray(W[\"W_v.0\"][320:384],float)@P\n_,s1,r1=svd(v1,full_matrices=False)\n_,s5,r5=svd(v5,full_matrices=False)\nprint(\"Principal cosines between top-28 L0H1 and L0H5 value row spaces:\")\nprint(np.round(svdvals(r1[:28]@r5[:28].T),3))\n\n# Consensus directions read by both value matrices.\nG=r1[:28].T@r1[:28]+r5[:28].T@r5[:28]\ne,u=np.linalg.eigh(G)\nu=u[:,np.argsort(e)[::-1][:28]]\nprint(\"Consensus eigenvalues:\",np.round(np.sort(e)[::-1][:35],3))\n\nprint(\"\\nL0 QK energy inside the consensus content subspace (unscaled matrices)\")\nfor h in range(6):\n    s=slice(64*h,64*(h+1))\n    a=np.asarray(W[\"W_q.0\"][s],float).T@np.asarray(W[\"W_k.0\"][s],float)\n    print(h,\"query\",round(np.linalg.norm(u.T@a)**2/np.linalg.norm(a)**2,3),\n          \"key\",round(np.linalg.norm(a@u)**2/np.linalg.norm(a)**2,3))\n\nprint(\"\\nICA of final MLP output WEIGHT columns, not activations\")\nY=np.asarray(W[\"W_proj.3\"],float).T\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,\n            max_iter=2000,tol=0.0005)\nZ=ica.fit_transform(Y)\nku=kurtosis(Z,axis=0,fisher=True)\nsk=skew(Z,axis=0)\nprint(\"Sorted component kurtoses:\",np.round(np.sort(ku)[::-1],2))\nprint(\"Sorted absolute component skewness:\",np.round(np.sort(np.abs(sk))[::-1],2))\nmix=ica.mixing_\n# Report component directions' relation to the initial value-read subspace.\nfrac=np.sum((u.T@mix)**2,axis=0)/np.sum(mix**2,axis=0)\nprint(\"Output component overlap with initial content subspace:\",np.round(np.sort(frac)[::-1],3))\nprint(\"Component norms:\",np.round(np.sort(np.linalg.norm(mix,axis=0))[::-1],3))\n\nprint(\"\\nInitial MLP input-weight geometry in consensus content coordinates\")\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nX=F@u\nica2=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,\n             max_iter=2000,tol=0.0005)\nZ2=ica2.fit_transform(X)\nprint(\"Sorted component kurtoses:\",np.round(np.sort(kurtosis(Z2,axis=0))[::-1],2))\nprint(\"Sorted absolute component skewness:\",np.round(np.sort(np.abs(skew(Z2,axis=0)))[::-1],2))\n\nprint(\"\\nFinal MLP output-column directional clustering\")\nnorm=np.linalg.norm(Y,axis=1)\nYsmall=Y[norm>=np.quantile(norm,.5)]\nU,S,VT=svd(Ysmall-Ysmall.mean(0),full_matrices=False)\nX=U[:,:30]*S[:30]\nX=X/np.linalg.norm(X,axis=1,keepdims=True)\nfor k in [20,24,28,32,40]:\n    km=KMeans(n_clusters=k,n_init=10,random_state=17).fit(X)\n    sizes=np.bincount(km.labels_,minlength=k)\n    print(\"k\",k,\"inertia\",round(km.inertia_,2),\n          \"cluster sizes\",np.sort(sizes)[::-1].tolist())",
  "out": "Principal cosines between top-28 L0H1 and L0H5 value row spaces:\n[0.982 0.98  0.978 0.978 0.975 0.972 0.972 0.969 0.966 0.964 0.961 0.955\n 0.952 0.95  0.944 0.937 0.931 0.922 0.918 0.907 0.887 0.847 0.821 0.695\n 0.62  0.504 0.356 0.169]\nConsensus eigenvalues: [1.982 1.98  1.978 1.978 1.975 1.972 1.972 1.969 1.966 1.964 1.961 1.955\n 1.952 1.95  1.944 1.937 1.931 1.922 1.918 1.907 1.887 1.847 1.821 1.695\n 1.62  1.504 1.356 1.169 0.831 0.644 0.496 0.38  0.305 0.179 0.153]\n\nL0 QK energy inside the consensus content subspace (unscaled matrices)\n0 query 0.077 key 0.441\n1 query 0.204 key 0.198\n2 query 0.429 key 0.599\n3 query 0.201 key 0.472\n4 query 0.054 key 0.108\n5 query 0.075 key 0.078\n\nICA of final MLP output WEIGHT columns, not activations\nSorted component kurtoses: [25.01  1.46  1.34  1.3   1.27  1.23  1.17  1.13  1.13  1.1   1.08  1.05\n  1.03  1.02  1.    0.97  0.97  0.96  0.86  0.58  0.5   0.49  0.43  0.41\n -0.24 -0.4  -0.52 -0.56]\nSorted absolute component skewness: [0.68 0.4  0.38 0.24 0.24 0.2  0.18 0.18 0.17 0.16 0.14 0.13 0.12 0.11\n 0.1  0.07 0.06 0.06 0.06 0.05 0.05 0.04 0.04 0.03 0.03 0.03 0.01 0.01]\nOutput component overlap with initial content subspace: [0.12  0.115 0.1   0.099 0.097 0.097 0.096 0.091 0.09  0.09  0.087 0.085\n 0.082 0.076 0.075 0.075 0.071 0.071 0.07  0.068 0.068 0.063 0.062 0.056\n 0.054 0.053 0.053 0.049]\nComponent norms: [0.524 0.485 0.476 0.473 0.425 0.424 0.422 0.414 0.409 0.402 0.402 0.4\n 0.398 0.391 0.39  0.375 0.374 0.372 0.363 0.355 0.353 0.351 0.346 0.344\n 0.342 0.337 0.33  0.326]\n\nInitial MLP input-weight geometry in consensus content coordinates\nSorted component kurtoses: [376.31 261.48 223.02 197.54 171.95 166.83 162.4  153.1  141.93 129.79\n 121.49 105.13  82.7   79.19  78.52  75.73  73.28  69.47  65.69  59.39\n  53.58  49.19  43.57  43.35  36.13  30.05  26.73  15.29]\nSorted absolute component skewness: [16.48 13.18 12.66 11.22 10.92 10.71 10.65 10.27  9.82  9.82  8.55  7.85\n  7.07  6.9   6.6   6.49  6.47  5.62  5.54  5.28  5.21  5.18  4.62  3.2\n  3.01  2.94  1.23  0.34]\n\nFinal MLP output-column directional clustering\nk 20 inertia 543.63 cluster sizes [52, 48, 46, 45, 41, 39, 39, 38, 38, 37, 37, 37, 36, 36, 36, 36, 34, 34, 30, 29]\nk 24 inertia 530.88 cluster sizes [43, 41, 38, 35, 35, 35, 35, 34, 33, 32, 32, 32, 32, 32, 31, 31, 31, 29, 28, 28, 27, 25, 25, 24]\nk 28 inertia 517.56 cluster sizes [59, 42, 38, 33, 32, 30, 30, 29, 28, 28, 27, 27, 27, 27, 27, 26, 25, 25, 24, 24, 24, 23, 21, 19, 19, 19, 18, 17]\nk 32 inertia 507.03 cluster sizes [37, 34, 32, 32, 31, 30, 29, 28, 27, 26, 26, 25, 25, 25, 25, 24, 24, 24, 23, 23, 22, 21, 21, 21, 21, 19, 18, 17, 16, 14, 14, 14]\nk 40 inertia 488.43 cluster sizes [35, 35, 28, 24, 24, 24, 24, 23, 22, 22, 22, 22, 22, 21, 21, 21, 21, 20, 20, 20, 19, 19, 19, 19, 18, 17, 16, 15, 15, 15, 15, 14, 14, 14, 12, 12, 11, 11, 11, 11]\n\n[stderr]\n/env/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:132: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n  warnings.warn(\n",
  "seconds": 2.793,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "2143a71d313da37b11d57acbf23d8e1b1b70c7a9b1e4503a4e14f047606627e1",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
Principal cosines between top-28 L0H1 and L0H5 value row spaces:
[0.982 0.98  0.978 0.978 0.975 0.972 0.972 0.969 0.966 0.964 0.961 0.955
 0.952 0.95  0.944 0.937 0.931 0.922 0.918 0.907 0.887 0.847 0.821 0.695
 0.62  0.504 0.356 0.169]
Consensus eigenvalues: [1.982 1.98  1.978 1.978 1.975 1.972 1.972 1.969 1.966 1.964 1.961 1.955
 1.952 1.95  1.944 1.937 1.931 1.922 1.918 1.907 1.887 1.847 1.821 1.695
 1.62  1.504 1.356 1.169 0.831 0.644 0.496 0.38  0.305 0.179 0.153]

L0 QK energy inside the consensus content subspace (unscaled matrices)
0 query 0.077 key 0.441
1 query 0.204 key 0.198
2 query 0.429 key 0.599
3 query 0.201 key 0.472
4 query 0.054 key 0.108
5 query 0.075 key 0.078

ICA of final MLP output WEIGHT columns, not activations
Sorted component kurtoses: [25.01  1.46  1.34  1.3   1.27  1.23  1.17  1.13  1.13  1.1   1.08  1.05
  1.03  1.02  1.    0.97  0.97  0.96  0.86  0.58  0.5   0.49  0.43  0.41
 -0.24 -0.4  -0.52 -0.56]
Sorted absolute component skewness: [0.68 0.4  0.38 0.24 0.24 0.2  0.18 0.18 0.17 0.16 0.14 0.13 0.12 0.11
 0.1  0.07 0.06 0.06 0.06 0.05 0.05 0.04 0.04 0.03 0.03 0.03 0.01 0.01]
Output component overlap with initial content subspace: [0.12  0.115 0.1   0.099 0.097 0.097 0.096 0.091 0.09  0.09  0.087 0.085
 0.082 0.076 0.075 0.075 0.071 0.071 0.07  0.068 0.068 0.063 0.062 0.056
 0.054 0.053 0.053 0.049]
Component norms: [0.524 0.485 0.476 0.473 0.425 0.424 0.422 0.414 0.409 0.402 0.402 0.4
 0.398 0.391 0.39  0.375 0.374 0.372 0.363 0.355 0.353 0.351 0.346 0.344
 0.342 0.337 0.33  0.326]

Initial MLP input-weight geometry in consensus content coordinates
Sorted component kurtoses: [376.31 261.48 223.02 197.54 171.95 166.83 162.4  153.1  141.93 129.79
 121.49 105.13  82.7   79.19  78.52  75.73  73.28  69.47  65.69  59.39
  53.58  49.19  43.57  43.35  36.13  30.05  26.73  15.29]
Sorted absolute component skewness: [16.48 13.18 12.66 11.22 10.92 10.71 10.65 10.27  9.82  9.82  8.55  7.85
  7.07  6.9   6.6   6.49  6.47  5.62  5.54  5.28  5.21  5.18  4.62  3.2
  3.01  2.94  1.23  0.34]

Final MLP output-column directional clustering
k 20 inertia 543.63 cluster sizes [52, 48, 46, 45, 41, 39, 39, 38, 38, 37, 37, 37, 36, 36, 36, 36, 34, 34, 30, 29]
k 24 inertia 530.88 cluster sizes [43, 41, 38, 35, 35, 35, 35, 34, 33, 32, 32, 32, 32, 32, 31, 31, 31, 29, 28, 28, 27, 25, 25, 24]
k 28 inertia 517.56 cluster sizes [59, 42, 38, 33, 32, 30, 30, 29, 28, 28, 27, 27, 27, 27, 27, 26, 25, 25, 24, 24, 24, 23, 21, 19, 19, 19, 18, 17]
k 32 inertia 507.03 cluster sizes [37, 34, 32, 32, 31, 30, 29, 28, 27, 26, 26, 25, 25, 25, 25, 24, 24, 24, 23, 23, 22, 21, 21, 21, 21, 19, 18, 17, 16, 14, 14, 14]
k 40 inertia 488.43 cluster sizes [35, 35, 28, 24, 24, 24, 24, 23, 22, 22, 22, 22, 22, 21, 21, 21, 21, 20, 20, 20, 19, 19, 19, 19, 18, 17, 16, 15, 15, 15, 15, 14, 14, 14, 12, 12, 11, 11, 11, 11]

[stderr]
/env/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:132: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
  warnings.warn(


========================================================================
TURN t005
MESSAGE SENT TO CODEX
OUTPUT of run 4 of at most 100:
```
Principal cosines between top-28 L0H1 and L0H5 value row spaces:
[0.982 0.98  0.978 0.978 0.975 0.972 0.972 0.969 0.966 0.964 0.961 0.955
 0.952 0.95  0.944 0.937 0.931 0.922 0.918 0.907 0.887 0.847 0.821 0.695
 0.62  0.504 0.356 0.169]
Consensus eigenvalues: [1.982 1.98  1.978 1.978 1.975 1.972 1.972 1.969 1.966 1.964 1.961 1.955
 1.952 1.95  1.944 1.937 1.931 1.922 1.918 1.907 1.887 1.847 1.821 1.695
 1.62  1.504 1.356 1.169 0.831 0.644 0.496 0.38  0.305 0.179 0.153]

L0 QK energy inside the consensus content subspace (unscaled matrices)
0 query 0.077 key 0.441
1 query 0.204 key 0.198
2 query 0.429 key 0.599
3 query 0.201 key 0.472
4 query 0.054 key 0.108
5 query 0.075 key 0.078

ICA of final MLP output WEIGHT columns, not activations
Sorted component kurtoses: [25.01  1.46  1.34  1.3   1.27  1.23  1.17  1.13  1.13  1.1   1.08  1.05
  1.03  1.02  1.    0.97  0.97  0.96  0.86  0.58  0.5   0.49  0.43  0.41
 -0.24 -0.4  -0.52 -0.56]
Sorted absolute component skewness: [0.68 0.4  0.38 0.24 0.24 0.2  0.18 0.18 0.17 0.16 0.14 0.13 0.12 0.11
 0.1  0.07 0.06 0.06 0.06 0.05 0.05 0.04 0.04 0.03 0.03 0.03 0.01 0.01]
Output component overlap with initial content subspace: [0.12  0.115 0.1   0.099 0.097 0.097 0.096 0.091 0.09  0.09  0.087 0.085
 0.082 0.076 0.075 0.075 0.071 0.071 0.07  0.068 0.068 0.063 0.062 0.056
 0.054 0.053 0.053 0.049]
Component norms: [0.524 0.485 0.476 0.473 0.425 0.424 0.422 0.414 0.409 0.402 0.402 0.4
 0.398 0.391 0.39  0.375 0.374 0.372 0.363 0.355 0.353 0.351 0.346 0.344
 0.342 0.337 0.33  0.326]

Initial MLP input-weight geometry in consensus content coordinates
Sorted component kurtoses: [376.31 261.48 223.02 197.54 171.95 166.83 162.4  153.1  141.93 129.79
 121.49 105.13  82.7   79.19  78.52  75.73  73.28  69.47  65.69  59.39
  53.58  49.19  43.57  43.35  36.13  30.05  26.73  15.29]
Sorted absolute component skewness: [16.48 13.18 12.66 11.22 10.92 10.71 10.65 10.27  9.82  9.82  8.55  7.85
  7.07  6.9   6.6   6.49  6.47  5.62  5.54  5.28  5.21  5.18  4.62  3.2
  3.01  2.94  1.23  0.34]

Final MLP output-column directional clustering
k 20 inertia 543.63 cluster sizes [52, 48, 46, 45, 41, 39, 39, 38, 38, 37, 37, 37, 36, 36, 36, 36, 34, 34, 30, 29]
k 24 inertia 530.88 cluster sizes [43, 41, 38, 35, 35, 35, 35, 34, 33, 32, 32, 32, 32, 32, 31, 31, 31, 29, 28, 28, 27, 25, 25, 24]
k 28 inertia 517.56 cluster sizes [59, 42, 38, 33, 32, 30, 30, 29, 28, 28, 27, 27, 27, 27, 27, 26, 25, 25, 24, 24, 24, 23, 21, 19, 19, 19, 18, 17]
k 32 inertia 507.03 cluster sizes [37, 34, 32, 32, 31, 30, 29, 28, 27, 26, 26, 25, 25, 25, 25, 24, 24, 24, 23, 23, 22, 21, 21, 21, 21, 19, 18, 17, 16, 14, 14, 14]
k 40 inertia 488.43 cluster sizes [35, 35, 28, 24, 24, 24, 24, 23, 22, 22, 22, 22, 22, 21, 21, 21, 21, 20, 20, 20, 19, 19, 19, 19, 18, 17, 16, 15, 15, 15, 15, 14, 14, 14, 12, 12, 11, 11, 11, 11]

[stderr]
/env/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:132: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
  warnings.warn(

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd
from scipy.stats import skew, kurtosis
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d=384
P=np.eye(d)-np.ones((d,d))/d
rs=[]
for h in [1,5]:
    _,_,r=svd(np.asarray(W["W_v.0"][64*h:64*(h+1)],float)@P,full_matrices=False)
    rs.append(r[:28])
G=sum(r.T@r for r in rs)
e,u=np.linalg.eigh(G)
u=u[:,np.argsort(e)[::-1][:28]]
F=np.asarray(W["W_fc.0"],float)*W["ln2_w.0"][None,:]
O=np.asarray(W["W_proj.0"],float)
X=F@u
ica=FastICA(n_components=28,whiten="unit-variance",random_state=17,
            max_iter=4000,tol=.001)
Z=ica.fit_transform(X)
sg=np.where(skew(Z,axis=0)>=0,1.,-1.)
Z*=sg
mix=ica.mixing_*sg[None,:]
print("ICA component: kurtosis, dominant row and its coefficient, top-five row indices, output-column norm")
for k in range(28):
    ix=np.argsort(np.abs(Z[:,k]))[-5:][::-1]
    print(k,round(kurtosis(Z[:,k]),1),int(ix[0]),round(Z[ix[0],k],2),
          ix.tolist(),round(np.linalg.norm(O[:,ix[0]]),3))

print("\nLargest initial MLP input row norms: row, full norm, content-projection norm, output-column norm")
ix=np.argsort(np.linalg.norm(F,axis=1))[-40:][::-1]
for i in ix:
    print(int(i),round(np.linalg.norm(F[i]),3),round(np.linalg.norm(X[i]),3),
          round(np.linalg.norm(O[:,i]),3))

print("\nCharacter-direction candidate geometry")
dirs=u@mix
dirs/=np.linalg.norm(dirs,axis=0,keepdims=True)
gram=dirs.T@dirs
print("Pairwise cosine quantiles:",np.round(np.quantile(gram[np.triu_indices(28,1)],[0,.1,.25,.5,.75,.9,1]),3))
for k in range(28):
    ix=np.argsort(gram[k])[-4:][::-1]
    print(k,[(int(i),round(gram[k,i],3)) for i in ix if i!=k])

print("\nContent-key geometry of L0H2, in the candidate direction basis")
s=slice(128,192)
a=np.asarray(W["W_q.0"][s],float).T@np.asarray(W["W_k.0"][s],float)
g=dirs.T@a@dirs
g/=np.linalg.norm(g)
for k in range(28):
    ix=np.argsort(g[k])[-3:][::-1]
    print(k,[(int(i),round(g[k,i],3)) for i in ix])
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 5,
  "turn": "t005",
  "code": "import numpy as np\nfrom scipy.linalg import svd\nfrom scipy.stats import skew, kurtosis\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nG=sum(r.T@r for r in rs)\ne,u=np.linalg.eigh(G)\nu=u[:,np.argsort(e)[::-1][:28]]\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nO=np.asarray(W[\"W_proj.0\"],float)\nX=F@u\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,\n            max_iter=4000,tol=.001)\nZ=ica.fit_transform(X)\nsg=np.where(skew(Z,axis=0)>=0,1.,-1.)\nZ*=sg\nmix=ica.mixing_*sg[None,:]\nprint(\"ICA component: kurtosis, dominant row and its coefficient, top-five row indices, output-column norm\")\nfor k in range(28):\n    ix=np.argsort(np.abs(Z[:,k]))[-5:][::-1]\n    print(k,round(kurtosis(Z[:,k]),1),int(ix[0]),round(Z[ix[0],k],2),\n          ix.tolist(),round(np.linalg.norm(O[:,ix[0]]),3))\n\nprint(\"\\nLargest initial MLP input row norms: row, full norm, content-projection norm, output-column norm\")\nix=np.argsort(np.linalg.norm(F,axis=1))[-40:][::-1]\nfor i in ix:\n    print(int(i),round(np.linalg.norm(F[i]),3),round(np.linalg.norm(X[i]),3),\n          round(np.linalg.norm(O[:,i]),3))\n\nprint(\"\\nCharacter-direction candidate geometry\")\ndirs=u@mix\ndirs/=np.linalg.norm(dirs,axis=0,keepdims=True)\ngram=dirs.T@dirs\nprint(\"Pairwise cosine quantiles:\",np.round(np.quantile(gram[np.triu_indices(28,1)],[0,.1,.25,.5,.75,.9,1]),3))\nfor k in range(28):\n    ix=np.argsort(gram[k])[-4:][::-1]\n    print(k,[(int(i),round(gram[k,i],3)) for i in ix if i!=k])\n\nprint(\"\\nContent-key geometry of L0H2, in the candidate direction basis\")\ns=slice(128,192)\na=np.asarray(W[\"W_q.0\"][s],float).T@np.asarray(W[\"W_k.0\"][s],float)\ng=dirs.T@a@dirs\ng/=np.linalg.norm(g)\nfor k in range(28):\n    ix=np.argsort(g[k])[-3:][::-1]\n    print(k,[(int(i),round(g[k,i],3)) for i in ix])",
  "out": "ICA component: kurtosis, dominant row and its coefficient, top-five row indices, output-column norm\n0 43.3 344 10.48 [344, 736, 1332, 483, 231] 1.864\n1 43.6 126 12.16 [126, 853, 1224, 97, 553] 1.717\n2 162.4 451 18.97 [451, 705, 827, 464, 752] 2.087\n3 78.5 739 14.14 [739, 213, 1491, 595, 1409] 1.765\n4 49.2 773 13.12 [773, 1416, 764, 922, 193] 1.822\n5 36.1 857 11.46 [857, 655, 574, 1326, 727] 1.65\n6 121.5 1312 19.32 [1312, 650, 1357, 902, 985] 2.052\n7 105.1 996 17.38 [996, 308, 1292, 1192, 1047] 1.925\n8 73.3 674 15.06 [674, 5, 347, 62, 143] 1.796\n9 65.7 1270 15.01 [1270, 220, 551, 1337, 1522] 1.782\n10 53.6 1214 12.5 [1214, 1113, 1135, 447, 777] 2.474\n11 82.7 959 16.83 [959, 1034, 1031, 513, 636] 1.906\n12 26.7 388 12.66 [388, 188, 573, 749, 112] 2.478\n13 15.3 1465 7.26 [1465, 1325, 411, 870, 1417] 1.84\n14 171.9 605 18.53 [605, 81, 238, 1288, 1381] 1.994\n15 197.5 1464 22.34 [1464, 1438, 1078, 765, 459] 2.182\n16 69.5 1023 14.16 [1023, 1075, 676, 47, 727] 1.733\n17 376.3 305 26.43 [305, 35, 541, 555, 1287] 2.487\n18 59.4 293 13.82 [293, 360, 875, 1153, 593] 1.756\n19 79.2 1351 15.58 [1351, 1100, 91, 339, 596] 1.893\n20 166.8 246 17.37 [246, 1145, 102, 889, 1463] 1.91\n21 223.0 1046 20.52 [1046, 1483, 1449, 1428, 840] 2.231\n22 30.1 903 -10.13 [903, 193, 353, 1407, 1204] 1.752\n23 153.1 399 18.48 [399, 1492, 761, 1280, 985] 2.04\n24 141.9 1322 17.55 [1322, 1102, 204, 747, 84] 2.13\n25 261.5 947 23.79 [947, 1197, 520, 924, 1395] 2.259\n26 129.8 1085 18.04 [1085, 413, 1338, 507, 1097] 2.039\n27 75.7 1531 15.24 [1531, 1030, 1264, 1331, 849] 1.9\n\nLargest initial MLP input row norms: row, full norm, content-projection norm, output-column norm\n305 4.325 3.038 2.487\n780 3.453 1.021 2.275\n28 3.412 1.241 2.333\n1428 3.401 1.19 2.669\n947 3.384 2.785 2.259\n1423 3.344 1.031 2.199\n188 3.323 1.08 3.366\n1046 3.288 2.376 2.231\n941 3.198 0.957 2.235\n1397 3.191 1.01 2.403\n809 3.174 0.864 2.12\n762 3.132 0.893 2.245\n696 3.108 0.625 2.102\n1074 3.103 0.916 2.13\n1464 3.103 2.553 2.182\n1504 3.097 1.022 2.225\n524 3.055 1.714 2.238\n675 3.027 1.797 2.682\n112 3.024 1.097 2.35\n148 3.024 1.043 2.143\n1216 3.009 0.848 2.262\n1041 2.996 0.747 2.149\n786 2.992 0.774 2.078\n942 2.981 1.451 3.131\n511 2.977 0.871 2.224\n1383 2.969 1.379 3.3\n536 2.954 0.854 2.048\n1214 2.954 1.863 2.474\n1483 2.946 2.128 2.123\n608 2.944 0.842 2.221\n912 2.94 0.58 2.074\n467 2.933 0.71 2.124\n1069 2.924 0.913 2.214\n555 2.916 1.227 2.394\n1139 2.907 0.703 2.123\n1478 2.883 0.71 2.154\n1322 2.837 1.998 2.13\n1226 2.831 0.892 2.173\n230 2.83 1.589 2.79\n451 2.814 2.187 2.087\n\nCharacter-direction candidate geometry\nPairwise cosine quantiles: [-0.247 -0.058 -0.028  0.009  0.041  0.074  0.446]\n0 [(13, np.float64(0.134)), (20, np.float64(0.068)), (19, np.float64(0.062))]\n1 [(7, np.float64(0.094)), (2, np.float64(0.084)), (10, np.float64(0.083))]\n2 [(23, np.float64(0.162)), (25, np.float64(0.118)), (1, np.float64(0.084))]\n3 [(12, np.float64(0.316)), (21, np.float64(0.171)), (27, np.float64(0.098))]\n4 [(11, np.float64(0.102)), (9, np.float64(0.098)), (3, np.float64(0.061))]\n5 [(16, np.float64(0.146)), (18, np.float64(0.138)), (24, np.float64(0.086))]\n6 [(14, np.float64(0.071)), (24, np.float64(0.061)), (0, np.float64(0.06))]\n7 [(1, np.float64(0.094)), (22, np.float64(0.063)), (26, np.float64(0.056))]\n8 [(20, np.float64(0.122)), (10, np.float64(0.078)), (19, np.float64(0.072))]\n9 [(4, np.float64(0.098)), (17, np.float64(0.073)), (3, np.float64(0.07))]\n10 [(1, np.float64(0.083)), (8, np.float64(0.078)), (17, np.float64(0.073))]\n11 [(12, np.float64(0.127)), (4, np.float64(0.102)), (21, np.float64(0.089))]\n12 [(21, np.float64(0.446)), (3, np.float64(0.316)), (24, np.float64(0.21))]\n13 [(0, np.float64(0.134)), (19, np.float64(0.076)), (15, np.float64(0.06))]\n14 [(17, np.float64(0.106)), (12, np.float64(0.104)), (21, np.float64(0.098))]\n15 [(25, np.float64(0.107)), (23, np.float64(0.088)), (2, np.float64(0.083))]\n16 [(5, np.float64(0.146)), (24, np.float64(0.075)), (18, np.float64(0.059))]\n17 [(24, np.float64(0.162)), (14, np.float64(0.106)), (2, np.float64(0.076))]\n18 [(12, np.float64(0.17)), (5, np.float64(0.138)), (22, np.float64(0.082))]\n19 [(13, np.float64(0.076)), (20, np.float64(0.076)), (8, np.float64(0.072))]\n20 [(8, np.float64(0.122)), (19, np.float64(0.076)), (0, np.float64(0.068))]\n21 [(12, np.float64(0.446)), (3, np.float64(0.171)), (27, np.float64(0.154))]\n22 [(18, np.float64(0.082)), (7, np.float64(0.063)), (6, np.float64(0.059))]\n23 [(2, np.float64(0.162)), (21, np.float64(0.096)), (15, np.float64(0.088))]\n24 [(12, np.float64(0.21)), (17, np.float64(0.162)), (5, np.float64(0.086))]\n25 [(2, np.float64(0.118)), (15, np.float64(0.107)), (11, np.float64(0.086))]\n26 [(21, np.float64(0.094)), (17, np.float64(0.068)), (7, np.float64(0.056))]\n27 [(21, np.float64(0.154)), (3, np.float64(0.098)), (5, np.float64(0.077))]\n\nContent-key geometry of L0H2, in the candidate direction basis\n0 [(19, np.float64(0.042)), (7, np.float64(0.026)), (13, np.float64(0.014))]\n1 [(7, np.float64(0.046)), (20, np.float64(0.03)), (2, np.float64(0.028))]\n2 [(11, np.float64(0.077)), (21, np.float64(0.062)), (26, np.float64(0.054))]\n3 [(19, np.float64(0.042)), (8, np.float64(0.016)), (13, np.float64(0.011))]\n4 [(19, np.float64(0.044)), (10, np.float64(0.028)), (13, np.float64(0.021))]\n5 [(5, np.float64(0.056)), (4, np.float64(0.036)), (10, np.float64(0.031))]\n6 [(21, np.float64(0.065)), (25, np.float64(0.05)), (4, np.float64(0.029))]\n7 [(26, np.float64(0.065)), (11, np.float64(0.045)), (23, np.float64(0.042))]\n8 [(16, np.float64(0.029)), (4, np.float64(0.028)), (7, np.float64(0.024))]\n9 [(9, np.float64(0.047)), (20, np.float64(0.036)), (23, np.float64(0.016))]\n10 [(19, np.float64(0.055)), (14, np.float64(0.044)), (20, np.float64(0.022))]\n11 [(25, np.float64(0.056)), (24, np.float64(0.035)), (23, np.float64(0.033))]\n12 [(14, np.float64(0.057)), (8, np.float64(0.041)), (20, np.float64(0.039))]\n13 [(0, np.float64(0.056)), (14, np.float64(0.037)), (4, np.float64(0.036))]\n14 [(7, np.float64(0.054)), (14, np.float64(0.047)), (2, np.float64(0.037))]\n15 [(21, np.float64(0.06)), (15, np.float64(0.051)), (27, np.float64(0.047))]\n16 [(16, np.float64(0.068)), (21, np.float64(0.058)), (1, np.float64(0.05))]\n17 [(21, np.float64(0.109)), (14, np.float64(0.05)), (26, np.float64(0.049))]\n18 [(19, np.float64(0.065)), (23, np.float64(0.053)), (20, np.float64(0.046))]\n19 [(4, np.float64(0.073)), (22, np.float64(0.028)), (6, np.float64(0.018))]\n20 [(25, np.float64(0.079)), (1, np.float64(0.047)), (4, np.float64(0.038))]\n21 [(20, np.float64(0.088)), (19, np.float64(0.068)), (14, np.float64(0.048))]\n22 [(19, np.float64(0.054)), (9, np.float64(0.029)), (24, np.float64(0.026))]\n23 [(23, np.float64(0.086)), (26, np.float64(0.075)), (11, np.float64(0.059))]\n24 [(8, np.float64(0.082)), (14, np.float64(0.048)), (19, np.float64(0.023))]\n25 [(26, np.float64(0.048)), (2, np.float64(0.034)), (0, np.float64(0.024))]\n26 [(19, np.float64(0.126)), (17, np.float64(0.1)), (10, np.float64(0.043))]\n27 [(19, np.float64(0.039)), (14, np.float64(0.035)), (9, np.float64(0.025))]\n",
  "seconds": 1.506,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "aafb27b839accb7fc61eec8e8397af5c035275de8c9359a7e9d055a87123ca49",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
ICA component: kurtosis, dominant row and its coefficient, top-five row indices, output-column norm
0 43.3 344 10.48 [344, 736, 1332, 483, 231] 1.864
1 43.6 126 12.16 [126, 853, 1224, 97, 553] 1.717
2 162.4 451 18.97 [451, 705, 827, 464, 752] 2.087
3 78.5 739 14.14 [739, 213, 1491, 595, 1409] 1.765
4 49.2 773 13.12 [773, 1416, 764, 922, 193] 1.822
5 36.1 857 11.46 [857, 655, 574, 1326, 727] 1.65
6 121.5 1312 19.32 [1312, 650, 1357, 902, 985] 2.052
7 105.1 996 17.38 [996, 308, 1292, 1192, 1047] 1.925
8 73.3 674 15.06 [674, 5, 347, 62, 143] 1.796
9 65.7 1270 15.01 [1270, 220, 551, 1337, 1522] 1.782
10 53.6 1214 12.5 [1214, 1113, 1135, 447, 777] 2.474
11 82.7 959 16.83 [959, 1034, 1031, 513, 636] 1.906
12 26.7 388 12.66 [388, 188, 573, 749, 112] 2.478
13 15.3 1465 7.26 [1465, 1325, 411, 870, 1417] 1.84
14 171.9 605 18.53 [605, 81, 238, 1288, 1381] 1.994
15 197.5 1464 22.34 [1464, 1438, 1078, 765, 459] 2.182
16 69.5 1023 14.16 [1023, 1075, 676, 47, 727] 1.733
17 376.3 305 26.43 [305, 35, 541, 555, 1287] 2.487
18 59.4 293 13.82 [293, 360, 875, 1153, 593] 1.756
19 79.2 1351 15.58 [1351, 1100, 91, 339, 596] 1.893
20 166.8 246 17.37 [246, 1145, 102, 889, 1463] 1.91
21 223.0 1046 20.52 [1046, 1483, 1449, 1428, 840] 2.231
22 30.1 903 -10.13 [903, 193, 353, 1407, 1204] 1.752
23 153.1 399 18.48 [399, 1492, 761, 1280, 985] 2.04
24 141.9 1322 17.55 [1322, 1102, 204, 747, 84] 2.13
25 261.5 947 23.79 [947, 1197, 520, 924, 1395] 2.259
26 129.8 1085 18.04 [1085, 413, 1338, 507, 1097] 2.039
27 75.7 1531 15.24 [1531, 1030, 1264, 1331, 849] 1.9

Largest initial MLP input row norms: row, full norm, content-projection norm, output-column norm
305 4.325 3.038 2.487
780 3.453 1.021 2.275
28 3.412 1.241 2.333
1428 3.401 1.19 2.669
947 3.384 2.785 2.259
1423 3.344 1.031 2.199
188 3.323 1.08 3.366
1046 3.288 2.376 2.231
941 3.198 0.957 2.235
1397 3.191 1.01 2.403
809 3.174 0.864 2.12
762 3.132 0.893 2.245
696 3.108 0.625 2.102
1074 3.103 0.916 2.13
1464 3.103 2.553 2.182
1504 3.097 1.022 2.225
524 3.055 1.714 2.238
675 3.027 1.797 2.682
112 3.024 1.097 2.35
148 3.024 1.043 2.143
1216 3.009 0.848 2.262
1041 2.996 0.747 2.149
786 2.992 0.774 2.078
942 2.981 1.451 3.131
511 2.977 0.871 2.224
1383 2.969 1.379 3.3
536 2.954 0.854 2.048
1214 2.954 1.863 2.474
1483 2.946 2.128 2.123
608 2.944 0.842 2.221
912 2.94 0.58 2.074
467 2.933 0.71 2.124
1069 2.924 0.913 2.214
555 2.916 1.227 2.394
1139 2.907 0.703 2.123
1478 2.883 0.71 2.154
1322 2.837 1.998 2.13
1226 2.831 0.892 2.173
230 2.83 1.589 2.79
451 2.814 2.187 2.087

Character-direction candidate geometry
Pairwise cosine quantiles: [-0.247 -0.058 -0.028  0.009  0.041  0.074  0.446]
0 [(13, np.float64(0.134)), (20, np.float64(0.068)), (19, np.float64(0.062))]
1 [(7, np.float64(0.094)), (2, np.float64(0.084)), (10, np.float64(0.083))]
2 [(23, np.float64(0.162)), (25, np.float64(0.118)), (1, np.float64(0.084))]
3 [(12, np.float64(0.316)), (21, np.float64(0.171)), (27, np.float64(0.098))]
4 [(11, np.float64(0.102)), (9, np.float64(0.098)), (3, np.float64(0.061))]
5 [(16, np.float64(0.146)), (18, np.float64(0.138)), (24, np.float64(0.086))]
6 [(14, np.float64(0.071)), (24, np.float64(0.061)), (0, np.float64(0.06))]
7 [(1, np.float64(0.094)), (22, np.float64(0.063)), (26, np.float64(0.056))]
8 [(20, np.float64(0.122)), (10, np.float64(0.078)), (19, np.float64(0.072))]
9 [(4, np.float64(0.098)), (17, np.float64(0.073)), (3, np.float64(0.07))]
10 [(1, np.float64(0.083)), (8, np.float64(0.078)), (17, np.float64(0.073))]
11 [(12, np.float64(0.127)), (4, np.float64(0.102)), (21, np.float64(0.089))]
12 [(21, np.float64(0.446)), (3, np.float64(0.316)), (24, np.float64(0.21))]
13 [(0, np.float64(0.134)), (19, np.float64(0.076)), (15, np.float64(0.06))]
14 [(17, np.float64(0.106)), (12, np.float64(0.104)), (21, np.float64(0.098))]
15 [(25, np.float64(0.107)), (23, np.float64(0.088)), (2, np.float64(0.083))]
16 [(5, np.float64(0.146)), (24, np.float64(0.075)), (18, np.float64(0.059))]
17 [(24, np.float64(0.162)), (14, np.float64(0.106)), (2, np.float64(0.076))]
18 [(12, np.float64(0.17)), (5, np.float64(0.138)), (22, np.float64(0.082))]
19 [(13, np.float64(0.076)), (20, np.float64(0.076)), (8, np.float64(0.072))]
20 [(8, np.float64(0.122)), (19, np.float64(0.076)), (0, np.float64(0.068))]
21 [(12, np.float64(0.446)), (3, np.float64(0.171)), (27, np.float64(0.154))]
22 [(18, np.float64(0.082)), (7, np.float64(0.063)), (6, np.float64(0.059))]
23 [(2, np.float64(0.162)), (21, np.float64(0.096)), (15, np.float64(0.088))]
24 [(12, np.float64(0.21)), (17, np.float64(0.162)), (5, np.float64(0.086))]
25 [(2, np.float64(0.118)), (15, np.float64(0.107)), (11, np.float64(0.086))]
26 [(21, np.float64(0.094)), (17, np.float64(0.068)), (7, np.float64(0.056))]
27 [(21, np.float64(0.154)), (3, np.float64(0.098)), (5, np.float64(0.077))]

Content-key geometry of L0H2, in the candidate direction basis
0 [(19, np.float64(0.042)), (7, np.float64(0.026)), (13, np.float64(0.014))]
1 [(7, np.float64(0.046)), (20, np.float64(0.03)), (2, np.float64(0.028))]
2 [(11, np.float64(0.077)), (21, np.float64(0.062)), (26, np.float64(0.054))]
3 [(19, np.float64(0.042)), (8, np.float64(0.016)), (13, np.float64(0.011))]
4 [(19, np.float64(0.044)), (10, np.float64(0.028)), (13, np.float64(0.021))]
5 [(5, np.float64(0.056)), (4, np.float64(0.036)), (10, np.float64(0.031))]
6 [(21, np.float64(0.065)), (25, np.float64(0.05)), (4, np.float64(0.029))]
7 [(26, np.float64(0.065)), (11, np.float64(0.045)), (23, np.float64(0.042))]
8 [(16, np.float64(0.029)), (4, np.float64(0.028)), (7, np.float64(0.024))]
9 [(9, np.float64(0.047)), (20, np.float64(0.036)), (23, np.float64(0.016))]
10 [(19, np.float64(0.055)), (14, np.float64(0.044)), (20, np.float64(0.022))]
11 [(25, np.float64(0.056)), (24, np.float64(0.035)), (23, np.float64(0.033))]
12 [(14, np.float64(0.057)), (8, np.float64(0.041)), (20, np.float64(0.039))]
13 [(0, np.float64(0.056)), (14, np.float64(0.037)), (4, np.float64(0.036))]
14 [(7, np.float64(0.054)), (14, np.float64(0.047)), (2, np.float64(0.037))]
15 [(21, np.float64(0.06)), (15, np.float64(0.051)), (27, np.float64(0.047))]
16 [(16, np.float64(0.068)), (21, np.float64(0.058)), (1, np.float64(0.05))]
17 [(21, np.float64(0.109)), (14, np.float64(0.05)), (26, np.float64(0.049))]
18 [(19, np.float64(0.065)), (23, np.float64(0.053)), (20, np.float64(0.046))]
19 [(4, np.float64(0.073)), (22, np.float64(0.028)), (6, np.float64(0.018))]
20 [(25, np.float64(0.079)), (1, np.float64(0.047)), (4, np.float64(0.038))]
21 [(20, np.float64(0.088)), (19, np.float64(0.068)), (14, np.float64(0.048))]
22 [(19, np.float64(0.054)), (9, np.float64(0.029)), (24, np.float64(0.026))]
23 [(23, np.float64(0.086)), (26, np.float64(0.075)), (11, np.float64(0.059))]
24 [(8, np.float64(0.082)), (14, np.float64(0.048)), (19, np.float64(0.023))]
25 [(26, np.float64(0.048)), (2, np.float64(0.034)), (0, np.float64(0.024))]
26 [(19, np.float64(0.126)), (17, np.float64(0.1)), (10, np.float64(0.043))]
27 [(19, np.float64(0.039)), (14, np.float64(0.035)), (9, np.float64(0.025))]


========================================================================
TURN t006
MESSAGE SENT TO CODEX
OUTPUT of run 5 of at most 100:
```
ICA component: kurtosis, dominant row and its coefficient, top-five row indices, output-column norm
0 43.3 344 10.48 [344, 736, 1332, 483, 231] 1.864
1 43.6 126 12.16 [126, 853, 1224, 97, 553] 1.717
2 162.4 451 18.97 [451, 705, 827, 464, 752] 2.087
3 78.5 739 14.14 [739, 213, 1491, 595, 1409] 1.765
4 49.2 773 13.12 [773, 1416, 764, 922, 193] 1.822
5 36.1 857 11.46 [857, 655, 574, 1326, 727] 1.65
6 121.5 1312 19.32 [1312, 650, 1357, 902, 985] 2.052
7 105.1 996 17.38 [996, 308, 1292, 1192, 1047] 1.925
8 73.3 674 15.06 [674, 5, 347, 62, 143] 1.796
9 65.7 1270 15.01 [1270, 220, 551, 1337, 1522] 1.782
10 53.6 1214 12.5 [1214, 1113, 1135, 447, 777] 2.474
11 82.7 959 16.83 [959, 1034, 1031, 513, 636] 1.906
12 26.7 388 12.66 [388, 188, 573, 749, 112] 2.478
13 15.3 1465 7.26 [1465, 1325, 411, 870, 1417] 1.84
14 171.9 605 18.53 [605, 81, 238, 1288, 1381] 1.994
15 197.5 1464 22.34 [1464, 1438, 1078, 765, 459] 2.182
16 69.5 1023 14.16 [1023, 1075, 676, 47, 727] 1.733
17 376.3 305 26.43 [305, 35, 541, 555, 1287] 2.487
18 59.4 293 13.82 [293, 360, 875, 1153, 593] 1.756
19 79.2 1351 15.58 [1351, 1100, 91, 339, 596] 1.893
20 166.8 246 17.37 [246, 1145, 102, 889, 1463] 1.91
21 223.0 1046 20.52 [1046, 1483, 1449, 1428, 840] 2.231
22 30.1 903 -10.13 [903, 193, 353, 1407, 1204] 1.752
23 153.1 399 18.48 [399, 1492, 761, 1280, 985] 2.04
24 141.9 1322 17.55 [1322, 1102, 204, 747, 84] 2.13
25 261.5 947 23.79 [947, 1197, 520, 924, 1395] 2.259
26 129.8 1085 18.04 [1085, 413, 1338, 507, 1097] 2.039
27 75.7 1531 15.24 [1531, 1030, 1264, 1331, 849] 1.9

Largest initial MLP input row norms: row, full norm, content-projection norm, output-column norm
305 4.325 3.038 2.487
780 3.453 1.021 2.275
28 3.412 1.241 2.333
1428 3.401 1.19 2.669
947 3.384 2.785 2.259
1423 3.344 1.031 2.199
188 3.323 1.08 3.366
1046 3.288 2.376 2.231
941 3.198 0.957 2.235
1397 3.191 1.01 2.403
809 3.174 0.864 2.12
762 3.132 0.893 2.245
696 3.108 0.625 2.102
1074 3.103 0.916 2.13
1464 3.103 2.553 2.182
1504 3.097 1.022 2.225
524 3.055 1.714 2.238
675 3.027 1.797 2.682
112 3.024 1.097 2.35
148 3.024 1.043 2.143
1216 3.009 0.848 2.262
1041 2.996 0.747 2.149
786 2.992 0.774 2.078
942 2.981 1.451 3.131
511 2.977 0.871 2.224
1383 2.969 1.379 3.3
536 2.954 0.854 2.048
1214 2.954 1.863 2.474
1483 2.946 2.128 2.123
608 2.944 0.842 2.221
912 2.94 0.58 2.074
467 2.933 0.71 2.124
1069 2.924 0.913 2.214
555 2.916 1.227 2.394
1139 2.907 0.703 2.123
1478 2.883 0.71 2.154
1322 2.837 1.998 2.13
1226 2.831 0.892 2.173
230 2.83 1.589 2.79
451 2.814 2.187 2.087

Character-direction candidate geometry
Pairwise cosine quantiles: [-0.247 -0.058 -0.028  0.009  0.041  0.074  0.446]
0 [(13, np.float64(0.134)), (20, np.float64(0.068)), (19, np.float64(0.062))]
1 [(7, np.float64(0.094)), (2, np.float64(0.084)), (10, np.float64(0.083))]
2 [(23, np.float64(0.162)), (25, np.float64(0.118)), (1, np.float64(0.084))]
3 [(12, np.float64(0.316)), (21, np.float64(0.171)), (27, np.float64(0.098))]
4 [(11, np.float64(0.102)), (9, np.float64(0.098)), (3, np.float64(0.061))]
5 [(16, np.float64(0.146)), (18, np.float64(0.138)), (24, np.float64(0.086))]
6 [(14, np.float64(0.071)), (24, np.float64(0.061)), (0, np.float64(0.06))]
7 [(1, np.float64(0.094)), (22, np.float64(0.063)), (26, np.float64(0.056))]
8 [(20, np.float64(0.122)), (10, np.float64(0.078)), (19, np.float64(0.072))]
9 [(4, np.float64(0.098)), (17, np.float64(0.073)), (3, np.float64(0.07))]
10 [(1, np.float64(0.083)), (8, np.float64(0.078)), (17, np.float64(0.073))]
11 [(12, np.float64(0.127)), (4, np.float64(0.102)), (21, np.float64(0.089))]
12 [(21, np.float64(0.446)), (3, np.float64(0.316)), (24, np.float64(0.21))]
13 [(0, np.float64(0.134)), (19, np.float64(0.076)), (15, np.float64(0.06))]
14 [(17, np.float64(0.106)), (12, np.float64(0.104)), (21, np.float64(0.098))]
15 [(25, np.float64(0.107)), (23, np.float64(0.088)), (2, np.float64(0.083))]
16 [(5, np.float64(0.146)), (24, np.float64(0.075)), (18, np.float64(0.059))]
17 [(24, np.float64(0.162)), (14, np.float64(0.106)), (2, np.float64(0.076))]
18 [(12, np.float64(0.17)), (5, np.float64(0.138)), (22, np.float64(0.082))]
19 [(13, np.float64(0.076)), (20, np.float64(0.076)), (8, np.float64(0.072))]
20 [(8, np.float64(0.122)), (19, np.float64(0.076)), (0, np.float64(0.068))]
21 [(12, np.float64(0.446)), (3, np.float64(0.171)), (27, np.float64(0.154))]
22 [(18, np.float64(0.082)), (7, np.float64(0.063)), (6, np.float64(0.059))]
23 [(2, np.float64(0.162)), (21, np.float64(0.096)), (15, np.float64(0.088))]
24 [(12, np.float64(0.21)), (17, np.float64(0.162)), (5, np.float64(0.086))]
25 [(2, np.float64(0.118)), (15, np.float64(0.107)), (11, np.float64(0.086))]
26 [(21, np.float64(0.094)), (17, np.float64(0.068)), (7, np.float64(0.056))]
27 [(21, np.float64(0.154)), (3, np.float64(0.098)), (5, np.float64(0.077))]

Content-key geometry of L0H2, in the candidate direction basis
0 [(19, np.float64(0.042)), (7, np.float64(0.026)), (13, np.float64(0.014))]
1 [(7, np.float64(0.046)), (20, np.float64(0.03)), (2, np.float64(0.028))]
2 [(11, np.float64(0.077)), (21, np.float64(0.062)), (26, np.float64(0.054))]
3 [(19, np.float64(0.042)), (8, np.float64(0.016)), (13, np.float64(0.011))]
4 [(19, np.float64(0.044)), (10, np.float64(0.028)), (13, np.float64(0.021))]
5 [(5, np.float64(0.056)), (4, np.float64(0.036)), (10, np.float64(0.031))]
6 [(21, np.float64(0.065)), (25, np.float64(0.05)), (4, np.float64(0.029))]
7 [(26, np.float64(0.065)), (11, np.float64(0.045)), (23, np.float64(0.042))]
8 [(16, np.float64(0.029)), (4, np.float64(0.028)), (7, np.float64(0.024))]
9 [(9, np.float64(0.047)), (20, np.float64(0.036)), (23, np.float64(0.016))]
10 [(19, np.float64(0.055)), (14, np.float64(0.044)), (20, np.float64(0.022))]
11 [(25, np.float64(0.056)), (24, np.float64(0.035)), (23, np.float64(0.033))]
12 [(14, np.float64(0.057)), (8, np.float64(0.041)), (20, np.float64(0.039))]
13 [(0, np.float64(0.056)), (14, np.float64(0.037)), (4, np.float64(0.036))]
14 [(7, np.float64(0.054)), (14, np.float64(0.047)), (2, np.float64(0.037))]
15 [(21, np.float64(0.06)), (15, np.float64(0.051)), (27, np.float64(0.047))]
16 [(16, np.float64(0.068)), (21, np.float64(0.058)), (1, np.float64(0.05))]
17 [(21, np.float64(0.109)), (14, np.float64(0.05)), (26, np.float64(0.049))]
18 [(19, np.float64(0.065)), (23, np.float64(0.053)), (20, np.float64(0.046))]
19 [(4, np.float64(0.073)), (22, np.float64(0.028)), (6, np.float64(0.018))]
20 [(25, np.float64(0.079)), (1, np.float64(0.047)), (4, np.float64(0.038))]
21 [(20, np.float64(0.088)), (19, np.float64(0.068)), (14, np.float64(0.048))]
22 [(19, np.float64(0.054)), (9, np.float64(0.029)), (24, np.float64(0.026))]
23 [(23, np.float64(0.086)), (26, np.float64(0.075)), (11, np.float64(0.059))]
24 [(8, np.float64(0.082)), (14, np.float64(0.048)), (19, np.float64(0.023))]
25 [(26, np.float64(0.048)), (2, np.float64(0.034)), (0, np.float64(0.024))]
26 [(19, np.float64(0.126)), (17, np.float64(0.1)), (10, np.float64(0.043))]
27 [(19, np.float64(0.039)), (14, np.float64(0.035)), (9, np.float64(0.025))]

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd
from scipy.stats import skew
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d=384
P=np.eye(d)-np.ones((d,d))/d
rs=[]
for h in [1,5]:
    _,_,r=svd(np.asarray(W["W_v.0"][64*h:64*(h+1)],float)@P,full_matrices=False)
    rs.append(r[:28])
G=sum(r.T@r for r in rs)
e,u=np.linalg.eigh(G)
u=u[:,np.argsort(e)[::-1][:28]]
F=np.asarray(W["W_fc.0"],float)*W["ln2_w.0"][None,:]
ica=FastICA(n_components=28,whiten="unit-variance",random_state=17,max_iter=4000,tol=.001)
Z=ica.fit_transform(F@u)
sg=np.where(skew(Z,axis=0)>=0,1.,-1.)
mix=ica.mixing_*sg[None,:]
T=u@np.linalg.pinv(mix.T)

# These are coefficients of MLP WEIGHT rows in weight-derived bases.
# No LayerNorm, attention distribution, or nonlinear MLP is evaluated.
C=F@T
C-=C.mean(0)
prev=[]
for h in [1,5]:
    s=slice(64*h,64*(h+1))
    b=np.asarray(W["W_o.0"][:,s],float)@(
        np.asarray(W["W_v.0"][s],float)*W["ln1_w.0"][None,:])@P
    R=F@b@T
    R-=R.mean(0)
    R/=np.std(R,axis=0,keepdims=True)
    prev.append(R)
C/=np.std(C,axis=0,keepdims=True)

for name,R in [("current",C),("H1 route",prev[0]),("H5 route",prev[1])]:
    strength=np.abs(R)
    ids=np.argmax(strength,axis=1)
    top=np.max(strength,axis=1)
    counts=np.bincount(ids[top>1.5],minlength=28)
    print(name,"dominant-axis counts (>1.5 SD):",counts.tolist())

print("\nWeight-row co-specialization: current axis versus H5-route axis")
print("Rows count neurons with both dominant coefficients >1.5 SD; positive coefficient fraction is also shown.")
ci=np.argmax(np.abs(C),axis=1)
ri=np.argmax(np.abs(prev[1]),axis=1)
cm=np.max(np.abs(C),axis=1)
rm=np.max(np.abs(prev[1]),axis=1)
sel=(cm>1.5)&(rm>1.5)
counts=np.zeros((28,28),dtype=int)
for n in np.where(sel)[0]:
    counts[ci[n],ri[n]]+=1
print("Selected neurons",int(sel.sum()),"positive current/route fractions",
      round(np.mean(C[np.where(sel)[0],ci[sel]]>0),3),
      round(np.mean(prev[1][np.where(sel)[0],ri[sel]]>0),3))
for k in range(28):
    ix=np.argsort(counts[k])[::-1][:6]
    print(k,"rowtotal",int(counts[k].sum()),
          [(int(j),int(counts[k,j])) for j in ix if counts[k,j]])

# Shared low-dimensional attention-weight structure, independent of the ICA.
A={};B={}
for l in range(4):
    scale=np.asarray(W[f"ln1_w.{l}"],float)
    for h in range(6):
        s=slice(64*h,64*(h+1))
        q,k,v=[np.asarray(W[f"W_{n}.{l}"][s],float) for n in ["q","k","v"]]
        A[l,h]=P@(scale[:,None]*(q.T@k)*scale[None,:])@P
        B[l,h]=np.asarray(W[f"W_o.{l}"][:,s],float)@(v*scale[None,:])@P
ub,sb,vb=svd(B[0,4],full_matrices=False)
print("\nL0H4 effective OV singular values:",np.round(sb[:12],3))
for rank in [2,3,6]:
    U=ub[:,:rank]
    print("Rank",rank,"L0H4 output subspace: later QK query/key energy fractions")
    for l in range(1,4):
        print("layer",l,[
            (h,round(np.linalg.norm(U.T@A[l,h])**2/np.linalg.norm(A[l,h])**2,3),
             round(np.linalg.norm(A[l,h]@U)**2/np.linalg.norm(A[l,h])**2,3))
            for h in range(6)])
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 6,
  "turn": "t006",
  "code": "import numpy as np\nfrom scipy.linalg import svd\nfrom scipy.stats import skew\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nG=sum(r.T@r for r in rs)\ne,u=np.linalg.eigh(G)\nu=u[:,np.argsort(e)[::-1][:28]]\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,max_iter=4000,tol=.001)\nZ=ica.fit_transform(F@u)\nsg=np.where(skew(Z,axis=0)>=0,1.,-1.)\nmix=ica.mixing_*sg[None,:]\nT=u@np.linalg.pinv(mix.T)\n\n# These are coefficients of MLP WEIGHT rows in weight-derived bases.\n# No LayerNorm, attention distribution, or nonlinear MLP is evaluated.\nC=F@T\nC-=C.mean(0)\nprev=[]\nfor h in [1,5]:\n    s=slice(64*h,64*(h+1))\n    b=np.asarray(W[\"W_o.0\"][:,s],float)@(\n        np.asarray(W[\"W_v.0\"][s],float)*W[\"ln1_w.0\"][None,:])@P\n    R=F@b@T\n    R-=R.mean(0)\n    R/=np.std(R,axis=0,keepdims=True)\n    prev.append(R)\nC/=np.std(C,axis=0,keepdims=True)\n\nfor name,R in [(\"current\",C),(\"H1 route\",prev[0]),(\"H5 route\",prev[1])]:\n    strength=np.abs(R)\n    ids=np.argmax(strength,axis=1)\n    top=np.max(strength,axis=1)\n    counts=np.bincount(ids[top>1.5],minlength=28)\n    print(name,\"dominant-axis counts (>1.5 SD):\",counts.tolist())\n\nprint(\"\\nWeight-row co-specialization: current axis versus H5-route axis\")\nprint(\"Rows count neurons with both dominant coefficients >1.5 SD; positive coefficient fraction is also shown.\")\nci=np.argmax(np.abs(C),axis=1)\nri=np.argmax(np.abs(prev[1]),axis=1)\ncm=np.max(np.abs(C),axis=1)\nrm=np.max(np.abs(prev[1]),axis=1)\nsel=(cm>1.5)&(rm>1.5)\ncounts=np.zeros((28,28),dtype=int)\nfor n in np.where(sel)[0]:\n    counts[ci[n],ri[n]]+=1\nprint(\"Selected neurons\",int(sel.sum()),\"positive current/route fractions\",\n      round(np.mean(C[np.where(sel)[0],ci[sel]]>0),3),\n      round(np.mean(prev[1][np.where(sel)[0],ri[sel]]>0),3))\nfor k in range(28):\n    ix=np.argsort(counts[k])[::-1][:6]\n    print(k,\"rowtotal\",int(counts[k].sum()),\n          [(int(j),int(counts[k,j])) for j in ix if counts[k,j]])\n\n# Shared low-dimensional attention-weight structure, independent of the ICA.\nA={};B={}\nfor l in range(4):\n    scale=np.asarray(W[f\"ln1_w.{l}\"],float)\n    for h in range(6):\n        s=slice(64*h,64*(h+1))\n        q,k,v=[np.asarray(W[f\"W_{n}.{l}\"][s],float) for n in [\"q\",\"k\",\"v\"]]\n        A[l,h]=P@(scale[:,None]*(q.T@k)*scale[None,:])@P\n        B[l,h]=np.asarray(W[f\"W_o.{l}\"][:,s],float)@(v*scale[None,:])@P\nub,sb,vb=svd(B[0,4],full_matrices=False)\nprint(\"\\nL0H4 effective OV singular values:\",np.round(sb[:12],3))\nfor rank in [2,3,6]:\n    U=ub[:,:rank]\n    print(\"Rank\",rank,\"L0H4 output subspace: later QK query/key energy fractions\")\n    for l in range(1,4):\n        print(\"layer\",l,[\n            (h,round(np.linalg.norm(U.T@A[l,h])**2/np.linalg.norm(A[l,h])**2,3),\n             round(np.linalg.norm(A[l,h]@U)**2/np.linalg.norm(A[l,h])**2,3))\n            for h in range(6)])",
  "out": "current dominant-axis counts (>1.5 SD): [28, 20, 16, 11, 20, 20, 17, 18, 21, 22, 18, 18, 39, 39, 13, 13, 25, 8, 24, 19, 11, 6, 28, 16, 12, 17, 13, 17]\nH1 route dominant-axis counts (>1.5 SD): [27, 19, 17, 27, 15, 20, 13, 20, 14, 18, 20, 18, 14, 17, 12, 14, 20, 12, 14, 19, 14, 9, 19, 9, 17, 11, 14, 17]\nH5 route dominant-axis counts (>1.5 SD): [23, 16, 16, 20, 24, 19, 17, 26, 23, 26, 22, 16, 13, 22, 15, 10, 23, 10, 21, 17, 14, 9, 23, 12, 8, 11, 12, 22]\n\nWeight-row co-specialization: current axis versus H5-route axis\nRows count neurons with both dominant coefficients >1.5 SD; positive coefficient fraction is also shown.\nSelected neurons 483 positive current/route fractions 0.828 0.609\n0 rowtotal 26 [(0, 11), (16, 3), (13, 3), (9, 2), (23, 2), (21, 1)]\n1 rowtotal 16 [(22, 4), (1, 3), (16, 2), (0, 1), (25, 1), (4, 1)]\n2 rowtotal 15 [(2, 9), (11, 1), (4, 1), (21, 1), (16, 1), (15, 1)]\n3 rowtotal 8 [(3, 5), (25, 1), (7, 1), (8, 1)]\n4 rowtotal 18 [(4, 6), (27, 2), (26, 2), (5, 2), (0, 2), (22, 1)]\n5 rowtotal 18 [(5, 4), (22, 2), (16, 2), (27, 1), (10, 1), (3, 1)]\n6 rowtotal 17 [(6, 8), (4, 2), (22, 1), (5, 1), (19, 1), (18, 1)]\n7 rowtotal 16 [(7, 7), (22, 2), (27, 1), (2, 1), (3, 1), (20, 1)]\n8 rowtotal 20 [(8, 4), (11, 3), (5, 3), (27, 1), (1, 1), (3, 1)]\n9 rowtotal 20 [(9, 8), (16, 3), (27, 1), (26, 1), (14, 1), (18, 1)]\n10 rowtotal 14 [(10, 5), (8, 2), (9, 2), (1, 1), (19, 1), (7, 1)]\n11 rowtotal 18 [(11, 8), (22, 2), (8, 2), (27, 1), (3, 1), (21, 1)]\n12 rowtotal 39 [(13, 4), (19, 4), (0, 4), (26, 3), (4, 3), (5, 3)]\n13 rowtotal 33 [(13, 11), (7, 3), (0, 3), (12, 2), (1, 2), (4, 2)]\n14 rowtotal 13 [(14, 5), (10, 3), (12, 1), (1, 1), (3, 1), (19, 1)]\n15 rowtotal 12 [(15, 5), (8, 2), (9, 2), (25, 1), (19, 1), (12, 1)]\n16 rowtotal 24 [(16, 4), (27, 3), (5, 3), (12, 2), (14, 2), (17, 1)]\n17 rowtotal 8 [(20, 3), (19, 2), (12, 1), (1, 1), (10, 1)]\n18 rowtotal 20 [(18, 6), (10, 3), (27, 2), (2, 1), (4, 1), (20, 1)]\n19 rowtotal 17 [(4, 3), (19, 3), (1, 2), (18, 2), (27, 1), (24, 1)]\n20 rowtotal 10 [(20, 6), (13, 1), (12, 1), (2, 1), (16, 1)]\n21 rowtotal 6 [(21, 3), (18, 1), (17, 1), (9, 1)]\n22 rowtotal 24 [(22, 7), (18, 3), (1, 2), (3, 2), (6, 2), (7, 2)]\n23 rowtotal 15 [(23, 6), (8, 2), (11, 1), (21, 1), (18, 1), (6, 1)]\n24 rowtotal 12 [(24, 2), (7, 2), (27, 1), (18, 1), (2, 1), (5, 1)]\n25 rowtotal 15 [(25, 5), (17, 2), (27, 1), (18, 1), (7, 1), (15, 1)]\n26 rowtotal 12 [(26, 4), (0, 1), (20, 1), (16, 1), (18, 1), (3, 1)]\n27 rowtotal 17 [(27, 6), (4, 3), (3, 2), (10, 2), (7, 1), (16, 1)]\n\nL0H4 effective OV singular values: [13.878  9.65   2.507  0.969  0.595  0.554  0.483  0.433  0.345  0.295\n  0.28   0.228]\nRank 2 L0H4 output subspace: later QK query/key energy fractions\nlayer 1 [(0, np.float64(0.041), np.float64(0.308)), (1, np.float64(0.067), np.float64(0.569)), (2, np.float64(0.065), np.float64(0.52)), (3, np.float64(0.093), np.float64(0.581)), (4, np.float64(0.076), np.float64(0.545)), (5, np.float64(0.05), np.float64(0.479))]\nlayer 2 [(0, np.float64(0.006), np.float64(0.048)), (1, np.float64(0.002), np.float64(0.003)), (2, np.float64(0.013), np.float64(0.064)), (3, np.float64(0.016), np.float64(0.088)), (4, np.float64(0.016), np.float64(0.09)), (5, np.float64(0.004), np.float64(0.039))]\nlayer 3 [(0, np.float64(0.001), np.float64(0.015)), (1, np.float64(0.001), np.float64(0.007)), (2, np.float64(0.002), np.float64(0.023)), (3, np.float64(0.003), np.float64(0.007)), (4, np.float64(0.002), np.float64(0.024)), (5, np.float64(0.001), np.float64(0.003))]\nRank 3 L0H4 output subspace: later QK query/key energy fractions\nlayer 1 [(0, np.float64(0.061), np.float64(0.342)), (1, np.float64(0.107), np.float64(0.627)), (2, np.float64(0.108), np.float64(0.631)), (3, np.float64(0.137), np.float64(0.677)), (4, np.float64(0.119), np.float64(0.645)), (5, np.float64(0.084), np.float64(0.524))]\nlayer 2 [(0, np.float64(0.01), np.float64(0.051)), (1, np.float64(0.005), np.float64(0.005)), (2, np.float64(0.014), np.float64(0.07)), (3, np.float64(0.022), np.float64(0.095)), (4, np.float64(0.023), np.float64(0.095)), (5, np.float64(0.006), np.float64(0.041))]\nlayer 3 [(0, np.float64(0.002), np.float64(0.017)), (1, np.float64(0.003), np.float64(0.009)), (2, np.float64(0.003), np.float64(0.025)), (3, np.float64(0.005), np.float64(0.01)), (4, np.float64(0.004), np.float64(0.025)), (5, np.float64(0.003), np.float64(0.005))]\nRank 6 L0H4 output subspace: later QK query/key energy fractions\nlayer 1 [(0, np.float64(0.1), np.float64(0.353)), (1, np.float64(0.161), np.float64(0.636)), (2, np.float64(0.175), np.float64(0.642)), (3, np.float64(0.209), np.float64(0.684)), (4, np.float64(0.186), np.float64(0.654)), (5, np.float64(0.135), np.float64(0.534))]\nlayer 2 [(0, np.float64(0.02), np.float64(0.06)), (1, np.float64(0.019), np.float64(0.013)), (2, np.float64(0.023), np.float64(0.078)), (3, np.float64(0.036), np.float64(0.102)), (4, np.float64(0.032), np.float64(0.105)), (5, np.float64(0.024), np.float64(0.051))]\nlayer 3 [(0, np.float64(0.012), np.float64(0.026)), (1, np.float64(0.013), np.float64(0.018)), (2, np.float64(0.013), np.float64(0.032)), (3, np.float64(0.018), np.float64(0.018)), (4, np.float64(0.017), np.float64(0.034)), (5, np.float64(0.012), np.float64(0.015))]\n",
  "seconds": 1.421,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "d426ed2a59e66e2505cf0e68148ca62232fde5682340720fb717f02ea477e387",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
current dominant-axis counts (>1.5 SD): [28, 20, 16, 11, 20, 20, 17, 18, 21, 22, 18, 18, 39, 39, 13, 13, 25, 8, 24, 19, 11, 6, 28, 16, 12, 17, 13, 17]
H1 route dominant-axis counts (>1.5 SD): [27, 19, 17, 27, 15, 20, 13, 20, 14, 18, 20, 18, 14, 17, 12, 14, 20, 12, 14, 19, 14, 9, 19, 9, 17, 11, 14, 17]
H5 route dominant-axis counts (>1.5 SD): [23, 16, 16, 20, 24, 19, 17, 26, 23, 26, 22, 16, 13, 22, 15, 10, 23, 10, 21, 17, 14, 9, 23, 12, 8, 11, 12, 22]

Weight-row co-specialization: current axis versus H5-route axis
Rows count neurons with both dominant coefficients >1.5 SD; positive coefficient fraction is also shown.
Selected neurons 483 positive current/route fractions 0.828 0.609
0 rowtotal 26 [(0, 11), (16, 3), (13, 3), (9, 2), (23, 2), (21, 1)]
1 rowtotal 16 [(22, 4), (1, 3), (16, 2), (0, 1), (25, 1), (4, 1)]
2 rowtotal 15 [(2, 9), (11, 1), (4, 1), (21, 1), (16, 1), (15, 1)]
3 rowtotal 8 [(3, 5), (25, 1), (7, 1), (8, 1)]
4 rowtotal 18 [(4, 6), (27, 2), (26, 2), (5, 2), (0, 2), (22, 1)]
5 rowtotal 18 [(5, 4), (22, 2), (16, 2), (27, 1), (10, 1), (3, 1)]
6 rowtotal 17 [(6, 8), (4, 2), (22, 1), (5, 1), (19, 1), (18, 1)]
7 rowtotal 16 [(7, 7), (22, 2), (27, 1), (2, 1), (3, 1), (20, 1)]
8 rowtotal 20 [(8, 4), (11, 3), (5, 3), (27, 1), (1, 1), (3, 1)]
9 rowtotal 20 [(9, 8), (16, 3), (27, 1), (26, 1), (14, 1), (18, 1)]
10 rowtotal 14 [(10, 5), (8, 2), (9, 2), (1, 1), (19, 1), (7, 1)]
11 rowtotal 18 [(11, 8), (22, 2), (8, 2), (27, 1), (3, 1), (21, 1)]
12 rowtotal 39 [(13, 4), (19, 4), (0, 4), (26, 3), (4, 3), (5, 3)]
13 rowtotal 33 [(13, 11), (7, 3), (0, 3), (12, 2), (1, 2), (4, 2)]
14 rowtotal 13 [(14, 5), (10, 3), (12, 1), (1, 1), (3, 1), (19, 1)]
15 rowtotal 12 [(15, 5), (8, 2), (9, 2), (25, 1), (19, 1), (12, 1)]
16 rowtotal 24 [(16, 4), (27, 3), (5, 3), (12, 2), (14, 2), (17, 1)]
17 rowtotal 8 [(20, 3), (19, 2), (12, 1), (1, 1), (10, 1)]
18 rowtotal 20 [(18, 6), (10, 3), (27, 2), (2, 1), (4, 1), (20, 1)]
19 rowtotal 17 [(4, 3), (19, 3), (1, 2), (18, 2), (27, 1), (24, 1)]
20 rowtotal 10 [(20, 6), (13, 1), (12, 1), (2, 1), (16, 1)]
21 rowtotal 6 [(21, 3), (18, 1), (17, 1), (9, 1)]
22 rowtotal 24 [(22, 7), (18, 3), (1, 2), (3, 2), (6, 2), (7, 2)]
23 rowtotal 15 [(23, 6), (8, 2), (11, 1), (21, 1), (18, 1), (6, 1)]
24 rowtotal 12 [(24, 2), (7, 2), (27, 1), (18, 1), (2, 1), (5, 1)]
25 rowtotal 15 [(25, 5), (17, 2), (27, 1), (18, 1), (7, 1), (15, 1)]
26 rowtotal 12 [(26, 4), (0, 1), (20, 1), (16, 1), (18, 1), (3, 1)]
27 rowtotal 17 [(27, 6), (4, 3), (3, 2), (10, 2), (7, 1), (16, 1)]

L0H4 effective OV singular values: [13.878  9.65   2.507  0.969  0.595  0.554  0.483  0.433  0.345  0.295
  0.28   0.228]
Rank 2 L0H4 output subspace: later QK query/key energy fractions
layer 1 [(0, np.float64(0.041), np.float64(0.308)), (1, np.float64(0.067), np.float64(0.569)), (2, np.float64(0.065), np.float64(0.52)), (3, np.float64(0.093), np.float64(0.581)), (4, np.float64(0.076), np.float64(0.545)), (5, np.float64(0.05), np.float64(0.479))]
layer 2 [(0, np.float64(0.006), np.float64(0.048)), (1, np.float64(0.002), np.float64(0.003)), (2, np.float64(0.013), np.float64(0.064)), (3, np.float64(0.016), np.float64(0.088)), (4, np.float64(0.016), np.float64(0.09)), (5, np.float64(0.004), np.float64(0.039))]
layer 3 [(0, np.float64(0.001), np.float64(0.015)), (1, np.float64(0.001), np.float64(0.007)), (2, np.float64(0.002), np.float64(0.023)), (3, np.float64(0.003), np.float64(0.007)), (4, np.float64(0.002), np.float64(0.024)), (5, np.float64(0.001), np.float64(0.003))]
Rank 3 L0H4 output subspace: later QK query/key energy fractions
layer 1 [(0, np.float64(0.061), np.float64(0.342)), (1, np.float64(0.107), np.float64(0.627)), (2, np.float64(0.108), np.float64(0.631)), (3, np.float64(0.137), np.float64(0.677)), (4, np.float64(0.119), np.float64(0.645)), (5, np.float64(0.084), np.float64(0.524))]
layer 2 [(0, np.float64(0.01), np.float64(0.051)), (1, np.float64(0.005), np.float64(0.005)), (2, np.float64(0.014), np.float64(0.07)), (3, np.float64(0.022), np.float64(0.095)), (4, np.float64(0.023), np.float64(0.095)), (5, np.float64(0.006), np.float64(0.041))]
layer 3 [(0, np.float64(0.002), np.float64(0.017)), (1, np.float64(0.003), np.float64(0.009)), (2, np.float64(0.003), np.float64(0.025)), (3, np.float64(0.005), np.float64(0.01)), (4, np.float64(0.004), np.float64(0.025)), (5, np.float64(0.003), np.float64(0.005))]
Rank 6 L0H4 output subspace: later QK query/key energy fractions
layer 1 [(0, np.float64(0.1), np.float64(0.353)), (1, np.float64(0.161), np.float64(0.636)), (2, np.float64(0.175), np.float64(0.642)), (3, np.float64(0.209), np.float64(0.684)), (4, np.float64(0.186), np.float64(0.654)), (5, np.float64(0.135), np.float64(0.534))]
layer 2 [(0, np.float64(0.02), np.float64(0.06)), (1, np.float64(0.019), np.float64(0.013)), (2, np.float64(0.023), np.float64(0.078)), (3, np.float64(0.036), np.float64(0.102)), (4, np.float64(0.032), np.float64(0.105)), (5, np.float64(0.024), np.float64(0.051))]
layer 3 [(0, np.float64(0.012), np.float64(0.026)), (1, np.float64(0.013), np.float64(0.018)), (2, np.float64(0.013), np.float64(0.032)), (3, np.float64(0.018), np.float64(0.018)), (4, np.float64(0.017), np.float64(0.034)), (5, np.float64(0.012), np.float64(0.015))]


========================================================================
TURN t007
MESSAGE SENT TO CODEX
OUTPUT of run 6 of at most 100:
```
current dominant-axis counts (>1.5 SD): [28, 20, 16, 11, 20, 20, 17, 18, 21, 22, 18, 18, 39, 39, 13, 13, 25, 8, 24, 19, 11, 6, 28, 16, 12, 17, 13, 17]
H1 route dominant-axis counts (>1.5 SD): [27, 19, 17, 27, 15, 20, 13, 20, 14, 18, 20, 18, 14, 17, 12, 14, 20, 12, 14, 19, 14, 9, 19, 9, 17, 11, 14, 17]
H5 route dominant-axis counts (>1.5 SD): [23, 16, 16, 20, 24, 19, 17, 26, 23, 26, 22, 16, 13, 22, 15, 10, 23, 10, 21, 17, 14, 9, 23, 12, 8, 11, 12, 22]

Weight-row co-specialization: current axis versus H5-route axis
Rows count neurons with both dominant coefficients >1.5 SD; positive coefficient fraction is also shown.
Selected neurons 483 positive current/route fractions 0.828 0.609
0 rowtotal 26 [(0, 11), (16, 3), (13, 3), (9, 2), (23, 2), (21, 1)]
1 rowtotal 16 [(22, 4), (1, 3), (16, 2), (0, 1), (25, 1), (4, 1)]
2 rowtotal 15 [(2, 9), (11, 1), (4, 1), (21, 1), (16, 1), (15, 1)]
3 rowtotal 8 [(3, 5), (25, 1), (7, 1), (8, 1)]
4 rowtotal 18 [(4, 6), (27, 2), (26, 2), (5, 2), (0, 2), (22, 1)]
5 rowtotal 18 [(5, 4), (22, 2), (16, 2), (27, 1), (10, 1), (3, 1)]
6 rowtotal 17 [(6, 8), (4, 2), (22, 1), (5, 1), (19, 1), (18, 1)]
7 rowtotal 16 [(7, 7), (22, 2), (27, 1), (2, 1), (3, 1), (20, 1)]
8 rowtotal 20 [(8, 4), (11, 3), (5, 3), (27, 1), (1, 1), (3, 1)]
9 rowtotal 20 [(9, 8), (16, 3), (27, 1), (26, 1), (14, 1), (18, 1)]
10 rowtotal 14 [(10, 5), (8, 2), (9, 2), (1, 1), (19, 1), (7, 1)]
11 rowtotal 18 [(11, 8), (22, 2), (8, 2), (27, 1), (3, 1), (21, 1)]
12 rowtotal 39 [(13, 4), (19, 4), (0, 4), (26, 3), (4, 3), (5, 3)]
13 rowtotal 33 [(13, 11), (7, 3), (0, 3), (12, 2), (1, 2), (4, 2)]
14 rowtotal 13 [(14, 5), (10, 3), (12, 1), (1, 1), (3, 1), (19, 1)]
15 rowtotal 12 [(15, 5), (8, 2), (9, 2), (25, 1), (19, 1), (12, 1)]
16 rowtotal 24 [(16, 4), (27, 3), (5, 3), (12, 2), (14, 2), (17, 1)]
17 rowtotal 8 [(20, 3), (19, 2), (12, 1), (1, 1), (10, 1)]
18 rowtotal 20 [(18, 6), (10, 3), (27, 2), (2, 1), (4, 1), (20, 1)]
19 rowtotal 17 [(4, 3), (19, 3), (1, 2), (18, 2), (27, 1), (24, 1)]
20 rowtotal 10 [(20, 6), (13, 1), (12, 1), (2, 1), (16, 1)]
21 rowtotal 6 [(21, 3), (18, 1), (17, 1), (9, 1)]
22 rowtotal 24 [(22, 7), (18, 3), (1, 2), (3, 2), (6, 2), (7, 2)]
23 rowtotal 15 [(23, 6), (8, 2), (11, 1), (21, 1), (18, 1), (6, 1)]
24 rowtotal 12 [(24, 2), (7, 2), (27, 1), (18, 1), (2, 1), (5, 1)]
25 rowtotal 15 [(25, 5), (17, 2), (27, 1), (18, 1), (7, 1), (15, 1)]
26 rowtotal 12 [(26, 4), (0, 1), (20, 1), (16, 1), (18, 1), (3, 1)]
27 rowtotal 17 [(27, 6), (4, 3), (3, 2), (10, 2), (7, 1), (16, 1)]

L0H4 effective OV singular values: [13.878  9.65   2.507  0.969  0.595  0.554  0.483  0.433  0.345  0.295
  0.28   0.228]
Rank 2 L0H4 output subspace: later QK query/key energy fractions
layer 1 [(0, np.float64(0.041), np.float64(0.308)), (1, np.float64(0.067), np.float64(0.569)), (2, np.float64(0.065), np.float64(0.52)), (3, np.float64(0.093), np.float64(0.581)), (4, np.float64(0.076), np.float64(0.545)), (5, np.float64(0.05), np.float64(0.479))]
layer 2 [(0, np.float64(0.006), np.float64(0.048)), (1, np.float64(0.002), np.float64(0.003)), (2, np.float64(0.013), np.float64(0.064)), (3, np.float64(0.016), np.float64(0.088)), (4, np.float64(0.016), np.float64(0.09)), (5, np.float64(0.004), np.float64(0.039))]
layer 3 [(0, np.float64(0.001), np.float64(0.015)), (1, np.float64(0.001), np.float64(0.007)), (2, np.float64(0.002), np.float64(0.023)), (3, np.float64(0.003), np.float64(0.007)), (4, np.float64(0.002), np.float64(0.024)), (5, np.float64(0.001), np.float64(0.003))]
Rank 3 L0H4 output subspace: later QK query/key energy fractions
layer 1 [(0, np.float64(0.061), np.float64(0.342)), (1, np.float64(0.107), np.float64(0.627)), (2, np.float64(0.108), np.float64(0.631)), (3, np.float64(0.137), np.float64(0.677)), (4, np.float64(0.119), np.float64(0.645)), (5, np.float64(0.084), np.float64(0.524))]
layer 2 [(0, np.float64(0.01), np.float64(0.051)), (1, np.float64(0.005), np.float64(0.005)), (2, np.float64(0.014), np.float64(0.07)), (3, np.float64(0.022), np.float64(0.095)), (4, np.float64(0.023), np.float64(0.095)), (5, np.float64(0.006), np.float64(0.041))]
layer 3 [(0, np.float64(0.002), np.float64(0.017)), (1, np.float64(0.003), np.float64(0.009)), (2, np.float64(0.003), np.float64(0.025)), (3, np.float64(0.005), np.float64(0.01)), (4, np.float64(0.004), np.float64(0.025)), (5, np.float64(0.003), np.float64(0.005))]
Rank 6 L0H4 output subspace: later QK query/key energy fractions
layer 1 [(0, np.float64(0.1), np.float64(0.353)), (1, np.float64(0.161), np.float64(0.636)), (2, np.float64(0.175), np.float64(0.642)), (3, np.float64(0.209), np.float64(0.684)), (4, np.float64(0.186), np.float64(0.654)), (5, np.float64(0.135), np.float64(0.534))]
layer 2 [(0, np.float64(0.02), np.float64(0.06)), (1, np.float64(0.019), np.float64(0.013)), (2, np.float64(0.023), np.float64(0.078)), (3, np.float64(0.036), np.float64(0.102)), (4, np.float64(0.032), np.float64(0.105)), (5, np.float64(0.024), np.float64(0.051))]
layer 3 [(0, np.float64(0.012), np.float64(0.026)), (1, np.float64(0.013), np.float64(0.018)), (2, np.float64(0.013), np.float64(0.032)), (3, np.float64(0.018), np.float64(0.018)), (4, np.float64(0.017), np.float64(0.034)), (5, np.float64(0.012), np.float64(0.015))]

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd, eig
from scipy.stats import skew
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d=384
P=np.eye(d)-np.ones((d,d))/d
rs=[]
for h in [1,5]:
    _,_,r=svd(np.asarray(W["W_v.0"][64*h:64*(h+1)],float)@P,full_matrices=False)
    rs.append(r[:28])
ev,U=np.linalg.eigh(sum(r.T@r for r in rs))
U=U[:,np.argsort(ev)[::-1][:28]]
F=np.asarray(W["W_fc.0"],float)*W["ln2_w.0"][None,:]
ica=FastICA(n_components=28,whiten="unit-variance",random_state=17,max_iter=4000,tol=.001)
Z=ica.fit_transform(F@U)
sg=np.where(skew(Z,axis=0)>=0,1.,-1.)
T=U@np.linalg.pinv((ica.mixing_*sg[None,:]).T)
T/=np.linalg.norm(T,axis=0,keepdims=True)

print("L0 character-subspace weight energies by unlabeled ICA axis")
for h in range(6):
    s=slice(64*h,64*(h+1))
    q,k,v=[np.asarray(W[f"W_{n}.0"][s],float) for n in ["q","k","v"]]
    a=q.T@k
    vals=[]
    for name,z in [("query",np.linalg.norm(T.T@a,axis=1)),
                   ("key",np.linalg.norm(a@T,axis=0)),
                   ("value",np.linalg.norm(v@T,axis=0))]:
        ix=np.argsort(z)[::-1][:7]
        vals.append((name,[(int(i),round(z[i],3)) for i in ix]))
    print(h,vals)

print("\nL0H4 top value-input singular vectors: overlap with content and QK5 row spaces")
v=np.asarray(W["W_v.0"][256:320],float)@P
_,ss,vt=svd(v,full_matrices=False)
q=np.asarray(W["W_q.0"][320:384],float)@P
k=np.asarray(W["W_k.0"][320:384],float)@P
_,_,pos=svd(np.concatenate([q,k]),full_matrices=False)
for j in range(6):
    print(j,round(ss[j],3),round(np.linalg.norm(U.T@vt[j])**2,3),
          round(np.linalg.norm(pos[:40]@vt[j])**2,3))

print("\nRelative rotation geometry of L0H1 and L0H5")
print("Weight-matrix powers only; no sequence or attention computation.")
# Exclude the shared value-reading subspace, then whiten a Q/K Gram matrix.
R=P-U@U.T
q5=np.asarray(W["W_q.0"][320:384],float)@R
k5=np.asarray(W["W_k.0"][320:384],float)@R
gram=(q5.T@q5+k5.T@k5)/2
ee,V=np.linalg.eigh(gram)
ix=np.argsort(ee)[::-1]
ee,V=ee[ix],V[:,ix]
for rank in [16,24,32,40]:
    T0=V[:,:rank]/np.sqrt(ee[:rank])[None,:]
    a5=T0.T@(q5.T@k5)@T0
    left,_,right=svd(a5)
    rot=left@right
    q1=np.asarray(W["W_q.0"][64:128],float)@R
    k1=np.asarray(W["W_k.0"][64:128],float)@R
    a1=T0.T@(q1.T@k1)@T0
    co=[]
    for power in range(-4,7):
        rp=np.linalg.matrix_power(rot,power)
        co.append((power,round(np.sum(a1*rp)/(np.linalg.norm(a1)*np.linalg.norm(rp)),3)))
    print("rank",rank,"H5 singular values",np.round(np.linalg.svd(a5,compute_uv=False)[[0,-1]],3),
          "H1 versus H5 polar powers",co)
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 7,
  "turn": "t007",
  "code": "import numpy as np\nfrom scipy.linalg import svd, eig\nfrom scipy.stats import skew\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nev,U=np.linalg.eigh(sum(r.T@r for r in rs))\nU=U[:,np.argsort(ev)[::-1][:28]]\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,max_iter=4000,tol=.001)\nZ=ica.fit_transform(F@U)\nsg=np.where(skew(Z,axis=0)>=0,1.,-1.)\nT=U@np.linalg.pinv((ica.mixing_*sg[None,:]).T)\nT/=np.linalg.norm(T,axis=0,keepdims=True)\n\nprint(\"L0 character-subspace weight energies by unlabeled ICA axis\")\nfor h in range(6):\n    s=slice(64*h,64*(h+1))\n    q,k,v=[np.asarray(W[f\"W_{n}.0\"][s],float) for n in [\"q\",\"k\",\"v\"]]\n    a=q.T@k\n    vals=[]\n    for name,z in [(\"query\",np.linalg.norm(T.T@a,axis=1)),\n                   (\"key\",np.linalg.norm(a@T,axis=0)),\n                   (\"value\",np.linalg.norm(v@T,axis=0))]:\n        ix=np.argsort(z)[::-1][:7]\n        vals.append((name,[(int(i),round(z[i],3)) for i in ix]))\n    print(h,vals)\n\nprint(\"\\nL0H4 top value-input singular vectors: overlap with content and QK5 row spaces\")\nv=np.asarray(W[\"W_v.0\"][256:320],float)@P\n_,ss,vt=svd(v,full_matrices=False)\nq=np.asarray(W[\"W_q.0\"][320:384],float)@P\nk=np.asarray(W[\"W_k.0\"][320:384],float)@P\n_,_,pos=svd(np.concatenate([q,k]),full_matrices=False)\nfor j in range(6):\n    print(j,round(ss[j],3),round(np.linalg.norm(U.T@vt[j])**2,3),\n          round(np.linalg.norm(pos[:40]@vt[j])**2,3))\n\nprint(\"\\nRelative rotation geometry of L0H1 and L0H5\")\nprint(\"Weight-matrix powers only; no sequence or attention computation.\")\n# Exclude the shared value-reading subspace, then whiten a Q/K Gram matrix.\nR=P-U@U.T\nq5=np.asarray(W[\"W_q.0\"][320:384],float)@R\nk5=np.asarray(W[\"W_k.0\"][320:384],float)@R\ngram=(q5.T@q5+k5.T@k5)/2\nee,V=np.linalg.eigh(gram)\nix=np.argsort(ee)[::-1]\nee,V=ee[ix],V[:,ix]\nfor rank in [16,24,32,40]:\n    T0=V[:,:rank]/np.sqrt(ee[:rank])[None,:]\n    a5=T0.T@(q5.T@k5)@T0\n    left,_,right=svd(a5)\n    rot=left@right\n    q1=np.asarray(W[\"W_q.0\"][64:128],float)@R\n    k1=np.asarray(W[\"W_k.0\"][64:128],float)@R\n    a1=T0.T@(q1.T@k1)@T0\n    co=[]\n    for power in range(-4,7):\n        rp=np.linalg.matrix_power(rot,power)\n        co.append((power,round(np.sum(a1*rp)/(np.linalg.norm(a1)*np.linalg.norm(rp)),3)))\n    print(\"rank\",rank,\"H5 singular values\",np.round(np.linalg.svd(a5,compute_uv=False)[[0,-1]],3),\n          \"H1 versus H5 polar powers\",co)",
  "out": "L0 character-subspace weight energies by unlabeled ICA axis\n0 [('query', [(13, np.float64(1.04)), (0, np.float64(0.472)), (4, np.float64(0.429)), (11, np.float64(0.37)), (23, np.float64(0.358)), (20, np.float64(0.301)), (1, np.float64(0.288))]), ('key', [(13, np.float64(2.241)), (0, np.float64(1.236)), (9, np.float64(1.114)), (5, np.float64(0.896)), (18, np.float64(0.868)), (22, np.float64(0.861)), (16, np.float64(0.834))]), ('value', [(13, np.float64(1.22)), (0, np.float64(0.899)), (25, np.float64(0.705)), (2, np.float64(0.624)), (15, np.float64(0.576)), (1, np.float64(0.574)), (26, np.float64(0.571))])]\n1 [('query', [(12, np.float64(11.063)), (21, np.float64(6.311)), (24, np.float64(4.53)), (18, np.float64(4.412)), (3, np.float64(4.007)), (8, np.float64(3.83)), (0, np.float64(3.495))]), ('key', [(12, np.float64(11.364)), (21, np.float64(6.31)), (24, np.float64(4.572)), (18, np.float64(4.199)), (3, np.float64(4.061)), (8, np.float64(3.909)), (0, np.float64(3.878))]), ('value', [(20, np.float64(1.882)), (1, np.float64(1.813)), (27, np.float64(1.79)), (14, np.float64(1.783)), (10, np.float64(1.773)), (9, np.float64(1.77)), (0, np.float64(1.762))])]\n2 [('query', [(26, np.float64(2.172)), (2, np.float64(2.047)), (17, np.float64(1.848)), (21, np.float64(1.558)), (7, np.float64(1.558)), (24, np.float64(1.553)), (18, np.float64(1.211))]), ('key', [(0, np.float64(2.229)), (7, np.float64(2.029)), (26, np.float64(1.936)), (17, np.float64(1.57)), (19, np.float64(1.564)), (25, np.float64(1.556)), (2, np.float64(1.482))]), ('value', [(0, np.float64(1.798)), (25, np.float64(1.437)), (23, np.float64(1.396)), (27, np.float64(1.293)), (15, np.float64(1.142)), (14, np.float64(1.101)), (26, np.float64(1.101))])]\n3 [('query', [(13, np.float64(1.841)), (0, np.float64(1.837)), (21, np.float64(1.177)), (15, np.float64(1.097)), (23, np.float64(1.069)), (24, np.float64(0.991)), (14, np.float64(0.924))]), ('key', [(13, np.float64(3.116)), (20, np.float64(2.433)), (17, np.float64(2.094)), (7, np.float64(1.987)), (1, np.float64(1.798)), (24, np.float64(1.586)), (5, np.float64(1.558))]), ('value', [(15, np.float64(0.865)), (20, np.float64(0.835)), (17, np.float64(0.834)), (13, np.float64(0.817)), (24, np.float64(0.8)), (21, np.float64(0.771)), (14, np.float64(0.768))])]\n4 [('query', [(12, np.float64(0.053)), (24, np.float64(0.05)), (0, np.float64(0.046)), (21, np.float64(0.043)), (18, np.float64(0.042)), (25, np.float64(0.04)), (2, np.float64(0.035))]), ('key', [(24, np.float64(0.076)), (21, np.float64(0.072)), (23, np.float64(0.067)), (17, np.float64(0.063)), (18, np.float64(0.06)), (12, np.float64(0.06)), (15, np.float64(0.057))]), ('value', [(13, np.float64(1.792)), (4, np.float64(1.095)), (16, np.float64(1.085)), (22, np.float64(1.037)), (5, np.float64(1.034)), (1, np.float64(0.977)), (11, np.float64(0.954))])]\n5 [('query', [(12, np.float64(5.833)), (21, np.float64(4.149)), (24, np.float64(3.958)), (17, np.float64(3.514)), (0, np.float64(3.37)), (15, np.float64(3.37)), (2, np.float64(2.768))]), ('key', [(12, np.float64(6.123)), (21, np.float64(4.609)), (24, np.float64(4.561)), (17, np.float64(4.545)), (15, np.float64(3.239)), (0, np.float64(3.224)), (2, np.float64(2.897))]), ('value', [(9, np.float64(2.033)), (1, np.float64(2.003)), (5, np.float64(1.981)), (10, np.float64(1.977)), (8, np.float64(1.901)), (18, np.float64(1.832)), (11, np.float64(1.823))])]\n\nL0H4 top value-input singular vectors: overlap with content and QK5 row spaces\n0 4.101 0.051 0.759\n1 3.34 0.136 0.621\n2 2.538 0.116 0.532\n3 2.115 0.605 0.448\n4 1.785 0.813 0.214\n5 1.547 0.731 0.581\n\nRelative rotation geometry of L0H1 and L0H5\nWeight-matrix powers only; no sequence or attention computation.\nrank 16 H5 singular values [1.063 0.059] H1 versus H5 polar powers [(-4, np.float64(0.231)), (-3, np.float64(-0.37)), (-2, np.float64(-0.09)), (-1, np.float64(0.167)), (0, np.float64(-0.278)), (1, np.float64(0.028)), (2, np.float64(0.623)), (3, np.float64(0.043)), (4, np.float64(-0.384)), (5, np.float64(0.131)), (6, np.float64(0.082))]\nrank 24 H5 singular values [1.272 0.037] H1 versus H5 polar powers [(-4, np.float64(0.24)), (-3, np.float64(-0.43)), (-2, np.float64(-0.111)), (-1, np.float64(0.29)), (0, np.float64(-0.29)), (1, np.float64(-0.031)), (2, np.float64(0.647)), (3, np.float64(-0.016)), (4, np.float64(-0.335)), (5, np.float64(0.197)), (6, np.float64(-0.036))]\nrank 32 H5 singular values [1.518 0.041] H1 versus H5 polar powers [(-4, np.float64(0.066)), (-3, np.float64(-0.104)), (-2, np.float64(-0.159)), (-1, np.float64(0.326)), (0, np.float64(-0.292)), (1, np.float64(0.093)), (2, np.float64(0.36)), (3, np.float64(0.057)), (4, np.float64(-0.195)), (5, np.float64(0.13)), (6, np.float64(-0.031))]\nrank 40 H5 singular values [1.614 0.013] H1 versus H5 polar powers [(-4, np.float64(-0.038)), (-3, np.float64(-0.036)), (-2, np.float64(-0.15)), (-1, np.float64(0.245)), (0, np.float64(-0.26)), (1, np.float64(0.135)), (2, np.float64(0.211)), (3, np.float64(0.061)), (4, np.float64(-0.173)), (5, np.float64(0.185)), (6, np.float64(-0.096))]\n",
  "seconds": 1.258,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "1af8a0b320c892f744b3ee6d6b7fa85829d8fe1f7e50ac7ac52cdb0583417bbb",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
L0 character-subspace weight energies by unlabeled ICA axis
0 [('query', [(13, np.float64(1.04)), (0, np.float64(0.472)), (4, np.float64(0.429)), (11, np.float64(0.37)), (23, np.float64(0.358)), (20, np.float64(0.301)), (1, np.float64(0.288))]), ('key', [(13, np.float64(2.241)), (0, np.float64(1.236)), (9, np.float64(1.114)), (5, np.float64(0.896)), (18, np.float64(0.868)), (22, np.float64(0.861)), (16, np.float64(0.834))]), ('value', [(13, np.float64(1.22)), (0, np.float64(0.899)), (25, np.float64(0.705)), (2, np.float64(0.624)), (15, np.float64(0.576)), (1, np.float64(0.574)), (26, np.float64(0.571))])]
1 [('query', [(12, np.float64(11.063)), (21, np.float64(6.311)), (24, np.float64(4.53)), (18, np.float64(4.412)), (3, np.float64(4.007)), (8, np.float64(3.83)), (0, np.float64(3.495))]), ('key', [(12, np.float64(11.364)), (21, np.float64(6.31)), (24, np.float64(4.572)), (18, np.float64(4.199)), (3, np.float64(4.061)), (8, np.float64(3.909)), (0, np.float64(3.878))]), ('value', [(20, np.float64(1.882)), (1, np.float64(1.813)), (27, np.float64(1.79)), (14, np.float64(1.783)), (10, np.float64(1.773)), (9, np.float64(1.77)), (0, np.float64(1.762))])]
2 [('query', [(26, np.float64(2.172)), (2, np.float64(2.047)), (17, np.float64(1.848)), (21, np.float64(1.558)), (7, np.float64(1.558)), (24, np.float64(1.553)), (18, np.float64(1.211))]), ('key', [(0, np.float64(2.229)), (7, np.float64(2.029)), (26, np.float64(1.936)), (17, np.float64(1.57)), (19, np.float64(1.564)), (25, np.float64(1.556)), (2, np.float64(1.482))]), ('value', [(0, np.float64(1.798)), (25, np.float64(1.437)), (23, np.float64(1.396)), (27, np.float64(1.293)), (15, np.float64(1.142)), (14, np.float64(1.101)), (26, np.float64(1.101))])]
3 [('query', [(13, np.float64(1.841)), (0, np.float64(1.837)), (21, np.float64(1.177)), (15, np.float64(1.097)), (23, np.float64(1.069)), (24, np.float64(0.991)), (14, np.float64(0.924))]), ('key', [(13, np.float64(3.116)), (20, np.float64(2.433)), (17, np.float64(2.094)), (7, np.float64(1.987)), (1, np.float64(1.798)), (24, np.float64(1.586)), (5, np.float64(1.558))]), ('value', [(15, np.float64(0.865)), (20, np.float64(0.835)), (17, np.float64(0.834)), (13, np.float64(0.817)), (24, np.float64(0.8)), (21, np.float64(0.771)), (14, np.float64(0.768))])]
4 [('query', [(12, np.float64(0.053)), (24, np.float64(0.05)), (0, np.float64(0.046)), (21, np.float64(0.043)), (18, np.float64(0.042)), (25, np.float64(0.04)), (2, np.float64(0.035))]), ('key', [(24, np.float64(0.076)), (21, np.float64(0.072)), (23, np.float64(0.067)), (17, np.float64(0.063)), (18, np.float64(0.06)), (12, np.float64(0.06)), (15, np.float64(0.057))]), ('value', [(13, np.float64(1.792)), (4, np.float64(1.095)), (16, np.float64(1.085)), (22, np.float64(1.037)), (5, np.float64(1.034)), (1, np.float64(0.977)), (11, np.float64(0.954))])]
5 [('query', [(12, np.float64(5.833)), (21, np.float64(4.149)), (24, np.float64(3.958)), (17, np.float64(3.514)), (0, np.float64(3.37)), (15, np.float64(3.37)), (2, np.float64(2.768))]), ('key', [(12, np.float64(6.123)), (21, np.float64(4.609)), (24, np.float64(4.561)), (17, np.float64(4.545)), (15, np.float64(3.239)), (0, np.float64(3.224)), (2, np.float64(2.897))]), ('value', [(9, np.float64(2.033)), (1, np.float64(2.003)), (5, np.float64(1.981)), (10, np.float64(1.977)), (8, np.float64(1.901)), (18, np.float64(1.832)), (11, np.float64(1.823))])]

L0H4 top value-input singular vectors: overlap with content and QK5 row spaces
0 4.101 0.051 0.759
1 3.34 0.136 0.621
2 2.538 0.116 0.532
3 2.115 0.605 0.448
4 1.785 0.813 0.214
5 1.547 0.731 0.581

Relative rotation geometry of L0H1 and L0H5
Weight-matrix powers only; no sequence or attention computation.
rank 16 H5 singular values [1.063 0.059] H1 versus H5 polar powers [(-4, np.float64(0.231)), (-3, np.float64(-0.37)), (-2, np.float64(-0.09)), (-1, np.float64(0.167)), (0, np.float64(-0.278)), (1, np.float64(0.028)), (2, np.float64(0.623)), (3, np.float64(0.043)), (4, np.float64(-0.384)), (5, np.float64(0.131)), (6, np.float64(0.082))]
rank 24 H5 singular values [1.272 0.037] H1 versus H5 polar powers [(-4, np.float64(0.24)), (-3, np.float64(-0.43)), (-2, np.float64(-0.111)), (-1, np.float64(0.29)), (0, np.float64(-0.29)), (1, np.float64(-0.031)), (2, np.float64(0.647)), (3, np.float64(-0.016)), (4, np.float64(-0.335)), (5, np.float64(0.197)), (6, np.float64(-0.036))]
rank 32 H5 singular values [1.518 0.041] H1 versus H5 polar powers [(-4, np.float64(0.066)), (-3, np.float64(-0.104)), (-2, np.float64(-0.159)), (-1, np.float64(0.326)), (0, np.float64(-0.292)), (1, np.float64(0.093)), (2, np.float64(0.36)), (3, np.float64(0.057)), (4, np.float64(-0.195)), (5, np.float64(0.13)), (6, np.float64(-0.031))]
rank 40 H5 singular values [1.614 0.013] H1 versus H5 polar powers [(-4, np.float64(-0.038)), (-3, np.float64(-0.036)), (-2, np.float64(-0.15)), (-1, np.float64(0.245)), (0, np.float64(-0.26)), (1, np.float64(0.135)), (2, np.float64(0.211)), (3, np.float64(0.061)), (4, np.float64(-0.173)), (5, np.float64(0.185)), (6, np.float64(-0.096))]


========================================================================
TURN t008
MESSAGE SENT TO CODEX
OUTPUT of run 7 of at most 100:
```
L0 character-subspace weight energies by unlabeled ICA axis
0 [('query', [(13, np.float64(1.04)), (0, np.float64(0.472)), (4, np.float64(0.429)), (11, np.float64(0.37)), (23, np.float64(0.358)), (20, np.float64(0.301)), (1, np.float64(0.288))]), ('key', [(13, np.float64(2.241)), (0, np.float64(1.236)), (9, np.float64(1.114)), (5, np.float64(0.896)), (18, np.float64(0.868)), (22, np.float64(0.861)), (16, np.float64(0.834))]), ('value', [(13, np.float64(1.22)), (0, np.float64(0.899)), (25, np.float64(0.705)), (2, np.float64(0.624)), (15, np.float64(0.576)), (1, np.float64(0.574)), (26, np.float64(0.571))])]
1 [('query', [(12, np.float64(11.063)), (21, np.float64(6.311)), (24, np.float64(4.53)), (18, np.float64(4.412)), (3, np.float64(4.007)), (8, np.float64(3.83)), (0, np.float64(3.495))]), ('key', [(12, np.float64(11.364)), (21, np.float64(6.31)), (24, np.float64(4.572)), (18, np.float64(4.199)), (3, np.float64(4.061)), (8, np.float64(3.909)), (0, np.float64(3.878))]), ('value', [(20, np.float64(1.882)), (1, np.float64(1.813)), (27, np.float64(1.79)), (14, np.float64(1.783)), (10, np.float64(1.773)), (9, np.float64(1.77)), (0, np.float64(1.762))])]
2 [('query', [(26, np.float64(2.172)), (2, np.float64(2.047)), (17, np.float64(1.848)), (21, np.float64(1.558)), (7, np.float64(1.558)), (24, np.float64(1.553)), (18, np.float64(1.211))]), ('key', [(0, np.float64(2.229)), (7, np.float64(2.029)), (26, np.float64(1.936)), (17, np.float64(1.57)), (19, np.float64(1.564)), (25, np.float64(1.556)), (2, np.float64(1.482))]), ('value', [(0, np.float64(1.798)), (25, np.float64(1.437)), (23, np.float64(1.396)), (27, np.float64(1.293)), (15, np.float64(1.142)), (14, np.float64(1.101)), (26, np.float64(1.101))])]
3 [('query', [(13, np.float64(1.841)), (0, np.float64(1.837)), (21, np.float64(1.177)), (15, np.float64(1.097)), (23, np.float64(1.069)), (24, np.float64(0.991)), (14, np.float64(0.924))]), ('key', [(13, np.float64(3.116)), (20, np.float64(2.433)), (17, np.float64(2.094)), (7, np.float64(1.987)), (1, np.float64(1.798)), (24, np.float64(1.586)), (5, np.float64(1.558))]), ('value', [(15, np.float64(0.865)), (20, np.float64(0.835)), (17, np.float64(0.834)), (13, np.float64(0.817)), (24, np.float64(0.8)), (21, np.float64(0.771)), (14, np.float64(0.768))])]
4 [('query', [(12, np.float64(0.053)), (24, np.float64(0.05)), (0, np.float64(0.046)), (21, np.float64(0.043)), (18, np.float64(0.042)), (25, np.float64(0.04)), (2, np.float64(0.035))]), ('key', [(24, np.float64(0.076)), (21, np.float64(0.072)), (23, np.float64(0.067)), (17, np.float64(0.063)), (18, np.float64(0.06)), (12, np.float64(0.06)), (15, np.float64(0.057))]), ('value', [(13, np.float64(1.792)), (4, np.float64(1.095)), (16, np.float64(1.085)), (22, np.float64(1.037)), (5, np.float64(1.034)), (1, np.float64(0.977)), (11, np.float64(0.954))])]
5 [('query', [(12, np.float64(5.833)), (21, np.float64(4.149)), (24, np.float64(3.958)), (17, np.float64(3.514)), (0, np.float64(3.37)), (15, np.float64(3.37)), (2, np.float64(2.768))]), ('key', [(12, np.float64(6.123)), (21, np.float64(4.609)), (24, np.float64(4.561)), (17, np.float64(4.545)), (15, np.float64(3.239)), (0, np.float64(3.224)), (2, np.float64(2.897))]), ('value', [(9, np.float64(2.033)), (1, np.float64(2.003)), (5, np.float64(1.981)), (10, np.float64(1.977)), (8, np.float64(1.901)), (18, np.float64(1.832)), (11, np.float64(1.823))])]

L0H4 top value-input singular vectors: overlap with content and QK5 row spaces
0 4.101 0.051 0.759
1 3.34 0.136 0.621
2 2.538 0.116 0.532
3 2.115 0.605 0.448
4 1.785 0.813 0.214
5 1.547 0.731 0.581

Relative rotation geometry of L0H1 and L0H5
Weight-matrix powers only; no sequence or attention computation.
rank 16 H5 singular values [1.063 0.059] H1 versus H5 polar powers [(-4, np.float64(0.231)), (-3, np.float64(-0.37)), (-2, np.float64(-0.09)), (-1, np.float64(0.167)), (0, np.float64(-0.278)), (1, np.float64(0.028)), (2, np.float64(0.623)), (3, np.float64(0.043)), (4, np.float64(-0.384)), (5, np.float64(0.131)), (6, np.float64(0.082))]
rank 24 H5 singular values [1.272 0.037] H1 versus H5 polar powers [(-4, np.float64(0.24)), (-3, np.float64(-0.43)), (-2, np.float64(-0.111)), (-1, np.float64(0.29)), (0, np.float64(-0.29)), (1, np.float64(-0.031)), (2, np.float64(0.647)), (3, np.float64(-0.016)), (4, np.float64(-0.335)), (5, np.float64(0.197)), (6, np.float64(-0.036))]
rank 32 H5 singular values [1.518 0.041] H1 versus H5 polar powers [(-4, np.float64(0.066)), (-3, np.float64(-0.104)), (-2, np.float64(-0.159)), (-1, np.float64(0.326)), (0, np.float64(-0.292)), (1, np.float64(0.093)), (2, np.float64(0.36)), (3, np.float64(0.057)), (4, np.float64(-0.195)), (5, np.float64(0.13)), (6, np.float64(-0.031))]
rank 40 H5 singular values [1.614 0.013] H1 versus H5 polar powers [(-4, np.float64(-0.038)), (-3, np.float64(-0.036)), (-2, np.float64(-0.15)), (-1, np.float64(0.245)), (0, np.float64(-0.26)), (1, np.float64(0.135)), (2, np.float64(0.211)), (3, np.float64(0.061)), (4, np.float64(-0.173)), (5, np.float64(0.185)), (6, np.float64(-0.096))]

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd, eigvals
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d=384
P=np.eye(d)-np.ones((d,d))/d
rs=[]
for h in [1,5]:
    _,_,r=svd(np.asarray(W["W_v.0"][64*h:64*(h+1)],float)@P,full_matrices=False)
    rs.append(r[:28])
ee,U=np.linalg.eigh(sum(r.T@r for r in rs))
U=U[:,np.argsort(ee)[::-1][:28]]
R=P-U@U.T
q5=np.asarray(W["W_q.0"][320:384],float)@R
k5=np.asarray(W["W_k.0"][320:384],float)@R
gg=(q5.T@q5+k5.T@k5)/2
ee,V=np.linalg.eigh(gg)
ix=np.argsort(ee)[::-1]
ee,V=ee[ix],V[:,ix]
T=V[:,:24]/np.sqrt(ee[:24])[None,:]
a5=T.T@(q5.T@k5)@T
left,_,right=svd(a5)
rot=left@right
powers=list(range(-3,9))

print("L0 position-subspace bilinear forms versus powers of the H5 polar factor")
print("Power columns:",powers)
for h in range(6):
    s=slice(64*h,64*(h+1))
    q,k=[np.asarray(W[f"W_{n}.0"][s],float)@R for n in ["q","k"]]
    a=T.T@(q.T@k)@T
    sims=[]
    for p in powers:
        rp=np.linalg.matrix_power(rot,p)
        sims.append(np.sum(a*rp)/(np.linalg.norm(a)*np.linalg.norm(rp)))
    print(f"L0H{h}",np.round(sims,3))

A={};B={}
for l in range(4):
    scale=np.asarray(W[f"ln1_w.{l}"],float)
    for h in range(6):
        s=slice(64*h,64*(h+1))
        q,k,v=[np.asarray(W[f"W_{n}.{l}"][s],float) for n in ["q","k","v"]]
        A[l,h]=P@(scale[:,None]*(q.T@k)*scale[None,:])@P
        B[l,h]=np.asarray(W[f"W_o.{l}"][:,s],float)@(v*scale[None,:])@P

print("\nMatching geometry restricted to initial shared value-reading subspace")
print("For U.T QK_l OV_0H5 U: trace/Frobenius, trace/nuclear, symmetric fraction, largest eigenvalues")
for l in range(1,4):
    for h in range(6):
        c=U.T@A[l,h]@B[0,5]@U
        ss=np.linalg.svd(c,compute_uv=False)
        ev=eigvals(c)
        ev=ev[np.argsort(np.abs(ev))[::-1]][:4]
        print(f"L{l}H{h}",
              round(np.trace(c)/np.linalg.norm(c),3),
              round(np.trace(c)/ss.sum(),3),
              round(np.linalg.norm((c+c.T)/2)**2/np.linalg.norm(c)**2,3),
              np.round(ev,3))

print("\nQK affinity of L1 heads: pairwise Frobenius cosines")
print(np.round([[np.sum(A[1,h]*A[1,k])/
                 (np.linalg.norm(A[1,h])*np.linalg.norm(A[1,k]))
                 for k in range(6)] for h in range(6)],3))

print("\nDirect positional-weight overlap at later layers")
# This tests only an unchanged residual-coordinate subspace, not layer outputs.
for l in range(1,4):
    for h in range(6):
        c=T.T@A[l,h]@T
        sims=[]
        for p in powers:
            rp=np.linalg.matrix_power(rot,p)
            sims.append(np.sum(c*rp)/(np.linalg.norm(c)*np.linalg.norm(rp)))
        k=int(np.argmax(sims))
        print(f"L{l}H{h}","best polar power",powers[k],
              "cosine",round(sims[k],3),
              "at powers 0,1,2",np.round(sims[3:6],3))
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 8,
  "turn": "t008",
  "code": "import numpy as np\nfrom scipy.linalg import svd, eigvals\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nee,U=np.linalg.eigh(sum(r.T@r for r in rs))\nU=U[:,np.argsort(ee)[::-1][:28]]\nR=P-U@U.T\nq5=np.asarray(W[\"W_q.0\"][320:384],float)@R\nk5=np.asarray(W[\"W_k.0\"][320:384],float)@R\ngg=(q5.T@q5+k5.T@k5)/2\nee,V=np.linalg.eigh(gg)\nix=np.argsort(ee)[::-1]\nee,V=ee[ix],V[:,ix]\nT=V[:,:24]/np.sqrt(ee[:24])[None,:]\na5=T.T@(q5.T@k5)@T\nleft,_,right=svd(a5)\nrot=left@right\npowers=list(range(-3,9))\n\nprint(\"L0 position-subspace bilinear forms versus powers of the H5 polar factor\")\nprint(\"Power columns:\",powers)\nfor h in range(6):\n    s=slice(64*h,64*(h+1))\n    q,k=[np.asarray(W[f\"W_{n}.0\"][s],float)@R for n in [\"q\",\"k\"]]\n    a=T.T@(q.T@k)@T\n    sims=[]\n    for p in powers:\n        rp=np.linalg.matrix_power(rot,p)\n        sims.append(np.sum(a*rp)/(np.linalg.norm(a)*np.linalg.norm(rp)))\n    print(f\"L0H{h}\",np.round(sims,3))\n\nA={};B={}\nfor l in range(4):\n    scale=np.asarray(W[f\"ln1_w.{l}\"],float)\n    for h in range(6):\n        s=slice(64*h,64*(h+1))\n        q,k,v=[np.asarray(W[f\"W_{n}.{l}\"][s],float) for n in [\"q\",\"k\",\"v\"]]\n        A[l,h]=P@(scale[:,None]*(q.T@k)*scale[None,:])@P\n        B[l,h]=np.asarray(W[f\"W_o.{l}\"][:,s],float)@(v*scale[None,:])@P\n\nprint(\"\\nMatching geometry restricted to initial shared value-reading subspace\")\nprint(\"For U.T QK_l OV_0H5 U: trace/Frobenius, trace/nuclear, symmetric fraction, largest eigenvalues\")\nfor l in range(1,4):\n    for h in range(6):\n        c=U.T@A[l,h]@B[0,5]@U\n        ss=np.linalg.svd(c,compute_uv=False)\n        ev=eigvals(c)\n        ev=ev[np.argsort(np.abs(ev))[::-1]][:4]\n        print(f\"L{l}H{h}\",\n              round(np.trace(c)/np.linalg.norm(c),3),\n              round(np.trace(c)/ss.sum(),3),\n              round(np.linalg.norm((c+c.T)/2)**2/np.linalg.norm(c)**2,3),\n              np.round(ev,3))\n\nprint(\"\\nQK affinity of L1 heads: pairwise Frobenius cosines\")\nprint(np.round([[np.sum(A[1,h]*A[1,k])/\n                 (np.linalg.norm(A[1,h])*np.linalg.norm(A[1,k]))\n                 for k in range(6)] for h in range(6)],3))\n\nprint(\"\\nDirect positional-weight overlap at later layers\")\n# This tests only an unchanged residual-coordinate subspace, not layer outputs.\nfor l in range(1,4):\n    for h in range(6):\n        c=T.T@A[l,h]@T\n        sims=[]\n        for p in powers:\n            rp=np.linalg.matrix_power(rot,p)\n            sims.append(np.sum(c*rp)/(np.linalg.norm(c)*np.linalg.norm(rp)))\n        k=int(np.argmax(sims))\n        print(f\"L{l}H{h}\",\"best polar power\",powers[k],\n              \"cosine\",round(sims[k],3),\n              \"at powers 0,1,2\",np.round(sims[3:6],3))",
  "out": "L0 position-subspace bilinear forms versus powers of the H5 polar factor\nPower columns: [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]\nL0H0 [ 0.027 -0.106 -0.067 -0.135  0.054  0.035  0.105 -0.012  0.02  -0.116\n -0.007 -0.033]\nL0H1 [-0.43  -0.111  0.29  -0.29  -0.031  0.647 -0.016 -0.335  0.197 -0.036\n -0.257  0.24 ]\nL0H2 [-0.113  0.119  0.157  0.023  0.034 -0.131 -0.213 -0.056  0.091  0.1\n  0.185  0.054]\nL0H3 [ 0.084  0.027 -0.152 -0.302 -0.123  0.03   0.086  0.211  0.138 -0.098\n -0.132 -0.093]\nL0H4 [-0.083 -0.033  0.168 -0.253  0.03   0.08  -0.086 -0.026  0.173 -0.177\n  0.087  0.046]\nL0H5 [-0.08   0.027 -0.328  0.18   0.956  0.18  -0.328  0.027 -0.08  -0.291\n  0.176  0.11 ]\n\nMatching geometry restricted to initial shared value-reading subspace\nFor U.T QK_l OV_0H5 U: trace/Frobenius, trace/nuclear, symmetric fraction, largest eigenvalues\nL1H0 -0.282 -0.081 0.554 [-0.07 +0.j    -0.056+0.j     0.051+0.j    -0.034+0.034j]\nL1H1 0.685 0.193 0.555 [0.256+0.j    0.162+0.j    0.012+0.118j 0.012-0.118j]\nL1H2 -0.241 -0.066 0.565 [-0.105+0.j    -0.073+0.012j -0.073-0.012j  0.069+0.j   ]\nL1H3 0.554 0.155 0.582 [ 0.134+0.036j  0.134-0.036j -0.138+0.j     0.082+0.066j]\nL1H4 0.437 0.123 0.564 [-0.115+0.j     0.098+0.046j  0.098-0.046j  0.074+0.j   ]\nL1H5 0.513 0.152 0.613 [0.35 +0.j    0.153+0.j    0.016+0.097j 0.016-0.097j]\nL2H0 -0.229 -0.059 0.506 [-0.174+0.j    -0.082+0.108j -0.082-0.108j  0.128+0.j   ]\nL2H1 2.954 0.755 0.766 [0.412+0.j    0.381+0.139j 0.381-0.139j 0.37 +0.024j]\nL2H2 -1.807 -0.476 0.624 [-0.396+0.2j   -0.396-0.2j   -0.357+0.j    -0.263+0.175j]\nL2H3 -0.194 -0.049 0.527 [ 0.169+0.j    -0.144+0.j     0.038+0.119j  0.038-0.119j]\nL2H4 0.039 0.01 0.555 [ 0.192+0.j    -0.136+0.026j -0.136-0.026j -0.083+0.103j]\nL2H5 0.485 0.125 0.53 [0.106+0.013j 0.106-0.013j 0.009+0.09j  0.009-0.09j ]\nL3H0 1.353 0.343 0.62 [0.27 +0.j    0.225+0.j    0.17 +0.067j 0.17 -0.067j]\nL3H1 2.764 0.698 0.711 [0.32 +0.j    0.293+0.j    0.234+0.067j 0.234-0.067j]\nL3H2 -0.095 -0.024 0.515 [-0.131+0.048j -0.131-0.048j  0.109+0.084j  0.109-0.084j]\nL3H3 0.039 0.01 0.488 [-0.02 +0.057j -0.02 -0.057j  0.051+0.021j  0.051-0.021j]\nL3H4 0.603 0.155 0.523 [0.11 +0.042j 0.11 -0.042j 0.099+0.j    0.034+0.085j]\nL3H5 2.947 0.753 0.753 [0.468+0.015j 0.468-0.015j 0.359+0.111j 0.359-0.111j]\n\nQK affinity of L1 heads: pairwise Frobenius cosines\n[[1.    0.302 0.393 0.327 0.338 0.266]\n [0.302 1.    0.524 0.591 0.568 0.566]\n [0.393 0.524 1.    0.538 0.578 0.45 ]\n [0.327 0.591 0.538 1.    0.607 0.524]\n [0.338 0.568 0.578 0.607 1.    0.479]\n [0.266 0.566 0.45  0.524 0.479 1.   ]]\n\nDirect positional-weight overlap at later layers\nL1H0 best polar power 1 cosine 0.054 at powers 0,1,2 [0.004 0.054 0.038]\nL1H1 best polar power 2 cosine 0.077 at powers 0,1,2 [-0.074 -0.003  0.077]\nL1H2 best polar power 3 cosine 0.081 at powers 0,1,2 [-0.052  0.018  0.063]\nL1H3 best polar power 2 cosine 0.064 at powers 0,1,2 [-0.068 -0.004  0.064]\nL1H4 best polar power 2 cosine 0.101 at powers 0,1,2 [-0.05  -0.024  0.101]\nL1H5 best polar power 2 cosine 0.094 at powers 0,1,2 [-0.109 -0.048  0.094]\nL2H0 best polar power 1 cosine 0.103 at powers 0,1,2 [-0.01   0.103  0.012]\nL2H1 best polar power 0 cosine 0.331 at powers 0,1,2 [0.331 0.    0.026]\nL2H2 best polar power 0 cosine 0.192 at powers 0,1,2 [ 0.192  0.022 -0.079]\nL2H3 best polar power 1 cosine 0.076 at powers 0,1,2 [0.022 0.076 0.046]\nL2H4 best polar power 0 cosine 0.047 at powers 0,1,2 [0.047 0.01  0.017]\nL2H5 best polar power 8 cosine 0.054 at powers 0,1,2 [ 0.007 -0.082  0.04 ]\nL3H0 best polar power 0 cosine 0.161 at powers 0,1,2 [ 0.161 -0.026 -0.003]\nL3H1 best polar power 0 cosine 0.258 at powers 0,1,2 [ 0.258  0.073 -0.103]\nL3H2 best polar power 0 cosine 0.115 at powers 0,1,2 [0.115 0.018 0.015]\nL3H3 best polar power 2 cosine 0.083 at powers 0,1,2 [-0.065 -0.048  0.083]\nL3H4 best polar power 8 cosine 0.046 at powers 0,1,2 [-0.035  0.004  0.028]\nL3H5 best polar power 0 cosine 0.589 at powers 0,1,2 [ 0.589  0.011 -0.083]\n",
  "seconds": 0.782,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "3ea8044f534032a8d766145020589f34f0d8cdee92f27ab3b97608790083c5df",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
L0 position-subspace bilinear forms versus powers of the H5 polar factor
Power columns: [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]
L0H0 [ 0.027 -0.106 -0.067 -0.135  0.054  0.035  0.105 -0.012  0.02  -0.116
 -0.007 -0.033]
L0H1 [-0.43  -0.111  0.29  -0.29  -0.031  0.647 -0.016 -0.335  0.197 -0.036
 -0.257  0.24 ]
L0H2 [-0.113  0.119  0.157  0.023  0.034 -0.131 -0.213 -0.056  0.091  0.1
  0.185  0.054]
L0H3 [ 0.084  0.027 -0.152 -0.302 -0.123  0.03   0.086  0.211  0.138 -0.098
 -0.132 -0.093]
L0H4 [-0.083 -0.033  0.168 -0.253  0.03   0.08  -0.086 -0.026  0.173 -0.177
  0.087  0.046]
L0H5 [-0.08   0.027 -0.328  0.18   0.956  0.18  -0.328  0.027 -0.08  -0.291
  0.176  0.11 ]

Matching geometry restricted to initial shared value-reading subspace
For U.T QK_l OV_0H5 U: trace/Frobenius, trace/nuclear, symmetric fraction, largest eigenvalues
L1H0 -0.282 -0.081 0.554 [-0.07 +0.j    -0.056+0.j     0.051+0.j    -0.034+0.034j]
L1H1 0.685 0.193 0.555 [0.256+0.j    0.162+0.j    0.012+0.118j 0.012-0.118j]
L1H2 -0.241 -0.066 0.565 [-0.105+0.j    -0.073+0.012j -0.073-0.012j  0.069+0.j   ]
L1H3 0.554 0.155 0.582 [ 0.134+0.036j  0.134-0.036j -0.138+0.j     0.082+0.066j]
L1H4 0.437 0.123 0.564 [-0.115+0.j     0.098+0.046j  0.098-0.046j  0.074+0.j   ]
L1H5 0.513 0.152 0.613 [0.35 +0.j    0.153+0.j    0.016+0.097j 0.016-0.097j]
L2H0 -0.229 -0.059 0.506 [-0.174+0.j    -0.082+0.108j -0.082-0.108j  0.128+0.j   ]
L2H1 2.954 0.755 0.766 [0.412+0.j    0.381+0.139j 0.381-0.139j 0.37 +0.024j]
L2H2 -1.807 -0.476 0.624 [-0.396+0.2j   -0.396-0.2j   -0.357+0.j    -0.263+0.175j]
L2H3 -0.194 -0.049 0.527 [ 0.169+0.j    -0.144+0.j     0.038+0.119j  0.038-0.119j]
L2H4 0.039 0.01 0.555 [ 0.192+0.j    -0.136+0.026j -0.136-0.026j -0.083+0.103j]
L2H5 0.485 0.125 0.53 [0.106+0.013j 0.106-0.013j 0.009+0.09j  0.009-0.09j ]
L3H0 1.353 0.343 0.62 [0.27 +0.j    0.225+0.j    0.17 +0.067j 0.17 -0.067j]
L3H1 2.764 0.698 0.711 [0.32 +0.j    0.293+0.j    0.234+0.067j 0.234-0.067j]
L3H2 -0.095 -0.024 0.515 [-0.131+0.048j -0.131-0.048j  0.109+0.084j  0.109-0.084j]
L3H3 0.039 0.01 0.488 [-0.02 +0.057j -0.02 -0.057j  0.051+0.021j  0.051-0.021j]
L3H4 0.603 0.155 0.523 [0.11 +0.042j 0.11 -0.042j 0.099+0.j    0.034+0.085j]
L3H5 2.947 0.753 0.753 [0.468+0.015j 0.468-0.015j 0.359+0.111j 0.359-0.111j]

QK affinity of L1 heads: pairwise Frobenius cosines
[[1.    0.302 0.393 0.327 0.338 0.266]
 [0.302 1.    0.524 0.591 0.568 0.566]
 [0.393 0.524 1.    0.538 0.578 0.45 ]
 [0.327 0.591 0.538 1.    0.607 0.524]
 [0.338 0.568 0.578 0.607 1.    0.479]
 [0.266 0.566 0.45  0.524 0.479 1.   ]]

Direct positional-weight overlap at later layers
L1H0 best polar power 1 cosine 0.054 at powers 0,1,2 [0.004 0.054 0.038]
L1H1 best polar power 2 cosine 0.077 at powers 0,1,2 [-0.074 -0.003  0.077]
L1H2 best polar power 3 cosine 0.081 at powers 0,1,2 [-0.052  0.018  0.063]
L1H3 best polar power 2 cosine 0.064 at powers 0,1,2 [-0.068 -0.004  0.064]
L1H4 best polar power 2 cosine 0.101 at powers 0,1,2 [-0.05  -0.024  0.101]
L1H5 best polar power 2 cosine 0.094 at powers 0,1,2 [-0.109 -0.048  0.094]
L2H0 best polar power 1 cosine 0.103 at powers 0,1,2 [-0.01   0.103  0.012]
L2H1 best polar power 0 cosine 0.331 at powers 0,1,2 [0.331 0.    0.026]
L2H2 best polar power 0 cosine 0.192 at powers 0,1,2 [ 0.192  0.022 -0.079]
L2H3 best polar power 1 cosine 0.076 at powers 0,1,2 [0.022 0.076 0.046]
L2H4 best polar power 0 cosine 0.047 at powers 0,1,2 [0.047 0.01  0.017]
L2H5 best polar power 8 cosine 0.054 at powers 0,1,2 [ 0.007 -0.082  0.04 ]
L3H0 best polar power 0 cosine 0.161 at powers 0,1,2 [ 0.161 -0.026 -0.003]
L3H1 best polar power 0 cosine 0.258 at powers 0,1,2 [ 0.258  0.073 -0.103]
L3H2 best polar power 0 cosine 0.115 at powers 0,1,2 [0.115 0.018 0.015]
L3H3 best polar power 2 cosine 0.083 at powers 0,1,2 [-0.065 -0.048  0.083]
L3H4 best polar power 8 cosine 0.046 at powers 0,1,2 [-0.035  0.004  0.028]
L3H5 best polar power 0 cosine 0.589 at powers 0,1,2 [ 0.589  0.011 -0.083]


========================================================================
TURN t009
MESSAGE SENT TO CODEX
OUTPUT of run 8 of at most 100:
```
L0 position-subspace bilinear forms versus powers of the H5 polar factor
Power columns: [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]
L0H0 [ 0.027 -0.106 -0.067 -0.135  0.054  0.035  0.105 -0.012  0.02  -0.116
 -0.007 -0.033]
L0H1 [-0.43  -0.111  0.29  -0.29  -0.031  0.647 -0.016 -0.335  0.197 -0.036
 -0.257  0.24 ]
L0H2 [-0.113  0.119  0.157  0.023  0.034 -0.131 -0.213 -0.056  0.091  0.1
  0.185  0.054]
L0H3 [ 0.084  0.027 -0.152 -0.302 -0.123  0.03   0.086  0.211  0.138 -0.098
 -0.132 -0.093]
L0H4 [-0.083 -0.033  0.168 -0.253  0.03   0.08  -0.086 -0.026  0.173 -0.177
  0.087  0.046]
L0H5 [-0.08   0.027 -0.328  0.18   0.956  0.18  -0.328  0.027 -0.08  -0.291
  0.176  0.11 ]

Matching geometry restricted to initial shared value-reading subspace
For U.T QK_l OV_0H5 U: trace/Frobenius, trace/nuclear, symmetric fraction, largest eigenvalues
L1H0 -0.282 -0.081 0.554 [-0.07 +0.j    -0.056+0.j     0.051+0.j    -0.034+0.034j]
L1H1 0.685 0.193 0.555 [0.256+0.j    0.162+0.j    0.012+0.118j 0.012-0.118j]
L1H2 -0.241 -0.066 0.565 [-0.105+0.j    -0.073+0.012j -0.073-0.012j  0.069+0.j   ]
L1H3 0.554 0.155 0.582 [ 0.134+0.036j  0.134-0.036j -0.138+0.j     0.082+0.066j]
L1H4 0.437 0.123 0.564 [-0.115+0.j     0.098+0.046j  0.098-0.046j  0.074+0.j   ]
L1H5 0.513 0.152 0.613 [0.35 +0.j    0.153+0.j    0.016+0.097j 0.016-0.097j]
L2H0 -0.229 -0.059 0.506 [-0.174+0.j    -0.082+0.108j -0.082-0.108j  0.128+0.j   ]
L2H1 2.954 0.755 0.766 [0.412+0.j    0.381+0.139j 0.381-0.139j 0.37 +0.024j]
L2H2 -1.807 -0.476 0.624 [-0.396+0.2j   -0.396-0.2j   -0.357+0.j    -0.263+0.175j]
L2H3 -0.194 -0.049 0.527 [ 0.169+0.j    -0.144+0.j     0.038+0.119j  0.038-0.119j]
L2H4 0.039 0.01 0.555 [ 0.192+0.j    -0.136+0.026j -0.136-0.026j -0.083+0.103j]
L2H5 0.485 0.125 0.53 [0.106+0.013j 0.106-0.013j 0.009+0.09j  0.009-0.09j ]
L3H0 1.353 0.343 0.62 [0.27 +0.j    0.225+0.j    0.17 +0.067j 0.17 -0.067j]
L3H1 2.764 0.698 0.711 [0.32 +0.j    0.293+0.j    0.234+0.067j 0.234-0.067j]
L3H2 -0.095 -0.024 0.515 [-0.131+0.048j -0.131-0.048j  0.109+0.084j  0.109-0.084j]
L3H3 0.039 0.01 0.488 [-0.02 +0.057j -0.02 -0.057j  0.051+0.021j  0.051-0.021j]
L3H4 0.603 0.155 0.523 [0.11 +0.042j 0.11 -0.042j 0.099+0.j    0.034+0.085j]
L3H5 2.947 0.753 0.753 [0.468+0.015j 0.468-0.015j 0.359+0.111j 0.359-0.111j]

QK affinity of L1 heads: pairwise Frobenius cosines
[[1.    0.302 0.393 0.327 0.338 0.266]
 [0.302 1.    0.524 0.591 0.568 0.566]
 [0.393 0.524 1.    0.538 0.578 0.45 ]
 [0.327 0.591 0.538 1.    0.607 0.524]
 [0.338 0.568 0.578 0.607 1.    0.479]
 [0.266 0.566 0.45  0.524 0.479 1.   ]]

Direct positional-weight overlap at later layers
L1H0 best polar power 1 cosine 0.054 at powers 0,1,2 [0.004 0.054 0.038]
L1H1 best polar power 2 cosine 0.077 at powers 0,1,2 [-0.074 -0.003  0.077]
L1H2 best polar power 3 cosine 0.081 at powers 0,1,2 [-0.052  0.018  0.063]
L1H3 best polar power 2 cosine 0.064 at powers 0,1,2 [-0.068 -0.004  0.064]
L1H4 best polar power 2 cosine 0.101 at powers 0,1,2 [-0.05  -0.024  0.101]
L1H5 best polar power 2 cosine 0.094 at powers 0,1,2 [-0.109 -0.048  0.094]
L2H0 best polar power 1 cosine 0.103 at powers 0,1,2 [-0.01   0.103  0.012]
L2H1 best polar power 0 cosine 0.331 at powers 0,1,2 [0.331 0.    0.026]
L2H2 best polar power 0 cosine 0.192 at powers 0,1,2 [ 0.192  0.022 -0.079]
L2H3 best polar power 1 cosine 0.076 at powers 0,1,2 [0.022 0.076 0.046]
L2H4 best polar power 0 cosine 0.047 at powers 0,1,2 [0.047 0.01  0.017]
L2H5 best polar power 8 cosine 0.054 at powers 0,1,2 [ 0.007 -0.082  0.04 ]
L3H0 best polar power 0 cosine 0.161 at powers 0,1,2 [ 0.161 -0.026 -0.003]
L3H1 best polar power 0 cosine 0.258 at powers 0,1,2 [ 0.258  0.073 -0.103]
L3H2 best polar power 0 cosine 0.115 at powers 0,1,2 [0.115 0.018 0.015]
L3H3 best polar power 2 cosine 0.083 at powers 0,1,2 [-0.065 -0.048  0.083]
L3H4 best polar power 8 cosine 0.046 at powers 0,1,2 [-0.035  0.004  0.028]
L3H5 best polar power 0 cosine 0.589 at powers 0,1,2 [ 0.589  0.011 -0.083]

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd
from scipy.stats import skew
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

d=384
P=np.eye(d)-np.ones((d,d))/d
rs=[]
for h in [1,5]:
    _,_,r=svd(np.asarray(W["W_v.0"][64*h:64*(h+1)],float)@P,full_matrices=False)
    rs.append(r[:28])
ee,U=np.linalg.eigh(sum(r.T@r for r in rs))
U=U[:,np.argsort(ee)[::-1][:28]]
F=np.asarray(W["W_fc.0"],float)*W["ln2_w.0"][None,:]
ica=FastICA(n_components=28,whiten="unit-variance",random_state=17,max_iter=4000,tol=.001)
Z=ica.fit_transform(F@U)
Z*=np.where(skew(Z,axis=0)>=0,1.,-1.)[None,:]

# A dictionary made entirely from existing MLP output-weight columns.
# These are not inferred activations or a forward pass.
O=np.asarray(W["W_proj.0"],float)
C=np.zeros((d,28))
for k in range(28):
    ids=np.argsort(np.abs(Z[:,k]))[-3:]
    coeff=Z[ids,k]
    C[:,k]=O[:,ids]@coeff/np.linalg.norm(coeff)
C/=np.linalg.norm(C,axis=0,keepdims=True)

out_u,_,_=svd(np.asarray(W["W_proj.3"],float),full_matrices=False)
out_u=out_u[:,:28]
J=out_u.T@C
Bs={}
for l,h in [(2,1),(3,0),(3,1),(3,5)]:
    s=slice(64*h,64*(h+1))
    Bs[l,h]=np.asarray(W[f"W_o.{l}"][:,s],float)@(
        np.asarray(W[f"W_v.{l}"][s],float)*W[f"ln1_w.{l}"][None,:])@P
M={key:out_u.T@b@C for key,b in Bs.items()}
print("Alignment of copy-candidate output WEIGHT dictionaries")
keys=list(M)
for key in keys:
    print(key,[(key2,round(np.sum(M[key]*M[key2])/
                          (np.linalg.norm(M[key])*np.linalg.norm(M[key2])),3))
               for key2 in keys])

print("\nDictionary singular values and output-subspace fractions")
print("Initial dictionary:",np.round(np.linalg.svd(C,compute_uv=False),3))
for key in keys:
    print(key,"output fraction",round(np.linalg.norm(M[key])**2/np.linalg.norm(Bs[key]@C)**2,3),
          "singular values",np.round(np.linalg.svd(M[key],compute_uv=False)[[0,4,9,14,19,23,26,27]],3))

# A static coordinate comparison. This is NOT a table of model logits or
# character-transition probabilities, and no linguistic labels are assigned.
maps={}
for key in [(2,1),(3,1),(3,5)]:
    a=M[key]
    uu,ss,vv=svd(a,full_matrices=False)
    inverse=(vv.T*(ss/(ss*ss+(ss[0]*.15)**2))[None,:])@uu.T
    maps[key]=inverse@J
print("\nAgreement between static coordinate-comparison matrices")
for key in maps:
    print(key,[(k2,round(np.corrcoef(maps[key].ravel(),maps[k2].ravel())[0,1],3))
               for k2 in maps])

print("\nL3H5 static comparison: strongest off-diagonal coefficients for each column")
g=maps[3,5]
for k in range(28):
    ix=np.argsort(g[:,k])[::-1]
    ix=[int(i) for i in ix if i!=k][:4]
    print(k,"diag",round(g[k,k],3),[(i,round(g[i,k],3)) for i in ix])
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 9,
  "turn": "t009",
  "code": "import numpy as np\nfrom scipy.linalg import svd\nfrom scipy.stats import skew\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nee,U=np.linalg.eigh(sum(r.T@r for r in rs))\nU=U[:,np.argsort(ee)[::-1][:28]]\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,max_iter=4000,tol=.001)\nZ=ica.fit_transform(F@U)\nZ*=np.where(skew(Z,axis=0)>=0,1.,-1.)[None,:]\n\n# A dictionary made entirely from existing MLP output-weight columns.\n# These are not inferred activations or a forward pass.\nO=np.asarray(W[\"W_proj.0\"],float)\nC=np.zeros((d,28))\nfor k in range(28):\n    ids=np.argsort(np.abs(Z[:,k]))[-3:]\n    coeff=Z[ids,k]\n    C[:,k]=O[:,ids]@coeff/np.linalg.norm(coeff)\nC/=np.linalg.norm(C,axis=0,keepdims=True)\n\nout_u,_,_=svd(np.asarray(W[\"W_proj.3\"],float),full_matrices=False)\nout_u=out_u[:,:28]\nJ=out_u.T@C\nBs={}\nfor l,h in [(2,1),(3,0),(3,1),(3,5)]:\n    s=slice(64*h,64*(h+1))\n    Bs[l,h]=np.asarray(W[f\"W_o.{l}\"][:,s],float)@(\n        np.asarray(W[f\"W_v.{l}\"][s],float)*W[f\"ln1_w.{l}\"][None,:])@P\nM={key:out_u.T@b@C for key,b in Bs.items()}\nprint(\"Alignment of copy-candidate output WEIGHT dictionaries\")\nkeys=list(M)\nfor key in keys:\n    print(key,[(key2,round(np.sum(M[key]*M[key2])/\n                          (np.linalg.norm(M[key])*np.linalg.norm(M[key2])),3))\n               for key2 in keys])\n\nprint(\"\\nDictionary singular values and output-subspace fractions\")\nprint(\"Initial dictionary:\",np.round(np.linalg.svd(C,compute_uv=False),3))\nfor key in keys:\n    print(key,\"output fraction\",round(np.linalg.norm(M[key])**2/np.linalg.norm(Bs[key]@C)**2,3),\n          \"singular values\",np.round(np.linalg.svd(M[key],compute_uv=False)[[0,4,9,14,19,23,26,27]],3))\n\n# A static coordinate comparison. This is NOT a table of model logits or\n# character-transition probabilities, and no linguistic labels are assigned.\nmaps={}\nfor key in [(2,1),(3,1),(3,5)]:\n    a=M[key]\n    uu,ss,vv=svd(a,full_matrices=False)\n    inverse=(vv.T*(ss/(ss*ss+(ss[0]*.15)**2))[None,:])@uu.T\n    maps[key]=inverse@J\nprint(\"\\nAgreement between static coordinate-comparison matrices\")\nfor key in maps:\n    print(key,[(k2,round(np.corrcoef(maps[key].ravel(),maps[k2].ravel())[0,1],3))\n               for k2 in maps])\n\nprint(\"\\nL3H5 static comparison: strongest off-diagonal coefficients for each column\")\ng=maps[3,5]\nfor k in range(28):\n    ix=np.argsort(g[:,k])[::-1]\n    ix=[int(i) for i in ix if i!=k][:4]\n    print(k,\"diag\",round(g[k,k],3),[(i,round(g[i,k],3)) for i in ix])",
  "out": "Alignment of copy-candidate output WEIGHT dictionaries\n(2, 1) [((2, 1), np.float64(1.0)), ((3, 0), np.float64(0.383)), ((3, 1), np.float64(0.441)), ((3, 5), np.float64(0.547))]\n(3, 0) [((2, 1), np.float64(0.383)), ((3, 0), np.float64(1.0)), ((3, 1), np.float64(0.439)), ((3, 5), np.float64(0.523))]\n(3, 1) [((2, 1), np.float64(0.441)), ((3, 0), np.float64(0.439)), ((3, 1), np.float64(1.0)), ((3, 5), np.float64(0.831))]\n(3, 5) [((2, 1), np.float64(0.547)), ((3, 0), np.float64(0.523)), ((3, 1), np.float64(0.831)), ((3, 5), np.float64(1.0))]\n\nDictionary singular values and output-subspace fractions\nInitial dictionary: [1.77  1.224 1.195 1.095 1.07  1.059 1.052 1.039 1.016 1.    0.999 0.98\n 0.973 0.963 0.947 0.936 0.923 0.914 0.9   0.885 0.868 0.862 0.855 0.827\n 0.81  0.783 0.779 0.765]\n(2, 1) output fraction 0.129 singular values [1.161 0.681 0.463 0.274 0.138 0.056 0.013 0.005]\n(3, 0) output fraction 0.183 singular values [1.418 1.03  0.687 0.385 0.244 0.112 0.059 0.039]\n(3, 1) output fraction 0.422 singular values [2.457 2.046 1.78  1.465 1.083 0.606 0.105 0.041]\n(3, 5) output fraction 0.396 singular values [2.513 1.969 1.645 1.352 0.972 0.59  0.245 0.019]\n\nAgreement between static coordinate-comparison matrices\n(2, 1) [((2, 1), np.float64(1.0)), ((3, 1), np.float64(0.437)), ((3, 5), np.float64(0.399))]\n(3, 1) [((2, 1), np.float64(0.437)), ((3, 1), np.float64(1.0)), ((3, 5), np.float64(0.742))]\n(3, 5) [((2, 1), np.float64(0.399)), ((3, 1), np.float64(0.742)), ((3, 5), np.float64(1.0))]\n\nL3H5 static comparison: strongest off-diagonal coefficients for each column\n0 diag -0.026 [(10, np.float64(0.073)), (15, np.float64(0.06)), (14, np.float64(0.051)), (20, np.float64(0.048))]\n1 diag -0.113 [(14, np.float64(0.121)), (15, np.float64(0.118)), (13, np.float64(0.085)), (18, np.float64(0.055))]\n2 diag -0.112 [(24, np.float64(0.076)), (16, np.float64(0.072)), (18, np.float64(0.066)), (12, np.float64(0.039))]\n3 diag -0.019 [(15, np.float64(0.096)), (10, np.float64(0.047)), (5, np.float64(0.046)), (16, np.float64(0.037))]\n4 diag -0.04 [(25, np.float64(0.081)), (18, np.float64(0.072)), (24, np.float64(0.067)), (10, np.float64(0.044))]\n5 diag -0.015 [(23, np.float64(0.146)), (26, np.float64(0.111)), (13, np.float64(0.076)), (0, np.float64(0.062))]\n6 diag -0.044 [(10, np.float64(0.093)), (18, np.float64(0.069)), (12, np.float64(0.043)), (21, np.float64(0.042))]\n7 diag 0.007 [(3, np.float64(0.106)), (20, np.float64(0.083)), (5, np.float64(0.078)), (27, np.float64(0.065))]\n8 diag -0.096 [(6, np.float64(0.131)), (13, np.float64(0.105)), (11, np.float64(0.08)), (5, np.float64(0.065))]\n9 diag -0.138 [(8, np.float64(0.127)), (13, np.float64(0.097)), (21, np.float64(0.079)), (6, np.float64(0.079))]\n10 diag 0.029 [(18, np.float64(0.103)), (15, np.float64(0.072)), (24, np.float64(0.07)), (3, np.float64(0.07))]\n11 diag -0.092 [(5, np.float64(0.152)), (14, np.float64(0.145)), (23, np.float64(0.1)), (10, np.float64(0.067))]\n12 diag -0.103 [(15, np.float64(0.09)), (13, np.float64(0.056)), (5, np.float64(0.039)), (16, np.float64(0.037))]\n13 diag 0.092 [(16, np.float64(0.096)), (11, np.float64(0.096)), (10, np.float64(0.094)), (14, np.float64(0.073))]\n14 diag -0.099 [(23, np.float64(0.053)), (21, np.float64(0.036)), (16, np.float64(0.03)), (10, np.float64(0.026))]\n15 diag -0.088 [(26, np.float64(0.044)), (0, np.float64(0.036)), (2, np.float64(0.03)), (21, np.float64(0.027))]\n16 diag -0.142 [(23, np.float64(0.082)), (15, np.float64(0.072)), (1, np.float64(0.061)), (6, np.float64(0.053))]\n17 diag -0.076 [(21, np.float64(0.192)), (2, np.float64(0.087)), (13, np.float64(0.048)), (8, np.float64(0.028))]\n18 diag -0.062 [(24, np.float64(0.104)), (2, np.float64(0.086)), (25, np.float64(0.081)), (21, np.float64(0.058))]\n19 diag -0.076 [(5, np.float64(0.057)), (24, np.float64(0.044)), (9, np.float64(0.044)), (8, np.float64(0.041))]\n20 diag -0.1 [(17, np.float64(0.101)), (15, np.float64(0.091)), (5, np.float64(0.058)), (26, np.float64(0.057))]\n21 diag 0.064 [(23, np.float64(0.074)), (18, np.float64(0.061)), (1, np.float64(0.04)), (12, np.float64(0.038))]\n22 diag -0.023 [(21, np.float64(0.108)), (19, np.float64(0.083)), (10, np.float64(0.065)), (12, np.float64(0.052))]\n23 diag -0.075 [(26, np.float64(0.134)), (18, np.float64(0.059)), (13, np.float64(0.042)), (22, np.float64(0.038))]\n24 diag -0.037 [(27, np.float64(0.133)), (14, np.float64(0.031)), (9, np.float64(0.028)), (1, np.float64(0.027))]\n25 diag -0.049 [(13, np.float64(0.106)), (3, np.float64(0.081)), (15, np.float64(0.034)), (21, np.float64(0.027))]\n26 diag -0.131 [(14, np.float64(0.076)), (21, np.float64(0.064)), (10, np.float64(0.051)), (12, np.float64(0.047))]\n27 diag -0.129 [(17, np.float64(0.056)), (1, np.float64(0.054)), (15, np.float64(0.049)), (18, np.float64(0.044))]\n",
  "seconds": 1.999,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "92a447f47e896d5c121ffe3cf421b66fc2f29b5f55059d907283d69ada9cfc62",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
Alignment of copy-candidate output WEIGHT dictionaries
(2, 1) [((2, 1), np.float64(1.0)), ((3, 0), np.float64(0.383)), ((3, 1), np.float64(0.441)), ((3, 5), np.float64(0.547))]
(3, 0) [((2, 1), np.float64(0.383)), ((3, 0), np.float64(1.0)), ((3, 1), np.float64(0.439)), ((3, 5), np.float64(0.523))]
(3, 1) [((2, 1), np.float64(0.441)), ((3, 0), np.float64(0.439)), ((3, 1), np.float64(1.0)), ((3, 5), np.float64(0.831))]
(3, 5) [((2, 1), np.float64(0.547)), ((3, 0), np.float64(0.523)), ((3, 1), np.float64(0.831)), ((3, 5), np.float64(1.0))]

Dictionary singular values and output-subspace fractions
Initial dictionary: [1.77  1.224 1.195 1.095 1.07  1.059 1.052 1.039 1.016 1.    0.999 0.98
 0.973 0.963 0.947 0.936 0.923 0.914 0.9   0.885 0.868 0.862 0.855 0.827
 0.81  0.783 0.779 0.765]
(2, 1) output fraction 0.129 singular values [1.161 0.681 0.463 0.274 0.138 0.056 0.013 0.005]
(3, 0) output fraction 0.183 singular values [1.418 1.03  0.687 0.385 0.244 0.112 0.059 0.039]
(3, 1) output fraction 0.422 singular values [2.457 2.046 1.78  1.465 1.083 0.606 0.105 0.041]
(3, 5) output fraction 0.396 singular values [2.513 1.969 1.645 1.352 0.972 0.59  0.245 0.019]

Agreement between static coordinate-comparison matrices
(2, 1) [((2, 1), np.float64(1.0)), ((3, 1), np.float64(0.437)), ((3, 5), np.float64(0.399))]
(3, 1) [((2, 1), np.float64(0.437)), ((3, 1), np.float64(1.0)), ((3, 5), np.float64(0.742))]
(3, 5) [((2, 1), np.float64(0.399)), ((3, 1), np.float64(0.742)), ((3, 5), np.float64(1.0))]

L3H5 static comparison: strongest off-diagonal coefficients for each column
0 diag -0.026 [(10, np.float64(0.073)), (15, np.float64(0.06)), (14, np.float64(0.051)), (20, np.float64(0.048))]
1 diag -0.113 [(14, np.float64(0.121)), (15, np.float64(0.118)), (13, np.float64(0.085)), (18, np.float64(0.055))]
2 diag -0.112 [(24, np.float64(0.076)), (16, np.float64(0.072)), (18, np.float64(0.066)), (12, np.float64(0.039))]
3 diag -0.019 [(15, np.float64(0.096)), (10, np.float64(0.047)), (5, np.float64(0.046)), (16, np.float64(0.037))]
4 diag -0.04 [(25, np.float64(0.081)), (18, np.float64(0.072)), (24, np.float64(0.067)), (10, np.float64(0.044))]
5 diag -0.015 [(23, np.float64(0.146)), (26, np.float64(0.111)), (13, np.float64(0.076)), (0, np.float64(0.062))]
6 diag -0.044 [(10, np.float64(0.093)), (18, np.float64(0.069)), (12, np.float64(0.043)), (21, np.float64(0.042))]
7 diag 0.007 [(3, np.float64(0.106)), (20, np.float64(0.083)), (5, np.float64(0.078)), (27, np.float64(0.065))]
8 diag -0.096 [(6, np.float64(0.131)), (13, np.float64(0.105)), (11, np.float64(0.08)), (5, np.float64(0.065))]
9 diag -0.138 [(8, np.float64(0.127)), (13, np.float64(0.097)), (21, np.float64(0.079)), (6, np.float64(0.079))]
10 diag 0.029 [(18, np.float64(0.103)), (15, np.float64(0.072)), (24, np.float64(0.07)), (3, np.float64(0.07))]
11 diag -0.092 [(5, np.float64(0.152)), (14, np.float64(0.145)), (23, np.float64(0.1)), (10, np.float64(0.067))]
12 diag -0.103 [(15, np.float64(0.09)), (13, np.float64(0.056)), (5, np.float64(0.039)), (16, np.float64(0.037))]
13 diag 0.092 [(16, np.float64(0.096)), (11, np.float64(0.096)), (10, np.float64(0.094)), (14, np.float64(0.073))]
14 diag -0.099 [(23, np.float64(0.053)), (21, np.float64(0.036)), (16, np.float64(0.03)), (10, np.float64(0.026))]
15 diag -0.088 [(26, np.float64(0.044)), (0, np.float64(0.036)), (2, np.float64(0.03)), (21, np.float64(0.027))]
16 diag -0.142 [(23, np.float64(0.082)), (15, np.float64(0.072)), (1, np.float64(0.061)), (6, np.float64(0.053))]
17 diag -0.076 [(21, np.float64(0.192)), (2, np.float64(0.087)), (13, np.float64(0.048)), (8, np.float64(0.028))]
18 diag -0.062 [(24, np.float64(0.104)), (2, np.float64(0.086)), (25, np.float64(0.081)), (21, np.float64(0.058))]
19 diag -0.076 [(5, np.float64(0.057)), (24, np.float64(0.044)), (9, np.float64(0.044)), (8, np.float64(0.041))]
20 diag -0.1 [(17, np.float64(0.101)), (15, np.float64(0.091)), (5, np.float64(0.058)), (26, np.float64(0.057))]
21 diag 0.064 [(23, np.float64(0.074)), (18, np.float64(0.061)), (1, np.float64(0.04)), (12, np.float64(0.038))]
22 diag -0.023 [(21, np.float64(0.108)), (19, np.float64(0.083)), (10, np.float64(0.065)), (12, np.float64(0.052))]
23 diag -0.075 [(26, np.float64(0.134)), (18, np.float64(0.059)), (13, np.float64(0.042)), (22, np.float64(0.038))]
24 diag -0.037 [(27, np.float64(0.133)), (14, np.float64(0.031)), (9, np.float64(0.028)), (1, np.float64(0.027))]
25 diag -0.049 [(13, np.float64(0.106)), (3, np.float64(0.081)), (15, np.float64(0.034)), (21, np.float64(0.027))]
26 diag -0.131 [(14, np.float64(0.076)), (21, np.float64(0.064)), (10, np.float64(0.051)), (12, np.float64(0.047))]
27 diag -0.129 [(17, np.float64(0.056)), (1, np.float64(0.054)), (15, np.float64(0.049)), (18, np.float64(0.044))]


========================================================================
TURN t010
MESSAGE SENT TO CODEX
OUTPUT of run 9 of at most 100:
```
Alignment of copy-candidate output WEIGHT dictionaries
(2, 1) [((2, 1), np.float64(1.0)), ((3, 0), np.float64(0.383)), ((3, 1), np.float64(0.441)), ((3, 5), np.float64(0.547))]
(3, 0) [((2, 1), np.float64(0.383)), ((3, 0), np.float64(1.0)), ((3, 1), np.float64(0.439)), ((3, 5), np.float64(0.523))]
(3, 1) [((2, 1), np.float64(0.441)), ((3, 0), np.float64(0.439)), ((3, 1), np.float64(1.0)), ((3, 5), np.float64(0.831))]
(3, 5) [((2, 1), np.float64(0.547)), ((3, 0), np.float64(0.523)), ((3, 1), np.float64(0.831)), ((3, 5), np.float64(1.0))]

Dictionary singular values and output-subspace fractions
Initial dictionary: [1.77  1.224 1.195 1.095 1.07  1.059 1.052 1.039 1.016 1.    0.999 0.98
 0.973 0.963 0.947 0.936 0.923 0.914 0.9   0.885 0.868 0.862 0.855 0.827
 0.81  0.783 0.779 0.765]
(2, 1) output fraction 0.129 singular values [1.161 0.681 0.463 0.274 0.138 0.056 0.013 0.005]
(3, 0) output fraction 0.183 singular values [1.418 1.03  0.687 0.385 0.244 0.112 0.059 0.039]
(3, 1) output fraction 0.422 singular values [2.457 2.046 1.78  1.465 1.083 0.606 0.105 0.041]
(3, 5) output fraction 0.396 singular values [2.513 1.969 1.645 1.352 0.972 0.59  0.245 0.019]

Agreement between static coordinate-comparison matrices
(2, 1) [((2, 1), np.float64(1.0)), ((3, 1), np.float64(0.437)), ((3, 5), np.float64(0.399))]
(3, 1) [((2, 1), np.float64(0.437)), ((3, 1), np.float64(1.0)), ((3, 5), np.float64(0.742))]
(3, 5) [((2, 1), np.float64(0.399)), ((3, 1), np.float64(0.742)), ((3, 5), np.float64(1.0))]

L3H5 static comparison: strongest off-diagonal coefficients for each column
0 diag -0.026 [(10, np.float64(0.073)), (15, np.float64(0.06)), (14, np.float64(0.051)), (20, np.float64(0.048))]
1 diag -0.113 [(14, np.float64(0.121)), (15, np.float64(0.118)), (13, np.float64(0.085)), (18, np.float64(0.055))]
2 diag -0.112 [(24, np.float64(0.076)), (16, np.float64(0.072)), (18, np.float64(0.066)), (12, np.float64(0.039))]
3 diag -0.019 [(15, np.float64(0.096)), (10, np.float64(0.047)), (5, np.float64(0.046)), (16, np.float64(0.037))]
4 diag -0.04 [(25, np.float64(0.081)), (18, np.float64(0.072)), (24, np.float64(0.067)), (10, np.float64(0.044))]
5 diag -0.015 [(23, np.float64(0.146)), (26, np.float64(0.111)), (13, np.float64(0.076)), (0, np.float64(0.062))]
6 diag -0.044 [(10, np.float64(0.093)), (18, np.float64(0.069)), (12, np.float64(0.043)), (21, np.float64(0.042))]
7 diag 0.007 [(3, np.float64(0.106)), (20, np.float64(0.083)), (5, np.float64(0.078)), (27, np.float64(0.065))]
8 diag -0.096 [(6, np.float64(0.131)), (13, np.float64(0.105)), (11, np.float64(0.08)), (5, np.float64(0.065))]
9 diag -0.138 [(8, np.float64(0.127)), (13, np.float64(0.097)), (21, np.float64(0.079)), (6, np.float64(0.079))]
10 diag 0.029 [(18, np.float64(0.103)), (15, np.float64(0.072)), (24, np.float64(0.07)), (3, np.float64(0.07))]
11 diag -0.092 [(5, np.float64(0.152)), (14, np.float64(0.145)), (23, np.float64(0.1)), (10, np.float64(0.067))]
12 diag -0.103 [(15, np.float64(0.09)), (13, np.float64(0.056)), (5, np.float64(0.039)), (16, np.float64(0.037))]
13 diag 0.092 [(16, np.float64(0.096)), (11, np.float64(0.096)), (10, np.float64(0.094)), (14, np.float64(0.073))]
14 diag -0.099 [(23, np.float64(0.053)), (21, np.float64(0.036)), (16, np.float64(0.03)), (10, np.float64(0.026))]
15 diag -0.088 [(26, np.float64(0.044)), (0, np.float64(0.036)), (2, np.float64(0.03)), (21, np.float64(0.027))]
16 diag -0.142 [(23, np.float64(0.082)), (15, np.float64(0.072)), (1, np.float64(0.061)), (6, np.float64(0.053))]
17 diag -0.076 [(21, np.float64(0.192)), (2, np.float64(0.087)), (13, np.float64(0.048)), (8, np.float64(0.028))]
18 diag -0.062 [(24, np.float64(0.104)), (2, np.float64(0.086)), (25, np.float64(0.081)), (21, np.float64(0.058))]
19 diag -0.076 [(5, np.float64(0.057)), (24, np.float64(0.044)), (9, np.float64(0.044)), (8, np.float64(0.041))]
20 diag -0.1 [(17, np.float64(0.101)), (15, np.float64(0.091)), (5, np.float64(0.058)), (26, np.float64(0.057))]
21 diag 0.064 [(23, np.float64(0.074)), (18, np.float64(0.061)), (1, np.float64(0.04)), (12, np.float64(0.038))]
22 diag -0.023 [(21, np.float64(0.108)), (19, np.float64(0.083)), (10, np.float64(0.065)), (12, np.float64(0.052))]
23 diag -0.075 [(26, np.float64(0.134)), (18, np.float64(0.059)), (13, np.float64(0.042)), (22, np.float64(0.038))]
24 diag -0.037 [(27, np.float64(0.133)), (14, np.float64(0.031)), (9, np.float64(0.028)), (1, np.float64(0.027))]
25 diag -0.049 [(13, np.float64(0.106)), (3, np.float64(0.081)), (15, np.float64(0.034)), (21, np.float64(0.027))]
26 diag -0.131 [(14, np.float64(0.076)), (21, np.float64(0.064)), (10, np.float64(0.051)), (12, np.float64(0.047))]
27 diag -0.129 [(17, np.float64(0.056)), (1, np.float64(0.054)), (15, np.float64(0.049)), (18, np.float64(0.044))]

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import svd
from scipy.stats import skew
from scipy.optimize import quadratic_assignment
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
threadpool_limits(limits=2)

# Weight-only reconstruction of the two static coordinate comparisons from run 9.
d=384
P=np.eye(d)-np.ones((d,d))/d
rs=[]
for h in [1,5]:
    _,_,r=svd(np.asarray(W["W_v.0"][64*h:64*(h+1)],float)@P,full_matrices=False)
    rs.append(r[:28])
ee,U=np.linalg.eigh(sum(r.T@r for r in rs))
U=U[:,np.argsort(ee)[::-1][:28]]
F=np.asarray(W["W_fc.0"],float)*W["ln2_w.0"][None,:]
ica=FastICA(n_components=28,whiten="unit-variance",random_state=17,max_iter=4000,tol=.001)
Z=ica.fit_transform(F@U)
Z*=np.where(skew(Z,axis=0)>=0,1.,-1.)[None,:]
O=np.asarray(W["W_proj.0"],float)
C=np.zeros((d,28))
for k in range(28):
    ids=np.argsort(np.abs(Z[:,k]))[-3:]
    cc=Z[ids,k]
    C[:,k]=O[:,ids]@cc/np.linalg.norm(cc)
C/=np.linalg.norm(C,axis=0,keepdims=True)
out_u,_,_=svd(np.asarray(W["W_proj.3"],float),full_matrices=False)
out_u=out_u[:,:28]
J=out_u.T@C
maps={}
for h in [1,5]:
    s=slice(64*h,64*(h+1))
    b=np.asarray(W["W_o.3"][:,s],float)@(
        np.asarray(W["W_v.3"][s],float)*W["ln1_w.3"][None,:])@P
    a=out_u.T@b@C
    uu,ss,vv=svd(a,full_matrices=False)
    inverse=(vv.T*(ss/(ss*ss+(ss[0]*.15)**2))[None,:])@uu.T
    maps[h]=(inverse@J).T

# These short, invented controls are ONLY counted as text.
# They are NOT fed to the network, any layer, or any weight matrix.
# Matching their unlabeled bigram graphs is exploratory, not a calibrated test.
samples={}
samples["english"]="""
the history of the city is closely related to the development of the region
many people lived in small villages before the construction of the railway
the population increased during the twentieth century and the town became an important industrial centre
the university was founded in the nineteenth century and has several departments
its research includes mathematics physics chemistry biology and computer science
the company produces equipment for commercial and domestic use
the first edition of the book was published in london and was later translated into several languages
she wrote about her experiences and the lives of the people she had met
the river flows through the northern part of the country before reaching the sea
the climate is generally mild although temperatures can vary considerably
the national government is responsible for education health and public services
the team won the championship after defeating their opponents in the final match
the organization has members in many countries and holds an annual conference
a number of different methods have been proposed to explain these results
this process requires a significant amount of energy and may take several years
he studied at the local school and later worked as a teacher and journalist
the species is found in forests and grasslands where it feeds on insects and small animals
the music was composed for a large orchestra and performed for the first time in paris
there are several buildings of historical interest including the church and the old castle
the system consists of a central processor connected to a network of other devices
"""
samples["python"]="""
import os
import sys
from collections import defaultdict
from pathlib import path

class connection
    def init self host port timeout none
        self host host
        self port port
        self timeout timeout
        self closed false
        self data dict
    def connect self
        if self closed
            raise valueerror invalid connection
        return self client connect self host self port
    def close self
        self closed true
        self client close
    def get self key default none
        return self data get key default
    def update self values
        for key value in values items
            self data key value

def process items limit none
    result list
    for index item in enumerate items
        if item is none
            continue
        value str item strip lower
        if not value
            continue
        result append value
        if limit is not none and len result limit
            break
    return result

def load path encoding utf
    with open path mode r encoding encoding as stream
        return stream read

def validate value expected type
    if not isinstance value expected type
        raise typeerror expected value
    return value

class handler
    def init self config
        self config config
        self cache dict
    def call self request args kwargs
        try
            response self process request
        except exception as error
            self logger error str error
            raise
        else
            return response
    def process self request
        key request get name
        if key not in self cache
            self cache key self create key
        return self cache key
    def create self name
        options self config get name dict
        return objectfactory name options

def main args none
    parser argumentparser
    parser add argument input
    parser add argument output
    options parser parse args args
    data load options input
    result process data splitlines
    with open options output w as stream
        for line in result
            stream write line
    return none
"""
samples["romaji"]="""
watashi wa sono toki mada kodomo de atta
sore wa aru aki no yoru no koto de aru
kare wa shizuka ni heya no naka ni haitte kita
watashi wa nani mo iwanai de sono kao o mite ita
soto ni wa ame ga futte ite kaze no oto ga kikoeta
kono machi ni kita no wa mou zuibun mae no koto da
haha wa itsumo yasashii koe de watashi o yonda
sono hito wa mukashi kara koko ni sunde iru rashikatta
dare mo inai michi o hitori de aruite iru to
tooku no yama no ue ni tsuki ga dete ita
sore kara futari wa nagai aida hanashi o shita
doushite sonna koto o suru no ka watashi ni wa wakaranakatta
kare no kotoba ni wa fushigi na chikara ga atta
ashita no asa ni wa mata tabi ni denakereba naranai
watashi wa hon o tojite mado no soto o nagameta
chiisana tori ga niwa no ki no eda ni tomatte ita
sensei wa sukoshi waratte sore wa omoshiroi to itta
kanojo wa nanimo shiranai you na kao o shite iru
yagate haru ga kite hana ga sakihajimeta
soshite subete no koto ga yume no you ni omoeta
"""
vocab="\n abcdefghijklmnopqrstuvwxyz"
def counts(text):
    lines=[]
    for line in text.lower().splitlines():
        lead=len(line)-len(line.lstrip(" "))
        body="".join(c if "a"<=c<="z" else " " for c in line[lead:])
        lines.append(" "*lead+" ".join(body.split()))
    text="\n".join(lines)
    a=np.zeros((28,28))
    for x,y in zip(text,text[1:]):
        a[vocab.index(x),vocab.index(y)]+=1
    return a

def standardized(a):
    a=a.copy()
    a=a-a.mean(0,keepdims=True)-a.mean(1,keepdims=True)+a.mean()
    np.fill_diagonal(a,0)
    return a/np.linalg.norm(a)

A=standardized(maps[1])
test=standardized(maps[5])
rng=np.random.default_rng(927)
print("EXPLORATORY graph matching against invented controls; scores are NOT probabilities.")
for transform in ["sqrt_counts","log_conditional"]:
    print("\nTransform:",transform)
    for name,txt in samples.items():
        cnt=counts(txt)
        if transform=="sqrt_counts":
            ref=np.sqrt(cnt)
        else:
            ref=np.log((cnt+.25)/(cnt.sum(1,keepdims=True)+7))
        ref=standardized(ref)
        fits=[]
        for trial in range(80):
            result=quadratic_assignment(A,ref,method="faq",
                options={"maximize":True,"P0":"randomized","rng":rng,
                         "maxiter":60,"tol":.001})
            perm=result.col_ind
            rr=ref[np.ix_(perm,perm)]
            fits.append((float(np.sum(A*rr)),float(np.sum(test*rr)),perm))
        fits.sort(key=lambda x:x[0],reverse=True)
        best=fits[0]
        print(name,"fit",round(best[0],3),"other-head agreement",round(best[1],3),
              "top10 mean other-head",round(np.mean([x[1] for x in fits[:10]]),3))
        print("  tentative axis labels:",
              " ".join(f"{i}:{repr(vocab[j])}" for i,j in enumerate(best[2])))
```

PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 10,
  "turn": "t010",
  "code": "import numpy as np\nfrom scipy.linalg import svd\nfrom scipy.stats import skew\nfrom scipy.optimize import quadratic_assignment\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\n# Weight-only reconstruction of the two static coordinate comparisons from run 9.\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nee,U=np.linalg.eigh(sum(r.T@r for r in rs))\nU=U[:,np.argsort(ee)[::-1][:28]]\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,max_iter=4000,tol=.001)\nZ=ica.fit_transform(F@U)\nZ*=np.where(skew(Z,axis=0)>=0,1.,-1.)[None,:]\nO=np.asarray(W[\"W_proj.0\"],float)\nC=np.zeros((d,28))\nfor k in range(28):\n    ids=np.argsort(np.abs(Z[:,k]))[-3:]\n    cc=Z[ids,k]\n    C[:,k]=O[:,ids]@cc/np.linalg.norm(cc)\nC/=np.linalg.norm(C,axis=0,keepdims=True)\nout_u,_,_=svd(np.asarray(W[\"W_proj.3\"],float),full_matrices=False)\nout_u=out_u[:,:28]\nJ=out_u.T@C\nmaps={}\nfor h in [1,5]:\n    s=slice(64*h,64*(h+1))\n    b=np.asarray(W[\"W_o.3\"][:,s],float)@(\n        np.asarray(W[\"W_v.3\"][s],float)*W[\"ln1_w.3\"][None,:])@P\n    a=out_u.T@b@C\n    uu,ss,vv=svd(a,full_matrices=False)\n    inverse=(vv.T*(ss/(ss*ss+(ss[0]*.15)**2))[None,:])@uu.T\n    maps[h]=(inverse@J).T\n\n# These short, invented controls are ONLY counted as text.\n# They are NOT fed to the network, any layer, or any weight matrix.\n# Matching their unlabeled bigram graphs is exploratory, not a calibrated test.\nsamples={}\nsamples[\"english\"]=\"\"\"\nthe history of the city is closely related to the development of the region\nmany people lived in small villages before the construction of the railway\nthe population increased during the twentieth century and the town became an important industrial centre\nthe university was founded in the nineteenth century and has several departments\nits research includes mathematics physics chemistry biology and computer science\nthe company produces equipment for commercial and domestic use\nthe first edition of the book was published in london and was later translated into several languages\nshe wrote about her experiences and the lives of the people she had met\nthe river flows through the northern part of the country before reaching the sea\nthe climate is generally mild although temperatures can vary considerably\nthe national government is responsible for education health and public services\nthe team won the championship after defeating their opponents in the final match\nthe organization has members in many countries and holds an annual conference\na number of different methods have been proposed to explain these results\nthis process requires a significant amount of energy and may take several years\nhe studied at the local school and later worked as a teacher and journalist\nthe species is found in forests and grasslands where it feeds on insects and small animals\nthe music was composed for a large orchestra and performed for the first time in paris\nthere are several buildings of historical interest including the church and the old castle\nthe system consists of a central processor connected to a network of other devices\n\"\"\"\nsamples[\"python\"]=\"\"\"\nimport os\nimport sys\nfrom collections import defaultdict\nfrom pathlib import path\n\nclass connection\n    def init self host port timeout none\n        self host host\n        self port port\n        self timeout timeout\n        self closed false\n        self data dict\n    def connect self\n        if self closed\n            raise valueerror invalid connection\n        return self client connect self host self port\n    def close self\n        self closed true\n        self client close\n    def get self key default none\n        return self data get key default\n    def update self values\n        for key value in values items\n            self data key value\n\ndef process items limit none\n    result list\n    for index item in enumerate items\n        if item is none\n            continue\n        value str item strip lower\n        if not value\n            continue\n        result append value\n        if limit is not none and len result limit\n            break\n    return result\n\ndef load path encoding utf\n    with open path mode r encoding encoding as stream\n        return stream read\n\ndef validate value expected type\n    if not isinstance value expected type\n        raise typeerror expected value\n    return value\n\nclass handler\n    def init self config\n        self config config\n        self cache dict\n    def call self request args kwargs\n        try\n            response self process request\n        except exception as error\n            self logger error str error\n            raise\n        else\n            return response\n    def process self request\n        key request get name\n        if key not in self cache\n            self cache key self create key\n        return self cache key\n    def create self name\n        options self config get name dict\n        return objectfactory name options\n\ndef main args none\n    parser argumentparser\n    parser add argument input\n    parser add argument output\n    options parser parse args args\n    data load options input\n    result process data splitlines\n    with open options output w as stream\n        for line in result\n            stream write line\n    return none\n\"\"\"\nsamples[\"romaji\"]=\"\"\"\nwatashi wa sono toki mada kodomo de atta\nsore wa aru aki no yoru no koto de aru\nkare wa shizuka ni heya no naka ni haitte kita\nwatashi wa nani mo iwanai de sono kao o mite ita\nsoto ni wa ame ga futte ite kaze no oto ga kikoeta\nkono machi ni kita no wa mou zuibun mae no koto da\nhaha wa itsumo yasashii koe de watashi o yonda\nsono hito wa mukashi kara koko ni sunde iru rashikatta\ndare mo inai michi o hitori de aruite iru to\ntooku no yama no ue ni tsuki ga dete ita\nsore kara futari wa nagai aida hanashi o shita\ndoushite sonna koto o suru no ka watashi ni wa wakaranakatta\nkare no kotoba ni wa fushigi na chikara ga atta\nashita no asa ni wa mata tabi ni denakereba naranai\nwatashi wa hon o tojite mado no soto o nagameta\nchiisana tori ga niwa no ki no eda ni tomatte ita\nsensei wa sukoshi waratte sore wa omoshiroi to itta\nkanojo wa nanimo shiranai you na kao o shite iru\nyagate haru ga kite hana ga sakihajimeta\nsoshite subete no koto ga yume no you ni omoeta\n\"\"\"\nvocab=\"\\n abcdefghijklmnopqrstuvwxyz\"\ndef counts(text):\n    lines=[]\n    for line in text.lower().splitlines():\n        lead=len(line)-len(line.lstrip(\" \"))\n        body=\"\".join(c if \"a\"<=c<=\"z\" else \" \" for c in line[lead:])\n        lines.append(\" \"*lead+\" \".join(body.split()))\n    text=\"\\n\".join(lines)\n    a=np.zeros((28,28))\n    for x,y in zip(text,text[1:]):\n        a[vocab.index(x),vocab.index(y)]+=1\n    return a\n\ndef standardized(a):\n    a=a.copy()\n    a=a-a.mean(0,keepdims=True)-a.mean(1,keepdims=True)+a.mean()\n    np.fill_diagonal(a,0)\n    return a/np.linalg.norm(a)\n\nA=standardized(maps[1])\ntest=standardized(maps[5])\nrng=np.random.default_rng(927)\nprint(\"EXPLORATORY graph matching against invented controls; scores are NOT probabilities.\")\nfor transform in [\"sqrt_counts\",\"log_conditional\"]:\n    print(\"\\nTransform:\",transform)\n    for name,txt in samples.items():\n        cnt=counts(txt)\n        if transform==\"sqrt_counts\":\n            ref=np.sqrt(cnt)\n        else:\n            ref=np.log((cnt+.25)/(cnt.sum(1,keepdims=True)+7))\n        ref=standardized(ref)\n        fits=[]\n        for trial in range(80):\n            result=quadratic_assignment(A,ref,method=\"faq\",\n                options={\"maximize\":True,\"P0\":\"randomized\",\"rng\":rng,\n                         \"maxiter\":60,\"tol\":.001})\n            perm=result.col_ind\n            rr=ref[np.ix_(perm,perm)]\n            fits.append((float(np.sum(A*rr)),float(np.sum(test*rr)),perm))\n        fits.sort(key=lambda x:x[0],reverse=True)\n        best=fits[0]\n        print(name,\"fit\",round(best[0],3),\"other-head agreement\",round(best[1],3),\n              \"top10 mean other-head\",round(np.mean([x[1] for x in fits[:10]]),3))\n        print(\"  tentative axis labels:\",\n              \" \".join(f\"{i}:{repr(vocab[j])}\" for i,j in enumerate(best[2])))",
  "out": "EXPLORATORY graph matching against invented controls; scores are NOT probabilities.\n\nTransform: sqrt_counts\nenglish fit 0.442 other-head agreement 0.343 top10 mean other-head 0.29\n  tentative axis labels: 0:'\\n' 1:'t' 2:'m' 3:'g' 4:'f' 5:'o' 6:'d' 7:'p' 8:'n' 9:'i' 10:'s' 11:'c' 12:'l' 13:' ' 14:'h' 15:'y' 16:'a' 17:'z' 18:'e' 19:'j' 20:'b' 21:'v' 22:'x' 23:'k' 24:'q' 25:'r' 26:'w' 27:'u'\npython fit 0.455 other-head agreement 0.346 top10 mean other-head 0.286\n  tentative axis labels: 0:'\\n' 1:'t' 2:'v' 3:'p' 4:'d' 5:'o' 6:'g' 7:'m' 8:'n' 9:'i' 10:'s' 11:'c' 12:'l' 13:' ' 14:'h' 15:'y' 16:'a' 17:'b' 18:'e' 19:'x' 20:'r' 21:'j' 22:'z' 23:'k' 24:'q' 25:'f' 26:'w' 27:'u'\nromaji fit 0.409 other-head agreement 0.281 top10 mean other-head 0.261\n  tentative axis labels: 0:'e' 1:'y' 2:'k' 3:'g' 4:'m' 5:'u' 6:'w' 7:'b' 8:' ' 9:'o' 10:'t' 11:'s' 12:'r' 13:'n' 14:'h' 15:'z' 16:'i' 17:'p' 18:'a' 19:'f' 20:'l' 21:'\\n' 22:'q' 23:'d' 24:'c' 25:'j' 26:'x' 27:'v'\n\nTransform: log_conditional\nenglish fit 0.445 other-head agreement 0.329 top10 mean other-head 0.29\n  tentative axis labels: 0:'\\n' 1:'t' 2:'z' 3:'d' 4:'f' 5:'o' 6:'g' 7:'l' 8:'n' 9:'i' 10:'s' 11:'c' 12:'r' 13:' ' 14:'h' 15:'y' 16:'a' 17:'j' 18:'e' 19:'p' 20:'b' 21:'k' 22:'x' 23:'m' 24:'q' 25:'v' 26:'w' 27:'u'\npython fit 0.393 other-head agreement 0.3 top10 mean other-head 0.283\n  tentative axis labels: 0:'j' 1:'r' 2:'d' 3:'f' 4:'k' 5:' ' 6:'o' 7:'l' 8:'n' 9:'e' 10:'p' 11:'t' 12:'m' 13:'s' 14:'y' 15:'\\n' 16:'i' 17:'b' 18:'a' 19:'g' 20:'h' 21:'c' 22:'x' 23:'v' 24:'q' 25:'z' 26:'w' 27:'u'\nromaji fit 0.414 other-head agreement 0.288 top10 mean other-head 0.251\n  tentative axis labels: 0:'c' 1:'h' 2:'d' 3:'w' 4:'k' 5:'u' 6:'n' 7:'b' 8:' ' 9:'e' 10:'t' 11:'s' 12:'r' 13:'i' 14:'l' 15:'f' 16:'j' 17:'x' 18:'a' 19:'z' 20:'v' 21:'y' 22:'q' 23:'g' 24:'o' 25:'\\n' 26:'p' 27:'m'\n",
  "seconds": 1.633,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "01dfe7d6f68c1fc6c29cf6ad212c96cdf35079f18c6ac10a306b0f5ba99b3a20",
  "weights_sha256": "77ed8f7d418619c9390da0abea19de2a05d6e2200d8484473deafee15a6fd43f"
}

EXACT HARNESS FEEDBACK
EXPLORATORY graph matching against invented controls; scores are NOT probabilities.

Transform: sqrt_counts
english fit 0.442 other-head agreement 0.343 top10 mean other-head 0.29
  tentative axis labels: 0:'\n' 1:'t' 2:'m' 3:'g' 4:'f' 5:'o' 6:'d' 7:'p' 8:'n' 9:'i' 10:'s' 11:'c' 12:'l' 13:' ' 14:'h' 15:'y' 16:'a' 17:'z' 18:'e' 19:'j' 20:'b' 21:'v' 22:'x' 23:'k' 24:'q' 25:'r' 26:'w' 27:'u'
python fit 0.455 other-head agreement 0.346 top10 mean other-head 0.286
  tentative axis labels: 0:'\n' 1:'t' 2:'v' 3:'p' 4:'d' 5:'o' 6:'g' 7:'m' 8:'n' 9:'i' 10:'s' 11:'c' 12:'l' 13:' ' 14:'h' 15:'y' 16:'a' 17:'b' 18:'e' 19:'x' 20:'r' 21:'j' 22:'z' 23:'k' 24:'q' 25:'f' 26:'w' 27:'u'
romaji fit 0.409 other-head agreement 0.281 top10 mean other-head 0.261
  tentative axis labels: 0:'e' 1:'y' 2:'k' 3:'g' 4:'m' 5:'u' 6:'w' 7:'b' 8:' ' 9:'o' 10:'t' 11:'s' 12:'r' 13:'n' 14:'h' 15:'z' 16:'i' 17:'p' 18:'a' 19:'f' 20:'l' 21:'\n' 22:'q' 23:'d' 24:'c' 25:'j' 26:'x' 27:'v'

Transform: log_conditional
english fit 0.445 other-head agreement 0.329 top10 mean other-head 0.29
  tentative axis labels: 0:'\n' 1:'t' 2:'z' 3:'d' 4:'f' 5:'o' 6:'g' 7:'l' 8:'n' 9:'i' 10:'s' 11:'c' 12:'r' 13:' ' 14:'h' 15:'y' 16:'a' 17:'j' 18:'e' 19:'p' 20:'b' 21:'k' 22:'x' 23:'m' 24:'q' 25:'v' 26:'w' 27:'u'
python fit 0.393 other-head agreement 0.3 top10 mean other-head 0.283
  tentative axis labels: 0:'j' 1:'r' 2:'d' 3:'f' 4:'k' 5:' ' 6:'o' 7:'l' 8:'n' 9:'e' 10:'p' 11:'t' 12:'m' 13:'s' 14:'y' 15:'\n' 16:'i' 17:'b' 18:'a' 19:'g' 20:'h' 21:'c' 22:'x' 23:'v' 24:'q' 25:'z' 26:'w' 27:'u'
romaji fit 0.414 other-head agreement 0.288 top10 mean other-head 0.251
  tentative axis labels: 0:'c' 1:'h' 2:'d' 3:'w' 4:'k' 5:'u' 6:'n' 7:'b' 8:' ' 9:'e' 10:'t' 11:'s' 12:'r' 13:'i' 14:'l' 15:'f' 16:'j' 17:'x' 18:'a' 19:'z' 20:'v' 21:'y' 22:'q' 23:'g' 24:'o' 25:'\n' 26:'p' 27:'m'


========================================================================
TURN t011
MESSAGE SENT TO CODEX
OUTPUT of run 10 of at most 100:
```
EXPLORATORY graph matching against invented controls; scores are NOT probabilities.

Transform: sqrt_counts
english fit 0.442 other-head agreement 0.343 top10 mean other-head 0.29
  tentative axis labels: 0:'\n' 1:'t' 2:'m' 3:'g' 4:'f' 5:'o' 6:'d' 7:'p' 8:'n' 9:'i' 10:'s' 11:'c' 12:'l' 13:' ' 14:'h' 15:'y' 16:'a' 17:'z' 18:'e' 19:'j' 20:'b' 21:'v' 22:'x' 23:'k' 24:'q' 25:'r' 26:'w' 27:'u'
python fit 0.455 other-head agreement 0.346 top10 mean other-head 0.286
  tentative axis labels: 0:'\n' 1:'t' 2:'v' 3:'p' 4:'d' 5:'o' 6:'g' 7:'m' 8:'n' 9:'i' 10:'s' 11:'c' 12:'l' 13:' ' 14:'h' 15:'y' 16:'a' 17:'b' 18:'e' 19:'x' 20:'r' 21:'j' 22:'z' 23:'k' 24:'q' 25:'f' 26:'w' 27:'u'
romaji fit 0.409 other-head agreement 0.281 top10 mean other-head 0.261
  tentative axis labels: 0:'e' 1:'y' 2:'k' 3:'g' 4:'m' 5:'u' 6:'w' 7:'b' 8:' ' 9:'o' 10:'t' 11:'s' 12:'r' 13:'n' 14:'h' 15:'z' 16:'i' 17:'p' 18:'a' 19:'f' 20:'l' 21:'\n' 22:'q' 23:'d' 24:'c' 25:'j' 26:'x' 27:'v'

Transform: log_conditional
english fit 0.445 other-head agreement 0.329 top10 mean other-head 0.29
  tentative axis labels: 0:'\n' 1:'t' 2:'z' 3:'d' 4:'f' 5:'o' 6:'g' 7:'l' 8:'n' 9:'i' 10:'s' 11:'c' 12:'r' 13:' ' 14:'h' 15:'y' 16:'a' 17:'j' 18:'e' 19:'p' 20:'b' 21:'k' 22:'x' 23:'m' 24:'q' 25:'v' 26:'w' 27:'u'
python fit 0.393 other-head agreement 0.3 top10 mean other-head 0.283
  tentative axis labels: 0:'j' 1:'r' 2:'d' 3:'f' 4:'k' 5:' ' 6:'o' 7:'l' 8:'n' 9:'e' 10:'p' 11:'t' 12:'m' 13:'s' 14:'y' 15:'\n' 16:'i' 17:'b' 18:'a' 19:'g' 20:'h' 21:'c' 22:'x' 23:'v' 24:'q' 25:'z' 26:'w' 27:'u'
romaji fit 0.414 other-head agreement 0.288 top10 mean other-head 0.251
  tentative axis labels: 0:'c' 1:'h' 2:'d' 3:'w' 4:'k' 5:'u' 6:'n' 7:'b' 8:' ' 9:'e' 10:'t' 11:'s' 12:'r' 13:'i' 14:'l' 15:'f' 16:'j' 17:'x' 18:'a' 19:'z' 20:'v' 21:'y' 22:'q' 23:'g' 24:'o' 25:'\n' 26:'p' 27:'m'

```
CLI OUTPUT IN EVENT ORDER
ERROR
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)

ERROR
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.

AGENT_MESSAGE
## Observations

**The strongest conclusion is “as is”; the corpus identification is less certain.** These are weight-only inferences, not measured attention patterns.

- **A likely one-character/two-character offset pair:** L0H5 and L0H1 read closely overlapping value subspaces: their first 23 principal cosines exceed 0.82. In a weight-derived positional subspace, L0H1’s QK matrix aligns with the **square** of L0H5’s polar factor: cosine **0.647**, versus **−0.031** with its first power. This supports successive character offsets.
- **A broad aggregation candidate:** `W_q.0[256:320]` and `W_k.0[256:320]` have norms **2.173** and **2.623**, much smaller than L0H5’s **15.898** and **15.965**. Nevertheless, L0H4’s OV product has leading singular values **18.815, 15.373**. It appears designed to aggregate rather than sharply select.
- **Matching circuits:** Later QK matrices compose positively with L0H5’s OV matrix, particularly at **L2H1, L3H1, L3H5**. Their matching scores defined below are **0.755, 0.698, 0.753**. L3H1 and L3H5 also produce closely aligned output-weight dictionaries: cosine **0.831**.
- **Corpus evidence is weaker:** Exploratory matching of a static weight-derived association matrix against invented text controls favored English under log-conditional scoring: **0.445**, versus Python **0.393**, romaji **0.414**. Another scoring convention narrowly favored Python. These are heuristic comparisons, **not likelihoods or decoded logits**.

For the head evidence below:
- **r** is the trace/nuclear-norm ratio of the QK–L0H5-OV composition restricted to the shared early value-reading subspace. Positive values support matching, but do not alone prove induction.
- **f** is the fraction of a head’s key-side QK energy in L0H4’s leading three output directions.

## Heads

L0H0: **unclear** — QK stable rank 1.545; low-dimensional selection, but no identifiable backward distance.  
L0H1: **local** — strong squared-rotation relationship to L0H5; likely approximately two characters back.  
L0H2: **unclear** — substantial content dependence: 59.9% of key-side QK energy lies in the shared value-reading subspace.  
L0H3: **unclear** — low-rank, content-sensitive QK structure; no convincing fixed-offset signature.  
L0H4: **broad** — unusually weak Q/K weights alongside strong, low-rank OV; strongest aggregation candidate.  
L0H5: **previous** — strongest directional positional structure, shared character-reading subspace, and downstream matching compositions.

L1H0: **unclear** — f=0.342; reads the aggregation channel, but its attention range remains unresolved.  
L1H1: **unclear** — f=0.627; strong shared positional/summary dependence, not enough to establish locality.  
L1H2: **unclear** — f=0.631; similarly summary-dependent, without a clear matching circuit.  
L1H3: **unclear** — f=0.677; strongest summary-channel dependence, but no identifiable offset.  
L1H4: **unclear** — f=0.645; closely related to other L1 kernels, without decisive range evidence.  
L1H5: **unclear** — f=0.524; weak matching component, r=0.152, insufficient to call induction.

L2H0: **unclear** — r=−0.059; neither a positive matching circuit nor a resolved offset.  
L2H1: **copy** — r=0.755; strong positive matching through the likely previous-character head.  
L2H2: **unclear** — r=−0.476, but strongly supplies later matching circuits; could construct context features rather than copy.  
L2H3: **unclear** — r=−0.049; summary-channel coupling does not establish attention distance.  
L2H4: **unclear** — r=0.010; strong composition magnitude without a positive matching signature.  
L2H5: **unclear** — r=0.125; weak matching evidence, insufficient for a functional assignment.

L3H0: **copy** — tentative: r=0.343 and output-weight alignment with stronger copy candidates.  
L3H1: **copy** — r=0.698; strong matching through both early and intermediate feature-writing heads.  
L3H2: **unclear** — r=−0.024; no decisive offset or induction signature.  
L3H3: **unclear** — r=0.010; comparatively weak QK structure, but that does not establish broad attention.  
L3H4: **unclear** — r=0.155; modest matching component without a decisive dominant role.  
L3H5: **copy** — r=0.753; especially strong key composition with L2H2 and agreement with L3H1.

## Reasoning

The likely adjacent-character offset pair and multiple downstream matching circuits strongly favor **preserved character order**. Shuffling retains line composition, but removes most benefits of remembering specific neighboring characters and repeating ordered contexts.

My best corpus judgment is **English**, with Python a substantial alternative. The static association comparisons and broadly developed character-feature structure lean that way; they do not provide a reliable linguistic identification. I found no sufficiently clear indentation-specific or romaji-specific signature to override that judgment.

corpus: english  
order: as is  
probabilities: python=0.34 romaji=0.06 english=0.60; as is=0.99 shuffled=0.01

SAVED FINAL ANSWER
## Observations

**The strongest conclusion is “as is”; the corpus identification is less certain.** These are weight-only inferences, not measured attention patterns.

- **A likely one-character/two-character offset pair:** L0H5 and L0H1 read closely overlapping value subspaces: their first 23 principal cosines exceed 0.82. In a weight-derived positional subspace, L0H1’s QK matrix aligns with the **square** of L0H5’s polar factor: cosine **0.647**, versus **−0.031** with its first power. This supports successive character offsets.
- **A broad aggregation candidate:** `W_q.0[256:320]` and `W_k.0[256:320]` have norms **2.173** and **2.623**, much smaller than L0H5’s **15.898** and **15.965**. Nevertheless, L0H4’s OV product has leading singular values **18.815, 15.373**. It appears designed to aggregate rather than sharply select.
- **Matching circuits:** Later QK matrices compose positively with L0H5’s OV matrix, particularly at **L2H1, L3H1, L3H5**. Their matching scores defined below are **0.755, 0.698, 0.753**. L3H1 and L3H5 also produce closely aligned output-weight dictionaries: cosine **0.831**.
- **Corpus evidence is weaker:** Exploratory matching of a static weight-derived association matrix against invented text controls favored English under log-conditional scoring: **0.445**, versus Python **0.393**, romaji **0.414**. Another scoring convention narrowly favored Python. These are heuristic comparisons, **not likelihoods or decoded logits**.

For the head evidence below:
- **r** is the trace/nuclear-norm ratio of the QK–L0H5-OV composition restricted to the shared early value-reading subspace. Positive values support matching, but do not alone prove induction.
- **f** is the fraction of a head’s key-side QK energy in L0H4’s leading three output directions.

## Heads

L0H0: **unclear** — QK stable rank 1.545; low-dimensional selection, but no identifiable backward distance.  
L0H1: **local** — strong squared-rotation relationship to L0H5; likely approximately two characters back.  
L0H2: **unclear** — substantial content dependence: 59.9% of key-side QK energy lies in the shared value-reading subspace.  
L0H3: **unclear** — low-rank, content-sensitive QK structure; no convincing fixed-offset signature.  
L0H4: **broad** — unusually weak Q/K weights alongside strong, low-rank OV; strongest aggregation candidate.  
L0H5: **previous** — strongest directional positional structure, shared character-reading subspace, and downstream matching compositions.

L1H0: **unclear** — f=0.342; reads the aggregation channel, but its attention range remains unresolved.  
L1H1: **unclear** — f=0.627; strong shared positional/summary dependence, not enough to establish locality.  
L1H2: **unclear** — f=0.631; similarly summary-dependent, without a clear matching circuit.  
L1H3: **unclear** — f=0.677; strongest summary-channel dependence, but no identifiable offset.  
L1H4: **unclear** — f=0.645; closely related to other L1 kernels, without decisive range evidence.  
L1H5: **unclear** — f=0.524; weak matching component, r=0.152, insufficient to call induction.

L2H0: **unclear** — r=−0.059; neither a positive matching circuit nor a resolved offset.  
L2H1: **copy** — r=0.755; strong positive matching through the likely previous-character head.  
L2H2: **unclear** — r=−0.476, but strongly supplies later matching circuits; could construct context features rather than copy.  
L2H3: **unclear** — r=−0.049; summary-channel coupling does not establish attention distance.  
L2H4: **unclear** — r=0.010; strong composition magnitude without a positive matching signature.  
L2H5: **unclear** — r=0.125; weak matching evidence, insufficient for a functional assignment.

L3H0: **copy** — tentative: r=0.343 and output-weight alignment with stronger copy candidates.  
L3H1: **copy** — r=0.698; strong matching through both early and intermediate feature-writing heads.  
L3H2: **unclear** — r=−0.024; no decisive offset or induction signature.  
L3H3: **unclear** — r=0.010; comparatively weak QK structure, but that does not establish broad attention.  
L3H4: **unclear** — r=0.155; modest matching component without a decisive dominant role.  
L3H5: **copy** — r=0.753; especially strong key composition with L2H2 and agreement with L3H1.

## Reasoning

The likely adjacent-character offset pair and multiple downstream matching circuits strongly favor **preserved character order**. Shuffling retains line composition, but removes most benefits of remembering specific neighboring characters and repeating ordered contexts.

My best corpus judgment is **English**, with Python a substantial alternative. The static association comparisons and broadly developed character-feature structure lean that way; they do not provide a reliable linguistic identification. I found no sufficiently clear indentation-specific or romaji-specific signature to override that judgment.

corpus: english  
order: as is  
probabilities: python=0.34 romaji=0.06 english=0.60; as is=0.99 shuffled=0.01