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Experiment 3 · english r1 · fresh

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Answer the user's request.
t001 · harness: python

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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 notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from threadpoolctl import threadpool_limits

np.set_printoptions(precision=4, suppress=True, linewidth=160)

with threadpool_limits(limits=2):
    print("AVAILABLE WEIGHTS")
    print([(k, tuple(v.shape)) for k, v in sorted(W.items())])
    print("\nLAYERNORM PARAMETERS: min, median, max, mean, std, L2 norm")
    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)
            print(f"{name}.{l}", np.round([a.min(), np.median(a), a.max(), a.mean(), a.std(), la.norm(a)], 5))

    print("\nMATRIX SPECTRA: Frobenius norm, largest singular value, stable rank, top 8 singular values")
    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 = la.svdvals(a)
            print(f"{name}.{l}: norm={la.norm(a):.4f} smax={s[0]:.4f} stable_rank={np.sum(s*s)/s[0]**2:.2f} top={np.round(s[:8],4)}")

    print("\nHEAD WEIGHT PRODUCTS (no inputs or forward passes)")
    print("QK=Q.T@K/8; OV=O@V. symfrac=||symmetric part||F/||matrix||F; trcos=trace/(sqrt(384)*norm).")
    for l in range(4):
        for j in range(6):
            sl = slice(64*j, 64*(j+1))
            q = np.asarray(W[f"W_q.{l}"][sl], dtype=np.float64)
            k = np.asarray(W[f"W_k.{l}"][sl], dtype=np.float64)
            v = np.asarray(W[f"W_v.{l}"][sl], dtype=np.float64)
            o = np.asarray(W[f"W_o.{l}"][:,sl], dtype=np.float64)
            qk = q.T @ k / 8
            ov = o @ v
            sq = la.svdvals(qk)[:64]
            so = la.svdvals(ov)[:64]
            print(
                f"L{l}H{j}: Q,K,V,O norms={np.round([la.norm(q),la.norm(k),la.norm(v),la.norm(o)],3)} "
                f"QK norm={la.norm(qk):.3f} top={np.round(sq[:6],3)} sr={np.sum(sq*sq)/sq[0]**2:.2f} "
                f"symfrac={la.norm((qk+qk.T)/2)/la.norm(qk):.3f} "
                f"trcos={np.trace(qk)/(np.sqrt(384)*la.norm(qk)):.4f}; "
                f"OV norm={la.norm(ov):.3f} top={np.round(so[:4],3)} sr={np.sum(so*so)/so[0]**2:.2f} "
                f"trcos={np.trace(ov)/(np.sqrt(384)*la.norm(ov)):.4f}"
            )
```
Permitted experiment harness: python — full request and result
{
  "run": 1,
  "turn": "t001",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\n\nnp.set_printoptions(precision=4, suppress=True, linewidth=160)\n\nwith threadpool_limits(limits=2):\n    print(\"AVAILABLE WEIGHTS\")\n    print([(k, tuple(v.shape)) for k, v in sorted(W.items())])\n    print(\"\\nLAYERNORM PARAMETERS: min, median, max, mean, std, L2 norm\")\n    for 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            print(f\"{name}.{l}\", np.round([a.min(), np.median(a), a.max(), a.mean(), a.std(), la.norm(a)], 5))\n\n    print(\"\\nMATRIX SPECTRA: Frobenius norm, largest singular value, stable rank, top 8 singular values\")\n    for 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 = la.svdvals(a)\n            print(f\"{name}.{l}: norm={la.norm(a):.4f} smax={s[0]:.4f} stable_rank={np.sum(s*s)/s[0]**2:.2f} top={np.round(s[:8],4)}\")\n\n    print(\"\\nHEAD WEIGHT PRODUCTS (no inputs or forward passes)\")\n    print(\"QK=Q.T@K/8; OV=O@V. symfrac=||symmetric part||F/||matrix||F; trcos=trace/(sqrt(384)*norm).\")\n    for l in range(4):\n        for j in range(6):\n            sl = slice(64*j, 64*(j+1))\n            q = np.asarray(W[f\"W_q.{l}\"][sl], dtype=np.float64)\n            k = np.asarray(W[f\"W_k.{l}\"][sl], dtype=np.float64)\n            v = np.asarray(W[f\"W_v.{l}\"][sl], dtype=np.float64)\n            o = np.asarray(W[f\"W_o.{l}\"][:,sl], dtype=np.float64)\n            qk = q.T @ k / 8\n            ov = o @ v\n            sq = la.svdvals(qk)[:64]\n            so = la.svdvals(ov)[:64]\n            print(\n                f\"L{l}H{j}: Q,K,V,O norms={np.round([la.norm(q),la.norm(k),la.norm(v),la.norm(o)],3)} \"\n                f\"QK norm={la.norm(qk):.3f} top={np.round(sq[:6],3)} sr={np.sum(sq*sq)/sq[0]**2:.2f} \"\n                f\"symfrac={la.norm((qk+qk.T)/2)/la.norm(qk):.3f} \"\n                f\"trcos={np.trace(qk)/(np.sqrt(384)*la.norm(qk)):.4f}; \"\n                f\"OV norm={la.norm(ov):.3f} top={np.round(so[:4],3)} sr={np.sum(so*so)/so[0]**2:.2f} \"\n                f\"trcos={np.trace(ov)/(np.sqrt(384)*la.norm(ov)):.4f}\"\n            )",
  "out": "AVAILABLE WEIGHTS\n[('W_fc.0', (1536, 384)), ('W_fc.1', (1536, 384)), ('W_fc.2', (1536, 384)), ('W_fc.3', (1536, 384)), ('W_k.0', (384, 384)), ('W_k.1', (384, 384)), ('W_k.2', (384, 384)), ('W_k.3', (384, 384)), ('W_o.0', (384, 384)), ('W_o.1', (384, 384)), ('W_o.2', (384, 384)), ('W_o.3', (384, 384)), ('W_proj.0', (384, 1536)), ('W_proj.1', (384, 1536)), ('W_proj.2', (384, 1536)), ('W_proj.3', (384, 1536)), ('W_q.0', (384, 384)), ('W_q.1', (384, 384)), ('W_q.2', (384, 384)), ('W_q.3', (384, 384)), ('W_v.0', (384, 384)), ('W_v.1', (384, 384)), ('W_v.2', (384, 384)), ('W_v.3', (384, 384)), ('ln1_b.0', (384,)), ('ln1_b.1', (384,)), ('ln1_b.2', (384,)), ('ln1_b.3', (384,)), ('ln1_w.0', (384,)), ('ln1_w.1', (384,)), ('ln1_w.2', (384,)), ('ln1_w.3', (384,)), ('ln2_b.0', (384,)), ('ln2_b.1', (384,)), ('ln2_b.2', (384,)), ('ln2_b.3', (384,)), ('ln2_w.0', (384,)), ('ln2_w.1', (384,)), ('ln2_w.2', (384,)), ('ln2_w.3', (384,))]\n\nLAYERNORM PARAMETERS: min, median, max, mean, std, L2 norm\nln1_w.0 [-0.001   0.1422  1.0341  0.2403  0.2436  6.7051]\nln1_b.0 [-0.0959  0.0007  0.091   0.0008  0.0287  0.5635]\nln2_w.0 [ 0.0044  0.7876  1.5072  0.8006  0.3295 16.9648]\nln2_b.0 [-0.4986  0.0058  0.3331  0.0022  0.0683  1.3392]\nln1_w.1 [0.1361 0.4535 1.1239 0.4575 0.1022 9.1868]\nln1_b.1 [-1.4712 -0.0071  1.1544 -0.005   0.1672  3.2771]\nln2_w.1 [-0.0019  1.0787  1.4856  1.0429  0.2003 20.8093]\nln2_b.1 [-0.3597 -0.0041  0.3686  0.0005  0.1364  2.6732]\nln1_w.2 [ 0.2078  0.7176  1.0144  0.7052  0.0749 13.8969]\nln1_b.2 [-1.3819 -0.0017  1.0527 -0.001   0.1525  2.9893]\nln2_w.2 [ 0.0011  1.3247  1.4655  1.3123  0.1255 25.8329]\nln2_b.2 [-0.3761  0.0067  0.3886  0.005   0.1537  3.0128]\nln1_w.3 [ 0.2944  0.7581  0.8701  0.7546  0.0542 14.8247]\nln1_b.3 [-0.6069  0.007   0.6502  0.0121  0.0849  1.6801]\nln2_w.3 [ 0.4364  1.2851  1.4306  1.2778  0.0808 25.0891]\nln2_b.3 [-0.6728  0.0019  0.3068  0.0043  0.1276  2.5016]\n\nMATRIX SPECTRA: Frobenius norm, largest singular value, stable rank, top 8 singular values\nW_q.0: norm=32.2912 smax=8.2519 stable_rank=15.31 top=[8.2519 8.2232 7.963  7.819  7.68   7.6416 7.5194 7.2054]\nW_k.0: norm=30.3909 smax=7.6464 stable_rank=15.80 top=[7.6464 7.4363 7.3304 6.9118 6.8285 6.6716 6.4724 6.4464]\nW_v.0: norm=14.8360 smax=4.3390 stable_rank=11.69 top=[4.339  3.5292 3.2699 3.1656 2.9824 2.6465 2.589  2.5519]\nW_o.0: norm=16.3713 smax=7.1033 stable_rank=5.31 top=[7.1033 6.6074 4.2683 2.7702 2.5025 2.285  2.2638 2.2184]\nW_fc.0: norm=46.2486 smax=18.1759 stable_rank=6.47 top=[18.1759 11.0178  8.7366  7.4517  7.2123  7.0621  7.0112  6.6204]\nW_proj.0: norm=39.3075 smax=9.8257 stable_rank=16.00 top=[9.8257 7.3508 6.2651 5.8693 5.7965 5.6574 5.5163 5.4501]\nW_q.1: norm=32.3126 smax=9.9032 stable_rank=10.65 top=[9.9032 7.7974 6.7042 5.9516 5.6721 5.6025 5.4021 5.3125]\nW_k.1: norm=29.2506 smax=13.3072 stable_rank=4.83 top=[13.3072  9.9327  5.8989  5.677   5.0458  4.622   4.4903  4.4044]\nW_v.1: norm=27.3529 smax=5.2845 stable_rank=26.79 top=[5.2845 4.9661 4.87   4.7328 4.6743 4.5853 4.4113 4.3216]\nW_o.1: norm=25.9233 smax=4.9657 stable_rank=27.25 top=[4.9657 3.2412 3.157  3.1119 3.0615 3.0531 2.9942 2.9545]\nW_fc.1: norm=79.1265 smax=21.4881 stable_rank=13.56 top=[21.4881 13.1349 12.8042 12.6247 12.3279 12.1184 11.8504 11.6908]\nW_proj.1: norm=75.7103 smax=8.1935 stable_rank=85.38 top=[8.1935 7.8941 7.1992 6.9972 6.671  6.4329 6.2745 6.1333]\nW_q.2: norm=39.3455 smax=5.7962 stable_rank=46.08 top=[5.7962 5.1962 4.8092 4.6159 4.6003 4.5145 4.4419 4.432 ]\nW_k.2: norm=37.7688 smax=10.6627 stable_rank=12.55 top=[10.6627  8.6244  4.9887  4.3937  4.241   4.0878  3.9564  3.8475]\nW_v.2: norm=33.3048 smax=3.7062 stable_rank=80.75 top=[3.7062 3.6245 3.5788 3.5334 3.5012 3.4679 3.4285 3.4153]\nW_o.2: norm=32.8397 smax=4.1025 stable_rank=64.08 top=[4.1025 3.9708 3.558  3.3813 3.3611 3.2925 3.1918 3.1686]\nW_fc.2: norm=78.9668 smax=15.3634 stable_rank=26.42 top=[15.3634  8.0212  7.8946  7.7247  7.5968  7.4905  7.2574  7.2158]\nW_proj.2: norm=76.8311 smax=9.4036 stable_rank=66.76 top=[9.4036 9.1867 8.7234 8.4703 8.0398 7.7955 7.6145 7.4501]\nW_q.3: norm=32.9482 smax=4.5598 stable_rank=52.21 top=[4.5598 4.2388 4.0054 3.9241 3.8529 3.7087 3.7044 3.6911]\nW_k.3: norm=32.2094 smax=6.0903 stable_rank=27.97 top=[6.0903 4.8989 4.1973 3.4559 3.3531 3.3051 3.2455 3.1608]\nW_v.3: norm=39.7941 smax=5.0598 stable_rank=61.85 top=[5.0598 4.9045 4.8481 4.769  4.6738 4.6053 4.5722 4.5185]\nW_o.3: norm=40.4550 smax=9.5668 stable_rank=17.88 top=[9.5668 8.7592 5.3386 5.139  4.9162 4.8063 4.7743 4.7359]\nW_fc.3: norm=77.7758 smax=14.1624 stable_rank=30.16 top=[14.1624  8.1249  7.9152  7.3262  7.1484  6.8556  6.7628  6.6911]\nW_proj.3: norm=82.3952 smax=26.3094 stable_rank=9.81 top=[26.3094 20.6845 19.6986 19.2612 18.5937 18.1973 17.8617 17.6671]\n\nHEAD WEIGHT PRODUCTS (no inputs or forward passes)\nQK=Q.T@K/8; OV=O@V. symfrac=||symmetric part||F/||matrix||F; trcos=trace/(sqrt(384)*norm).\nL0H0: Q,K,V,O norms=[3.305 4.18  6.039 6.666] QK norm=0.381 top=[0.314 0.112 0.087 0.055 0.041 0.039] sr=1.47 symfrac=0.727 trcos=-0.0289; OV norm=12.904 top=[10.252  5.603  3.475  2.818] sr=1.58 trcos=-0.0007\nL0H1: Q,K,V,O norms=[3.09  3.658 5.382 6.025] QK norm=0.230 top=[0.137 0.096 0.056 0.043 0.037 0.036] sr=2.81 symfrac=0.747 trcos=-0.0343; OV norm=10.148 top=[7.662 5.172 2.582 2.031] sr=1.75 trcos=-0.0038\nL0H2: Q,K,V,O norms=[30.818 28.3    7.765  8.915] QK norm=22.804 top=[7.485 7.16  6.948 6.793 6.404 6.182] sr=9.28 symfrac=0.696 trcos=0.0092; OV norm=13.223 top=[4.001 3.853 3.743 3.544] sr=10.92 trcos=-0.0179\nL0H3: Q,K,V,O norms=[6.821 7.243 5.434 5.029] QK norm=1.419 top=[0.821 0.749 0.402 0.331 0.303 0.267] sr=2.99 symfrac=0.763 trcos=-0.0359; OV norm=7.308 top=[4.305 4.046 2.493 2.078] sr=2.88 trcos=-0.0143\nL0H4: Q,K,V,O norms=[3.971 4.995 5.208 5.587] QK norm=0.826 top=[0.753 0.255 0.135 0.07  0.054 0.043] sr=1.20 symfrac=0.763 trcos=-0.0365; OV norm=7.312 top=[4.879 3.236 2.548 2.083] sr=2.25 trcos=-0.0067\nL0H5: Q,K,V,O norms=[3.19  3.802 6.143 7.162] QK norm=0.322 top=[0.274 0.068 0.049 0.041 0.037 0.037] sr=1.39 symfrac=0.729 trcos=-0.0191; OV norm=13.038 top=[10.314  5.468  3.148  2.935] sr=1.60 trcos=-0.0003\nL1H0: Q,K,V,O norms=[10.716  9.827 11.156  9.984] QK norm=2.958 top=[1.577 1.462 1.035 0.877 0.653 0.522] sr=3.52 symfrac=0.735 trcos=-0.0563; OV norm=21.707 top=[8.099 7.58  6.398 6.202] sr=7.18 trcos=-0.0508\nL1H1: Q,K,V,O norms=[13.292 12.068 11.72  11.303] QK norm=4.439 top=[2.456 2.132 1.344 1.193 0.918 0.869] sr=3.27 symfrac=0.742 trcos=-0.0647; OV norm=22.226 top=[6.583 6.009 5.739 5.681] sr=11.40 trcos=-0.0654\nL1H2: Q,K,V,O norms=[14.476 13.092 10.668 10.272] QK norm=5.223 top=[2.923 2.292 1.508 1.402 1.191 1.074] sr=3.19 symfrac=0.745 trcos=-0.0740; OV norm=19.517 top=[6.135 5.788 5.252 4.987] sr=10.12 trcos=-0.0573\nL1H3: Q,K,V,O norms=[13.358 12.153 10.904  9.928] QK norm=4.605 top=[2.638 2.173 1.454 1.142 1.062 0.813] sr=3.05 symfrac=0.740 trcos=-0.0616; OV norm=21.413 top=[7.767 6.864 6.527 6.06 ] sr=7.60 trcos=-0.0483\nL1H4: Q,K,V,O norms=[13.518 12.129 11.148 10.794] QK norm=4.673 top=[2.87  2.178 1.43  1.085 0.911 0.776] sr=2.65 symfrac=0.737 trcos=-0.0636; OV norm=22.066 top=[6.498 6.253 5.897 5.784] sr=11.53 trcos=-0.0615\nL1H5: Q,K,V,O norms=[13.486 12.132 11.376 11.136] QK norm=4.526 top=[2.618 2.293 1.294 1.053 0.925 0.835] sr=2.99 symfrac=0.730 trcos=-0.0560; OV norm=22.464 top=[6.208 6.019 5.803 5.645] sr=13.09 trcos=-0.0665\nL2H0: Q,K,V,O norms=[16.822 16.208 13.115 12.942] QK norm=4.822 top=[1.457 1.249 1.141 1.088 1.043 0.997] sr=10.95 symfrac=0.702 trcos=0.0026; OV norm=22.814 top=[4.898 4.628 4.553 4.4  ] sr=21.70 trcos=-0.1214\nL2H1: Q,K,V,O norms=[15.963 15.287 13.768 13.525] QK norm=4.139 top=[1.155 0.893 0.881 0.867 0.816 0.794] sr=12.85 symfrac=0.713 trcos=0.0146; OV norm=24.845 top=[5.468 5.157 4.968 4.755] sr=20.64 trcos=-0.1030\nL2H2: Q,K,V,O norms=[13.97  13.239 14.567 14.24 ] QK norm=3.154 top=[1.11  0.945 0.773 0.625 0.589 0.579] sr=8.07 symfrac=0.711 trcos=-0.0328; OV norm=28.707 top=[7.863 7.18  6.901 5.989] sr=13.33 trcos=-0.0831\nL2H3: Q,K,V,O norms=[16.121 15.554 13.498 13.391] QK norm=4.103 top=[1.066 0.993 0.907 0.877 0.822 0.797] sr=14.81 symfrac=0.706 trcos=-0.0184; OV norm=24.505 top=[6.18  5.8   5.539 4.932] sr=15.72 trcos=-0.1037\nL2H4: Q,K,V,O norms=[16.876 16.188 13.241 13.143] QK norm=4.603 top=[1.188 1.105 1.077 1.014 0.915 0.895] sr=15.02 symfrac=0.704 trcos=-0.0102; OV norm=23.279 top=[5.07  4.774 4.66  4.505] sr=21.08 trcos=-0.0998\nL2H5: Q,K,V,O norms=[16.445 15.839 13.339 13.161] QK norm=4.419 top=[1.201 1.063 0.969 0.943 0.934 0.905] sr=13.55 symfrac=0.704 trcos=-0.0148; OV norm=23.342 top=[4.84  4.693 4.49  4.483] sr=23.25 trcos=-0.1110\nL3H0: Q,K,V,O norms=[15.747 15.258 14.874 14.87 ] QK norm=3.905 top=[0.79  0.738 0.734 0.707 0.695 0.688] sr=24.41 symfrac=0.725 trcos=0.0782; OV norm=29.683 top=[8.384 7.583 6.42  6.247] sr=12.54 trcos=-0.1253\nL3H1: Q,K,V,O norms=[14.22  14.197 17.885 18.331] QK norm=3.224 top=[0.571 0.554 0.552 0.536 0.53  0.525] sr=31.82 symfrac=0.778 trcos=0.1765; OV norm=53.398 top=[13.854 13.612 13.378 12.359] sr=14.86 trcos=-0.0670\nL3H2: Q,K,V,O norms=[12.743 12.446 15.523 15.585] QK norm=2.630 top=[0.808 0.724 0.627 0.583 0.54  0.503] sr=10.60 symfrac=0.707 trcos=0.0077; OV norm=35.243 top=[11.891  9.927  8.462  8.285] sr=8.78 trcos=-0.0653\nL3H3: Q,K,V,O norms=[13.283 13.057 15.732 16.118] QK norm=2.794 top=[0.856 0.707 0.587 0.539 0.517 0.511] sr=10.64 symfrac=0.702 trcos=0.0035; OV norm=35.995 top=[12.434 10.397  9.009  8.061] sr=8.38 trcos=-0.0739\nL3H4: Q,K,V,O norms=[14.356 13.874 15.175 15.3  ] QK norm=3.249 top=[0.919 0.738 0.695 0.661 0.629 0.593] sr=12.50 symfrac=0.707 trcos=0.0253; OV norm=31.778 top=[9.526 8.963 7.011 6.951] sr=11.13 trcos=-0.0709\nL3H5: Q,K,V,O norms=[ 9.514  9.231 17.993 18.512] QK norm=1.481 top=[0.483 0.419 0.374 0.349 0.336 0.292] sr=9.40 symfrac=0.716 trcos=0.0060; OV norm=59.294 top=[21.979 19.201 17.421 16.747] sr=7.28 trcos=-0.0517\n",
  "seconds": 1.352,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "a84be157bdc32679e4c17ce957ef2e9e8b52a4065cc18f7e5caee6bbcebb06f8",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
AVAILABLE WEIGHTS
[('W_fc.0', (1536, 384)), ('W_fc.1', (1536, 384)), ('W_fc.2', (1536, 384)), ('W_fc.3', (1536, 384)), ('W_k.0', (384, 384)), ('W_k.1', (384, 384)), ('W_k.2', (384, 384)), ('W_k.3', (384, 384)), ('W_o.0', (384, 384)), ('W_o.1', (384, 384)), ('W_o.2', (384, 384)), ('W_o.3', (384, 384)), ('W_proj.0', (384, 1536)), ('W_proj.1', (384, 1536)), ('W_proj.2', (384, 1536)), ('W_proj.3', (384, 1536)), ('W_q.0', (384, 384)), ('W_q.1', (384, 384)), ('W_q.2', (384, 384)), ('W_q.3', (384, 384)), ('W_v.0', (384, 384)), ('W_v.1', (384, 384)), ('W_v.2', (384, 384)), ('W_v.3', (384, 384)), ('ln1_b.0', (384,)), ('ln1_b.1', (384,)), ('ln1_b.2', (384,)), ('ln1_b.3', (384,)), ('ln1_w.0', (384,)), ('ln1_w.1', (384,)), ('ln1_w.2', (384,)), ('ln1_w.3', (384,)), ('ln2_b.0', (384,)), ('ln2_b.1', (384,)), ('ln2_b.2', (384,)), ('ln2_b.3', (384,)), ('ln2_w.0', (384,)), ('ln2_w.1', (384,)), ('ln2_w.2', (384,)), ('ln2_w.3', (384,))]

LAYERNORM PARAMETERS: min, median, max, mean, std, L2 norm
ln1_w.0 [-0.001   0.1422  1.0341  0.2403  0.2436  6.7051]
ln1_b.0 [-0.0959  0.0007  0.091   0.0008  0.0287  0.5635]
ln2_w.0 [ 0.0044  0.7876  1.5072  0.8006  0.3295 16.9648]
ln2_b.0 [-0.4986  0.0058  0.3331  0.0022  0.0683  1.3392]
ln1_w.1 [0.1361 0.4535 1.1239 0.4575 0.1022 9.1868]
ln1_b.1 [-1.4712 -0.0071  1.1544 -0.005   0.1672  3.2771]
ln2_w.1 [-0.0019  1.0787  1.4856  1.0429  0.2003 20.8093]
ln2_b.1 [-0.3597 -0.0041  0.3686  0.0005  0.1364  2.6732]
ln1_w.2 [ 0.2078  0.7176  1.0144  0.7052  0.0749 13.8969]
ln1_b.2 [-1.3819 -0.0017  1.0527 -0.001   0.1525  2.9893]
ln2_w.2 [ 0.0011  1.3247  1.4655  1.3123  0.1255 25.8329]
ln2_b.2 [-0.3761  0.0067  0.3886  0.005   0.1537  3.0128]
ln1_w.3 [ 0.2944  0.7581  0.8701  0.7546  0.0542 14.8247]
ln1_b.3 [-0.6069  0.007   0.6502  0.0121  0.0849  1.6801]
ln2_w.3 [ 0.4364  1.2851  1.4306  1.2778  0.0808 25.0891]
ln2_b.3 [-0.6728  0.0019  0.3068  0.0043  0.1276  2.5016]

MATRIX SPECTRA: Frobenius norm, largest singular value, stable rank, top 8 singular values
W_q.0: norm=32.2912 smax=8.2519 stable_rank=15.31 top=[8.2519 8.2232 7.963  7.819  7.68   7.6416 7.5194 7.2054]
W_k.0: norm=30.3909 smax=7.6464 stable_rank=15.80 top=[7.6464 7.4363 7.3304 6.9118 6.8285 6.6716 6.4724 6.4464]
W_v.0: norm=14.8360 smax=4.3390 stable_rank=11.69 top=[4.339  3.5292 3.2699 3.1656 2.9824 2.6465 2.589  2.5519]
W_o.0: norm=16.3713 smax=7.1033 stable_rank=5.31 top=[7.1033 6.6074 4.2683 2.7702 2.5025 2.285  2.2638 2.2184]
W_fc.0: norm=46.2486 smax=18.1759 stable_rank=6.47 top=[18.1759 11.0178  8.7366  7.4517  7.2123  7.0621  7.0112  6.6204]
W_proj.0: norm=39.3075 smax=9.8257 stable_rank=16.00 top=[9.8257 7.3508 6.2651 5.8693 5.7965 5.6574 5.5163 5.4501]
W_q.1: norm=32.3126 smax=9.9032 stable_rank=10.65 top=[9.9032 7.7974 6.7042 5.9516 5.6721 5.6025 5.4021 5.3125]
W_k.1: norm=29.2506 smax=13.3072 stable_rank=4.83 top=[13.3072  9.9327  5.8989  5.677   5.0458  4.622   4.4903  4.4044]
W_v.1: norm=27.3529 smax=5.2845 stable_rank=26.79 top=[5.2845 4.9661 4.87   4.7328 4.6743 4.5853 4.4113 4.3216]
W_o.1: norm=25.9233 smax=4.9657 stable_rank=27.25 top=[4.9657 3.2412 3.157  3.1119 3.0615 3.0531 2.9942 2.9545]
W_fc.1: norm=79.1265 smax=21.4881 stable_rank=13.56 top=[21.4881 13.1349 12.8042 12.6247 12.3279 12.1184 11.8504 11.6908]
W_proj.1: norm=75.7103 smax=8.1935 stable_rank=85.38 top=[8.1935 7.8941 7.1992 6.9972 6.671  6.4329 6.2745 6.1333]
W_q.2: norm=39.3455 smax=5.7962 stable_rank=46.08 top=[5.7962 5.1962 4.8092 4.6159 4.6003 4.5145 4.4419 4.432 ]
W_k.2: norm=37.7688 smax=10.6627 stable_rank=12.55 top=[10.6627  8.6244  4.9887  4.3937  4.241   4.0878  3.9564  3.8475]
W_v.2: norm=33.3048 smax=3.7062 stable_rank=80.75 top=[3.7062 3.6245 3.5788 3.5334 3.5012 3.4679 3.4285 3.4153]
W_o.2: norm=32.8397 smax=4.1025 stable_rank=64.08 top=[4.1025 3.9708 3.558  3.3813 3.3611 3.2925 3.1918 3.1686]
W_fc.2: norm=78.9668 smax=15.3634 stable_rank=26.42 top=[15.3634  8.0212  7.8946  7.7247  7.5968  7.4905  7.2574  7.2158]
W_proj.2: norm=76.8311 smax=9.4036 stable_rank=66.76 top=[9.4036 9.1867 8.7234 8.4703 8.0398 7.7955 7.6145 7.4501]
W_q.3: norm=32.9482 smax=4.5598 stable_rank=52.21 top=[4.5598 4.2388 4.0054 3.9241 3.8529 3.7087 3.7044 3.6911]
W_k.3: norm=32.2094 smax=6.0903 stable_rank=27.97 top=[6.0903 4.8989 4.1973 3.4559 3.3531 3.3051 3.2455 3.1608]
W_v.3: norm=39.7941 smax=5.0598 stable_rank=61.85 top=[5.0598 4.9045 4.8481 4.769  4.6738 4.6053 4.5722 4.5185]
W_o.3: norm=40.4550 smax=9.5668 stable_rank=17.88 top=[9.5668 8.7592 5.3386 5.139  4.9162 4.8063 4.7743 4.7359]
W_fc.3: norm=77.7758 smax=14.1624 stable_rank=30.16 top=[14.1624  8.1249  7.9152  7.3262  7.1484  6.8556  6.7628  6.6911]
W_proj.3: norm=82.3952 smax=26.3094 stable_rank=9.81 top=[26.3094 20.6845 19.6986 19.2612 18.5937 18.1973 17.8617 17.6671]

HEAD WEIGHT PRODUCTS (no inputs or forward passes)
QK=Q.T@K/8; OV=O@V. symfrac=||symmetric part||F/||matrix||F; trcos=trace/(sqrt(384)*norm).
L0H0: Q,K,V,O norms=[3.305 4.18  6.039 6.666] QK norm=0.381 top=[0.314 0.112 0.087 0.055 0.041 0.039] sr=1.47 symfrac=0.727 trcos=-0.0289; OV norm=12.904 top=[10.252  5.603  3.475  2.818] sr=1.58 trcos=-0.0007
L0H1: Q,K,V,O norms=[3.09  3.658 5.382 6.025] QK norm=0.230 top=[0.137 0.096 0.056 0.043 0.037 0.036] sr=2.81 symfrac=0.747 trcos=-0.0343; OV norm=10.148 top=[7.662 5.172 2.582 2.031] sr=1.75 trcos=-0.0038
L0H2: Q,K,V,O norms=[30.818 28.3    7.765  8.915] QK norm=22.804 top=[7.485 7.16  6.948 6.793 6.404 6.182] sr=9.28 symfrac=0.696 trcos=0.0092; OV norm=13.223 top=[4.001 3.853 3.743 3.544] sr=10.92 trcos=-0.0179
L0H3: Q,K,V,O norms=[6.821 7.243 5.434 5.029] QK norm=1.419 top=[0.821 0.749 0.402 0.331 0.303 0.267] sr=2.99 symfrac=0.763 trcos=-0.0359; OV norm=7.308 top=[4.305 4.046 2.493 2.078] sr=2.88 trcos=-0.0143
L0H4: Q,K,V,O norms=[3.971 4.995 5.208 5.587] QK norm=0.826 top=[0.753 0.255 0.135 0.07  0.054 0.043] sr=1.20 symfrac=0.763 trcos=-0.0365; OV norm=7.312 top=[4.879 3.236 2.548 2.083] sr=2.25 trcos=-0.0067
L0H5: Q,K,V,O norms=[3.19  3.802 6.143 7.162] QK norm=0.322 top=[0.274 0.068 0.049 0.041 0.037 0.037] sr=1.39 symfrac=0.729 trcos=-0.0191; OV norm=13.038 top=[10.314  5.468  3.148  2.935] sr=1.60 trcos=-0.0003
L1H0: Q,K,V,O norms=[10.716  9.827 11.156  9.984] QK norm=2.958 top=[1.577 1.462 1.035 0.877 0.653 0.522] sr=3.52 symfrac=0.735 trcos=-0.0563; OV norm=21.707 top=[8.099 7.58  6.398 6.202] sr=7.18 trcos=-0.0508
L1H1: Q,K,V,O norms=[13.292 12.068 11.72  11.303] QK norm=4.439 top=[2.456 2.132 1.344 1.193 0.918 0.869] sr=3.27 symfrac=0.742 trcos=-0.0647; OV norm=22.226 top=[6.583 6.009 5.739 5.681] sr=11.40 trcos=-0.0654
L1H2: Q,K,V,O norms=[14.476 13.092 10.668 10.272] QK norm=5.223 top=[2.923 2.292 1.508 1.402 1.191 1.074] sr=3.19 symfrac=0.745 trcos=-0.0740; OV norm=19.517 top=[6.135 5.788 5.252 4.987] sr=10.12 trcos=-0.0573
L1H3: Q,K,V,O norms=[13.358 12.153 10.904  9.928] QK norm=4.605 top=[2.638 2.173 1.454 1.142 1.062 0.813] sr=3.05 symfrac=0.740 trcos=-0.0616; OV norm=21.413 top=[7.767 6.864 6.527 6.06 ] sr=7.60 trcos=-0.0483
L1H4: Q,K,V,O norms=[13.518 12.129 11.148 10.794] QK norm=4.673 top=[2.87  2.178 1.43  1.085 0.911 0.776] sr=2.65 symfrac=0.737 trcos=-0.0636; OV norm=22.066 top=[6.498 6.253 5.897 5.784] sr=11.53 trcos=-0.0615
L1H5: Q,K,V,O norms=[13.486 12.132 11.376 11.136] QK norm=4.526 top=[2.618 2.293 1.294 1.053 0.925 0.835] sr=2.99 symfrac=0.730 trcos=-0.0560; OV norm=22.464 top=[6.208 6.019 5.803 5.645] sr=13.09 trcos=-0.0665
L2H0: Q,K,V,O norms=[16.822 16.208 13.115 12.942] QK norm=4.822 top=[1.457 1.249 1.141 1.088 1.043 0.997] sr=10.95 symfrac=0.702 trcos=0.0026; OV norm=22.814 top=[4.898 4.628 4.553 4.4  ] sr=21.70 trcos=-0.1214
L2H1: Q,K,V,O norms=[15.963 15.287 13.768 13.525] QK norm=4.139 top=[1.155 0.893 0.881 0.867 0.816 0.794] sr=12.85 symfrac=0.713 trcos=0.0146; OV norm=24.845 top=[5.468 5.157 4.968 4.755] sr=20.64 trcos=-0.1030
L2H2: Q,K,V,O norms=[13.97  13.239 14.567 14.24 ] QK norm=3.154 top=[1.11  0.945 0.773 0.625 0.589 0.579] sr=8.07 symfrac=0.711 trcos=-0.0328; OV norm=28.707 top=[7.863 7.18  6.901 5.989] sr=13.33 trcos=-0.0831
L2H3: Q,K,V,O norms=[16.121 15.554 13.498 13.391] QK norm=4.103 top=[1.066 0.993 0.907 0.877 0.822 0.797] sr=14.81 symfrac=0.706 trcos=-0.0184; OV norm=24.505 top=[6.18  5.8   5.539 4.932] sr=15.72 trcos=-0.1037
L2H4: Q,K,V,O norms=[16.876 16.188 13.241 13.143] QK norm=4.603 top=[1.188 1.105 1.077 1.014 0.915 0.895] sr=15.02 symfrac=0.704 trcos=-0.0102; OV norm=23.279 top=[5.07  4.774 4.66  4.505] sr=21.08 trcos=-0.0998
L2H5: Q,K,V,O norms=[16.445 15.839 13.339 13.161] QK norm=4.419 top=[1.201 1.063 0.969 0.943 0.934 0.905] sr=13.55 symfrac=0.704 trcos=-0.0148; OV norm=23.342 top=[4.84  4.693 4.49  4.483] sr=23.25 trcos=-0.1110
L3H0: Q,K,V,O norms=[15.747 15.258 14.874 14.87 ] QK norm=3.905 top=[0.79  0.738 0.734 0.707 0.695 0.688] sr=24.41 symfrac=0.725 trcos=0.0782; OV norm=29.683 top=[8.384 7.583 6.42  6.247] sr=12.54 trcos=-0.1253
L3H1: Q,K,V,O norms=[14.22  14.197 17.885 18.331] QK norm=3.224 top=[0.571 0.554 0.552 0.536 0.53  0.525] sr=31.82 symfrac=0.778 trcos=0.1765; OV norm=53.398 top=[13.854 13.612 13.378 12.359] sr=14.86 trcos=-0.0670
L3H2: Q,K,V,O norms=[12.743 12.446 15.523 15.585] QK norm=2.630 top=[0.808 0.724 0.627 0.583 0.54  0.503] sr=10.60 symfrac=0.707 trcos=0.0077; OV norm=35.243 top=[11.891  9.927  8.462  8.285] sr=8.78 trcos=-0.0653
L3H3: Q,K,V,O norms=[13.283 13.057 15.732 16.118] QK norm=2.794 top=[0.856 0.707 0.587 0.539 0.517 0.511] sr=10.64 symfrac=0.702 trcos=0.0035; OV norm=35.995 top=[12.434 10.397  9.009  8.061] sr=8.38 trcos=-0.0739
L3H4: Q,K,V,O norms=[14.356 13.874 15.175 15.3  ] QK norm=3.249 top=[0.919 0.738 0.695 0.661 0.629 0.593] sr=12.50 symfrac=0.707 trcos=0.0253; OV norm=31.778 top=[9.526 8.963 7.011 6.951] sr=11.13 trcos=-0.0709
L3H5: Q,K,V,O norms=[ 9.514  9.231 17.993 18.512] QK norm=1.481 top=[0.483 0.419 0.374 0.349 0.336 0.292] sr=9.40 symfrac=0.716 trcos=0.0060; OV norm=59.294 top=[21.979 19.201 17.421 16.747] sr=7.28 trcos=-0.0517
t002 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 10,009 characters
OUTPUT of run 1 of at most 100:
```
AVAILABLE WEIGHTS
[('W_fc.0', (1536, 384)), ('W_fc.1', (1536, 384)), ('W_fc.2', (1536, 384)), ('W_fc.3', (1536, 384)), ('W_k.0', (384, 384)), ('W_k.1', (384, 384)), ('W_k.2', (384, 384)), ('W_k.3', (384, 384)), ('W_o.0', (384, 384)), ('W_o.1', (384, 384)), ('W_o.2', (384, 384)), ('W_o.3', (384, 384)), ('W_proj.0', (384, 1536)), ('W_proj.1', (384, 1536)), ('W_proj.2', (384, 1536)), ('W_proj.3', (384, 1536)), ('W_q.0', (384, 384)), ('W_q.1', (384, 384)), ('W_q.2', (384, 384)), ('W_q.3', (384, 384)), ('W_v.0', (384, 384)), ('W_v.1', (384, 384)), ('W_v.2', (384, 384)), ('W_v.3', (384, 384)), ('ln1_b.0', (384,)), ('ln1_b.1', (384,)), ('ln1_b.2', (384,)), ('ln1_b.3', (384,)), ('ln1_w.0', (384,)), ('ln1_w.1', (384,)), ('ln1_w.2', (384,)), ('ln1_w.3', (384,)), ('ln2_b.0', (384,)), ('ln2_b.1', (384,)), ('ln2_b.2', (384,)), ('ln2_b.3', (384,)), ('ln2_w.0', (384,)), ('ln2_w.1', (384,)), ('ln2_w.2', (384,)), ('ln2_w.3', (384,))]

LAYERNORM PARAMETERS: min, median, max, mean, std, L2 norm
ln1_w.0 [-0.001   0.1422  1.0341  0.2403  0.2436  6.7051]
ln1_b.0 [-0.0959  0.0007  0.091   0.0008  0.0287  0.5635]
ln2_w.0 [ 0.0044  0.7876  1.5072  0.8006  0.3295 16.9648]
ln2_b.0 [-0.4986  0.0058  0.3331  0.0022  0.0683  1.3392]
ln1_w.1 [0.1361 0.4535 1.1239 0.4575 0.1022 9.1868]
ln1_b.1 [-1.4712 -0.0071  1.1544 -0.005   0.1672  3.2771]
ln2_w.1 [-0.0019  1.0787  1.4856  1.0429  0.2003 20.8093]
ln2_b.1 [-0.3597 -0.0041  0.3686  0.0005  0.1364  2.6732]
ln1_w.2 [ 0.2078  0.7176  1.0144  0.7052  0.0749 13.8969]
ln1_b.2 [-1.3819 -0.0017  1.0527 -0.001   0.1525  2.9893]
ln2_w.2 [ 0.0011  1.3247  1.4655  1.3123  0.1255 25.8329]
ln2_b.2 [-0.3761  0.0067  0.3886  0.005   0.1537  3.0128]
ln1_w.3 [ 0.2944  0.7581  0.8701  0.7546  0.0542 14.8247]
ln1_b.3 [-0.6069  0.007   0.6502  0.0121  0.0849  1.6801]
ln2_w.3 [ 0.4364  1.2851  1.4306  1.2778  0.0808 25.0891]
ln2_b.3 [-0.6728  0.0019  0.3068  0.0043  0.1276  2.5016]

MATRIX SPECTRA: Frobenius norm, largest singular value, stable rank, top 8 singular values
W_q.0: norm=32.2912 smax=8.2519 stable_rank=15.31 top=[8.2519 8.2232 7.963  7.819  7.68   7.6416 7.5194 7.2054]
W_k.0: norm=30.3909 smax=7.6464 stable_rank=15.80 top=[7.6464 7.4363 7.3304 6.9118 6.8285 6.6716 6.4724 6.4464]
W_v.0: norm=14.8360 smax=4.3390 stable_rank=11.69 top=[4.339  3.5292 3.2699 3.1656 2.9824 2.6465 2.589  2.5519]
W_o.0: norm=16.3713 smax=7.1033 stable_rank=5.31 top=[7.1033 6.6074 4.2683 2.7702 2.5025 2.285  2.2638 2.2184]
W_fc.0: norm=46.2486 smax=18.1759 stable_rank=6.47 top=[18.1759 11.0178  8.7366  7.4517  7.2123  7.0621  7.0112  6.6204]
W_proj.0: norm=39.3075 smax=9.8257 stable_rank=16.00 top=[9.8257 7.3508 6.2651 5.8693 5.7965 5.6574 5.5163 5.4501]
W_q.1: norm=32.3126 smax=9.9032 stable_rank=10.65 top=[9.9032 7.7974 6.7042 5.9516 5.6721 5.6025 5.4021 5.3125]
W_k.1: norm=29.2506 smax=13.3072 stable_rank=4.83 top=[13.3072  9.9327  5.8989  5.677   5.0458  4.622   4.4903  4.4044]
W_v.1: norm=27.3529 smax=5.2845 stable_rank=26.79 top=[5.2845 4.9661 4.87   4.7328 4.6743 4.5853 4.4113 4.3216]
W_o.1: norm=25.9233 smax=4.9657 stable_rank=27.25 top=[4.9657 3.2412 3.157  3.1119 3.0615 3.0531 2.9942 2.9545]
W_fc.1: norm=79.1265 smax=21.4881 stable_rank=13.56 top=[21.4881 13.1349 12.8042 12.6247 12.3279 12.1184 11.8504 11.6908]
W_proj.1: norm=75.7103 smax=8.1935 stable_rank=85.38 top=[8.1935 7.8941 7.1992 6.9972 6.671  6.4329 6.2745 6.1333]
W_q.2: norm=39.3455 smax=5.7962 stable_rank=46.08 top=[5.7962 5.1962 4.8092 4.6159 4.6003 4.5145 4.4419 4.432 ]
W_k.2: norm=37.7688 smax=10.6627 stable_rank=12.55 top=[10.6627  8.6244  4.9887  4.3937  4.241   4.0878  3.9564  3.8475]
W_v.2: norm=33.3048 smax=3.7062 stable_rank=80.75 top=[3.7062 3.6245 3.5788 3.5334 3.5012 3.4679 3.4285 3.4153]
W_o.2: norm=32.8397 smax=4.1025 stable_rank=64.08 top=[4.1025 3.9708 3.558  3.3813 3.3611 3.2925 3.1918 3.1686]
W_fc.2: norm=78.9668 smax=15.3634 stable_rank=26.42 top=[15.3634  8.0212  7.8946  7.7247  7.5968  7.4905  7.2574  7.2158]
W_proj.2: norm=76.8311 smax=9.4036 stable_rank=66.76 top=[9.4036 9.1867 8.7234 8.4703 8.0398 7.7955 7.6145 7.4501]
W_q.3: norm=32.9482 smax=4.5598 stable_rank=52.21 top=[4.5598 4.2388 4.0054 3.9241 3.8529 3.7087 3.7044 3.6911]
W_k.3: norm=32.2094 smax=6.0903 stable_rank=27.97 top=[6.0903 4.8989 4.1973 3.4559 3.3531 3.3051 3.2455 3.1608]
W_v.3: norm=39.7941 smax=5.0598 stable_rank=61.85 top=[5.0598 4.9045 4.8481 4.769  4.6738 4.6053 4.5722 4.5185]
W_o.3: norm=40.4550 smax=9.5668 stable_rank=17.88 top=[9.5668 8.7592 5.3386 5.139  4.9162 4.8063 4.7743 4.7359]
W_fc.3: norm=77.7758 smax=14.1624 stable_rank=30.16 top=[14.1624  8.1249  7.9152  7.3262  7.1484  6.8556  6.7628  6.6911]
W_proj.3: norm=82.3952 smax=26.3094 stable_rank=9.81 top=[26.3094 20.6845 19.6986 19.2612 18.5937 18.1973 17.8617 17.6671]

HEAD WEIGHT PRODUCTS (no inputs or forward passes)
QK=Q.T@K/8; OV=O@V. symfrac=||symmetric part||F/||matrix||F; trcos=trace/(sqrt(384)*norm).
L0H0: Q,K,V,O norms=[3.305 4.18  6.039 6.666] QK norm=0.381 top=[0.314 0.112 0.087 0.055 0.041 0.039] sr=1.47 symfrac=0.727 trcos=-0.0289; OV norm=12.904 top=[10.252  5.603  3.475  2.818] sr=1.58 trcos=-0.0007
L0H1: Q,K,V,O norms=[3.09  3.658 5.382 6.025] QK norm=0.230 top=[0.137 0.096 0.056 0.043 0.037 0.036] sr=2.81 symfrac=0.747 trcos=-0.0343; OV norm=10.148 top=[7.662 5.172 2.582 2.031] sr=1.75 trcos=-0.0038
L0H2: Q,K,V,O norms=[30.818 28.3    7.765  8.915] QK norm=22.804 top=[7.485 7.16  6.948 6.793 6.404 6.182] sr=9.28 symfrac=0.696 trcos=0.0092; OV norm=13.223 top=[4.001 3.853 3.743 3.544] sr=10.92 trcos=-0.0179
L0H3: Q,K,V,O norms=[6.821 7.243 5.434 5.029] QK norm=1.419 top=[0.821 0.749 0.402 0.331 0.303 0.267] sr=2.99 symfrac=0.763 trcos=-0.0359; OV norm=7.308 top=[4.305 4.046 2.493 2.078] sr=2.88 trcos=-0.0143
L0H4: Q,K,V,O norms=[3.971 4.995 5.208 5.587] QK norm=0.826 top=[0.753 0.255 0.135 0.07  0.054 0.043] sr=1.20 symfrac=0.763 trcos=-0.0365; OV norm=7.312 top=[4.879 3.236 2.548 2.083] sr=2.25 trcos=-0.0067
L0H5: Q,K,V,O norms=[3.19  3.802 6.143 7.162] QK norm=0.322 top=[0.274 0.068 0.049 0.041 0.037 0.037] sr=1.39 symfrac=0.729 trcos=-0.0191; OV norm=13.038 top=[10.314  5.468  3.148  2.935] sr=1.60 trcos=-0.0003
L1H0: Q,K,V,O norms=[10.716  9.827 11.156  9.984] QK norm=2.958 top=[1.577 1.462 1.035 0.877 0.653 0.522] sr=3.52 symfrac=0.735 trcos=-0.0563; OV norm=21.707 top=[8.099 7.58  6.398 6.202] sr=7.18 trcos=-0.0508
L1H1: Q,K,V,O norms=[13.292 12.068 11.72  11.303] QK norm=4.439 top=[2.456 2.132 1.344 1.193 0.918 0.869] sr=3.27 symfrac=0.742 trcos=-0.0647; OV norm=22.226 top=[6.583 6.009 5.739 5.681] sr=11.40 trcos=-0.0654
L1H2: Q,K,V,O norms=[14.476 13.092 10.668 10.272] QK norm=5.223 top=[2.923 2.292 1.508 1.402 1.191 1.074] sr=3.19 symfrac=0.745 trcos=-0.0740; OV norm=19.517 top=[6.135 5.788 5.252 4.987] sr=10.12 trcos=-0.0573
L1H3: Q,K,V,O norms=[13.358 12.153 10.904  9.928] QK norm=4.605 top=[2.638 2.173 1.454 1.142 1.062 0.813] sr=3.05 symfrac=0.740 trcos=-0.0616; OV norm=21.413 top=[7.767 6.864 6.527 6.06 ] sr=7.60 trcos=-0.0483
L1H4: Q,K,V,O norms=[13.518 12.129 11.148 10.794] QK norm=4.673 top=[2.87  2.178 1.43  1.085 0.911 0.776] sr=2.65 symfrac=0.737 trcos=-0.0636; OV norm=22.066 top=[6.498 6.253 5.897 5.784] sr=11.53 trcos=-0.0615
L1H5: Q,K,V,O norms=[13.486 12.132 11.376 11.136] QK norm=4.526 top=[2.618 2.293 1.294 1.053 0.925 0.835] sr=2.99 symfrac=0.730 trcos=-0.0560; OV norm=22.464 top=[6.208 6.019 5.803 5.645] sr=13.09 trcos=-0.0665
L2H0: Q,K,V,O norms=[16.822 16.208 13.115 12.942] QK norm=4.822 top=[1.457 1.249 1.141 1.088 1.043 0.997] sr=10.95 symfrac=0.702 trcos=0.0026; OV norm=22.814 top=[4.898 4.628 4.553 4.4  ] sr=21.70 trcos=-0.1214
L2H1: Q,K,V,O norms=[15.963 15.287 13.768 13.525] QK norm=4.139 top=[1.155 0.893 0.881 0.867 0.816 0.794] sr=12.85 symfrac=0.713 trcos=0.0146; OV norm=24.845 top=[5.468 5.157 4.968 4.755] sr=20.64 trcos=-0.1030
L2H2: Q,K,V,O norms=[13.97  13.239 14.567 14.24 ] QK norm=3.154 top=[1.11  0.945 0.773 0.625 0.589 0.579] sr=8.07 symfrac=0.711 trcos=-0.0328; OV norm=28.707 top=[7.863 7.18  6.901 5.989] sr=13.33 trcos=-0.0831
L2H3: Q,K,V,O norms=[16.121 15.554 13.498 13.391] QK norm=4.103 top=[1.066 0.993 0.907 0.877 0.822 0.797] sr=14.81 symfrac=0.706 trcos=-0.0184; OV norm=24.505 top=[6.18  5.8   5.539 4.932] sr=15.72 trcos=-0.1037
L2H4: Q,K,V,O norms=[16.876 16.188 13.241 13.143] QK norm=4.603 top=[1.188 1.105 1.077 1.014 0.915 0.895] sr=15.02 symfrac=0.704 trcos=-0.0102; OV norm=23.279 top=[5.07  4.774 4.66  4.505] sr=21.08 trcos=-0.0998
L2H5: Q,K,V,O norms=[16.445 15.839 13.339 13.161] QK norm=4.419 top=[1.201 1.063 0.969 0.943 0.934 0.905] sr=13.55 symfrac=0.704 trcos=-0.0148; OV norm=23.342 top=[4.84  4.693 4.49  4.483] sr=23.25 trcos=-0.1110
L3H0: Q,K,V,O norms=[15.747 15.258 14.874 14.87 ] QK norm=3.905 top=[0.79  0.738 0.734 0.707 0.695 0.688] sr=24.41 symfrac=0.725 trcos=0.0782; OV norm=29.683 top=[8.384 7.583 6.42  6.247] sr=12.54 trcos=-0.1253
L3H1: Q,K,V,O norms=[14.22  14.197 17.885 18.331] QK norm=3.224 top=[0.571 0.554 0.552 0.536 0.53  0.525] sr=31.82 symfrac=0.778 trcos=0.1765; OV norm=53.398 top=[13.854 13.612 13.378 12.359] sr=14.86 trcos=-0.0670
L3H2: Q,K,V,O norms=[12.743 12.446 15.523 15.585] QK norm=2.630 top=[0.808 0.724 0.627 0.583 0.54  0.503] sr=10.60 symfrac=0.707 trcos=0.0077; OV norm=35.243 top=[11.891  9.927  8.462  8.285] sr=8.78 trcos=-0.0653
L3H3: Q,K,V,O norms=[13.283 13.057 15.732 16.118] QK norm=2.794 top=[0.856 0.707 0.587 0.539 0.517 0.511] sr=10.64 symfrac=0.702 trcos=0.0035; OV norm=35.995 top=[12.434 10.397  9.009  8.061] sr=8.38 trcos=-0.0739
L3H4: Q,K,V,O norms=[14.356 13.874 15.175 15.3  ] QK norm=3.249 top=[0.919 0.738 0.695 0.661 0.629 0.593] sr=12.50 symfrac=0.707 trcos=0.0253; OV norm=31.778 top=[9.526 8.963 7.011 6.951] sr=11.13 trcos=-0.0709
L3H5: Q,K,V,O norms=[ 9.514  9.231 17.993 18.512] QK norm=1.481 top=[0.483 0.419 0.374 0.349 0.336 0.292] sr=9.40 symfrac=0.716 trcos=0.0060; OV norm=59.294 top=[21.979 19.201 17.421 16.747] sr=7.28 trcos=-0.0517

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4, suppress=True, linewidth=180)

def cosine(a, b):
    return np.vdot(a, b).real / (la.norm(a) * la.norm(b) + 1e-30)

with threadpool_limits(limits=2):
    QK, OV = {}, {}
    for l in range(4):
        g = np.asarray(W[f"ln1_w.{l}"], dtype=float)
        for j in range(6):
            s = slice(64*j, 64*j+64)
            q = np.asarray(W[f"W_q.{l}"][s], dtype=float) * g
            k = np.asarray(W[f"W_k.{l}"][s], dtype=float) * g
            v = np.asarray(W[f"W_v.{l}"][s], dtype=float) * g
            o = np.asarray(W[f"W_o.{l}"][:,s], dtype=float)
            QK[l,j] = q.T @ k / 8
            OV[l,j] = o @ v

    print("GAIN-WEIGHTED PRODUCTS: A=diag(ln1_w) Q.T K diag(ln1_w)/8, B=O V diag(ln1_w)")
    print("These are matrix products only; no LayerNorm, attention, or MLP evaluations.")
    for l in range(4):
        for j in range(6):
            a, b = QK[l,j], OV[l,j]
            s = la.svdvals(a)
            ev = la.eigvals(a)
            ev = ev[np.argsort(-abs(ev))][:12]
            print(f"L{l}H{j}: A_norm={la.norm(a):.4f}, sr={sum(s*s)/s[0]**2:.2f}, "
                  f"symfrac={la.norm((a+a.T)/2)/la.norm(a):.3f}, B_norm={la.norm(b):.3f}, "
                  f"top_s={np.round(s[:6],4)}, eig={np.round(ev[:8],4)}")

    print("\nPAIRWISE QK COSINES WITHIN EACH LAYER")
    for l in range(4):
        print("L",l)
        print(np.array([[cosine(QK[l,i], QK[l,j]) for j in range(6)] for i in range(6)]))

    print("\nL0H2 FULL NONZERO SINGULAR SPECTRUM")
    print(la.svdvals(QK[0,2])[:64])
    print("\nL0H2 TOP EIGENVALUES OF UNWEIGHTED Q.T K/8")
    q = np.asarray(W["W_q.0"][128:192], dtype=float)
    k = np.asarray(W["W_k.0"][128:192], dtype=float)
    ev = la.eigvals(k @ q.T / 8)
    print(ev[np.argsort(-abs(ev))][:32])

    print("\nRESIDUAL COORDINATE CONCENTRATION")
    for l in range(4):
        g = np.asarray(W[f"ln1_w.{l}"], dtype=float)
        q = np.asarray(W[f"W_q.{l}"], dtype=float)
        k = np.asarray(W[f"W_k.{l}"], dtype=float)
        energy = np.sum((q*g)**2 + (k*g)**2, axis=0)
        ix = np.argsort(-energy)[:16]
        print(f"L{l}: top_coordinates={ix}; fraction_top16={energy[ix].sum()/energy.sum():.4f}; gains={np.round(g[ix],4)}")

    print("\nDIRECT OV ALIGNMENT WITH LATER QK (matrix composition only)")
    print("For each later head: strongest earlier B on key side; enrichment = ||A B||F/(||A||F||B||F/sqrt(384)).")
    for l in range(1,4):
        for j in range(6):
            a = QK[l,j]
            scores = []
            for m in range(l):
                for h in range(6):
                    b = OV[m,h]
                    c = a @ b
                    enrich = la.norm(c)*np.sqrt(384)/(la.norm(a)*la.norm(b))
                    trace_ratio = np.trace(c)/(la.norm(c)*np.sqrt(384)+1e-30)
                    scores.append((enrich, m, h, trace_ratio))
            print(f"L{l}H{j}:", [(f"L{m}H{h}", round(e,3), round(t,4)) for e,m,h,t in sorted(scores, reverse=True)[:5]])
```
Permitted experiment harness: python — full request and result
{
  "run": 2,
  "turn": "t002",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4, suppress=True, linewidth=180)\n\ndef cosine(a, b):\n    return np.vdot(a, b).real / (la.norm(a) * la.norm(b) + 1e-30)\n\nwith threadpool_limits(limits=2):\n    QK, OV = {}, {}\n    for l in range(4):\n        g = np.asarray(W[f\"ln1_w.{l}\"], dtype=float)\n        for j in range(6):\n            s = slice(64*j, 64*j+64)\n            q = np.asarray(W[f\"W_q.{l}\"][s], dtype=float) * g\n            k = np.asarray(W[f\"W_k.{l}\"][s], dtype=float) * g\n            v = np.asarray(W[f\"W_v.{l}\"][s], dtype=float) * g\n            o = np.asarray(W[f\"W_o.{l}\"][:,s], dtype=float)\n            QK[l,j] = q.T @ k / 8\n            OV[l,j] = o @ v\n\n    print(\"GAIN-WEIGHTED PRODUCTS: A=diag(ln1_w) Q.T K diag(ln1_w)/8, B=O V diag(ln1_w)\")\n    print(\"These are matrix products only; no LayerNorm, attention, or MLP evaluations.\")\n    for l in range(4):\n        for j in range(6):\n            a, b = QK[l,j], OV[l,j]\n            s = la.svdvals(a)\n            ev = la.eigvals(a)\n            ev = ev[np.argsort(-abs(ev))][:12]\n            print(f\"L{l}H{j}: A_norm={la.norm(a):.4f}, sr={sum(s*s)/s[0]**2:.2f}, \"\n                  f\"symfrac={la.norm((a+a.T)/2)/la.norm(a):.3f}, B_norm={la.norm(b):.3f}, \"\n                  f\"top_s={np.round(s[:6],4)}, eig={np.round(ev[:8],4)}\")\n\n    print(\"\\nPAIRWISE QK COSINES WITHIN EACH LAYER\")\n    for l in range(4):\n        print(\"L\",l)\n        print(np.array([[cosine(QK[l,i], QK[l,j]) for j in range(6)] for i in range(6)]))\n\n    print(\"\\nL0H2 FULL NONZERO SINGULAR SPECTRUM\")\n    print(la.svdvals(QK[0,2])[:64])\n    print(\"\\nL0H2 TOP EIGENVALUES OF UNWEIGHTED Q.T K/8\")\n    q = np.asarray(W[\"W_q.0\"][128:192], dtype=float)\n    k = np.asarray(W[\"W_k.0\"][128:192], dtype=float)\n    ev = la.eigvals(k @ q.T / 8)\n    print(ev[np.argsort(-abs(ev))][:32])\n\n    print(\"\\nRESIDUAL COORDINATE CONCENTRATION\")\n    for l in range(4):\n        g = np.asarray(W[f\"ln1_w.{l}\"], dtype=float)\n        q = np.asarray(W[f\"W_q.{l}\"], dtype=float)\n        k = np.asarray(W[f\"W_k.{l}\"], dtype=float)\n        energy = np.sum((q*g)**2 + (k*g)**2, axis=0)\n        ix = np.argsort(-energy)[:16]\n        print(f\"L{l}: top_coordinates={ix}; fraction_top16={energy[ix].sum()/energy.sum():.4f}; gains={np.round(g[ix],4)}\")\n\n    print(\"\\nDIRECT OV ALIGNMENT WITH LATER QK (matrix composition only)\")\n    print(\"For each later head: strongest earlier B on key side; enrichment = ||A B||F/(||A||F||B||F/sqrt(384)).\")\n    for l in range(1,4):\n        for j in range(6):\n            a = QK[l,j]\n            scores = []\n            for m in range(l):\n                for h in range(6):\n                    b = OV[m,h]\n                    c = a @ b\n                    enrich = la.norm(c)*np.sqrt(384)/(la.norm(a)*la.norm(b))\n                    trace_ratio = np.trace(c)/(la.norm(c)*np.sqrt(384)+1e-30)\n                    scores.append((enrich, m, h, trace_ratio))\n            print(f\"L{l}H{j}:\", [(f\"L{m}H{h}\", round(e,3), round(t,4)) for e,m,h,t in sorted(scores, reverse=True)[:5]])",
  "out": "GAIN-WEIGHTED PRODUCTS: A=diag(ln1_w) Q.T K diag(ln1_w)/8, B=O V diag(ln1_w)\nThese are matrix products only; no LayerNorm, attention, or MLP evaluations.\nL0H0: A_norm=0.0546, sr=1.13, symfrac=0.715, B_norm=9.043, top_s=[0.0514 0.0114 0.0071 0.0046 0.0043 0.0036], eig=[ 0.0072+0.j     -0.004 +0.j     -0.0024+0.j     -0.0005+0.0014j -0.0005-0.0014j  0.0002+0.0012j  0.0002-0.0012j -0.0011+0.0004j]\nL0H1: A_norm=0.0295, sr=1.40, symfrac=0.724, B_norm=6.966, top_s=[0.025  0.0095 0.0053 0.0044 0.0035 0.0031], eig=[-0.0043+0.0011j -0.0043-0.0011j  0.002 +0.j     -0.0015+0.0007j -0.0015-0.0007j -0.0012+0.j     -0.0008+0.0008j -0.0008-0.0008j]\nL0H2: A_norm=4.0784, sr=8.31, symfrac=0.690, B_norm=2.540, top_s=[1.415  1.2749 1.2149 1.2061 1.159  1.0942], eig=[ 0.6322+0.8142j  0.6322-0.8142j -0.23  +0.9352j -0.23  -0.9352j  0.3332+0.8752j  0.3332-0.8752j -0.7788+0.3341j -0.7788-0.3341j]\nL0H3: A_norm=0.3946, sr=1.21, symfrac=0.860, B_norm=1.766, top_s=[0.3588 0.1191 0.0776 0.0425 0.0326 0.0273], eig=[ 0.2617+0.j      0.0777+0.j      0.0101+0.0309j  0.0101-0.0309j -0.0173+0.0052j -0.0173-0.0052j -0.0175+0.j     -0.0148+0.0026j]\nL0H4: A_norm=0.1063, sr=1.42, symfrac=0.709, B_norm=4.034, top_s=[0.0892 0.0536 0.0165 0.0065 0.0052 0.0041], eig=[-0.0279+0.0279j -0.0279-0.0279j -0.0078+0.j     -0.0022+0.j     -0.0011+0.0014j -0.0011-0.0014j -0.0015+0.j      0.0005-0.0011j]\nL0H5: A_norm=0.0776, sr=1.04, symfrac=0.762, B_norm=9.311, top_s=[0.0759 0.0108 0.0046 0.0037 0.0034 0.0029], eig=[-0.0311+0.j      0.0007+0.0017j  0.0007-0.0017j -0.0013+0.0003j -0.0013-0.0003j -0.0005+0.0011j -0.0005-0.0011j  0.0006-0.0008j]\nL1H0: A_norm=0.8874, sr=2.20, symfrac=0.710, B_norm=10.122, top_s=[0.5978 0.4823 0.2477 0.194  0.1316 0.1118], eig=[ 0.0276+0.1058j  0.0276-0.1058j -0.0784+0.j     -0.0652+0.0282j -0.0652-0.0282j -0.055 +0.007j  -0.055 -0.007j  -0.0469+0.j    ]\nL1H1: A_norm=1.3157, sr=2.23, symfrac=0.714, B_norm=10.333, top_s=[0.8818 0.6663 0.4114 0.2669 0.203  0.1785], eig=[ 0.069 +0.1912j  0.069 -0.1912j -0.1533+0.j     -0.1067+0.0244j -0.1067-0.0244j -0.1076+0.0063j -0.1076-0.0063j -0.0862+0.j    ]\nL1H2: A_norm=1.5512, sr=2.43, symfrac=0.713, B_norm=9.181, top_s=[0.9947 0.8604 0.3835 0.3254 0.2644 0.2271], eig=[ 0.0955+0.246j   0.0955-0.246j  -0.1678+0.j     -0.1413+0.0645j -0.1413-0.0645j -0.1349+0.0089j -0.1349-0.0089j -0.1036+0.0298j]\nL1H3: A_norm=1.3759, sr=2.24, symfrac=0.713, B_norm=10.051, top_s=[0.9196 0.7386 0.4078 0.2536 0.2205 0.1718], eig=[ 0.0931+0.2031j  0.0931-0.2031j -0.1754+0.j     -0.1129+0.0083j -0.1129-0.0083j -0.1071+0.j     -0.0401+0.091j  -0.0401-0.091j ]\nL1H4: A_norm=1.4507, sr=2.34, symfrac=0.711, B_norm=10.401, top_s=[0.9477 0.8684 0.3597 0.259  0.1933 0.1725], eig=[ 0.0609+0.2141j  0.0609-0.2141j -0.1348+0.0091j -0.1348-0.0091j -0.1275+0.j     -0.0988+0.j     -0.0816+0.0532j -0.0816-0.0532j]\nL1H5: A_norm=1.3383, sr=2.24, symfrac=0.706, B_norm=10.620, top_s=[0.8932 0.7393 0.3722 0.2296 0.204  0.1849], eig=[ 0.0873+0.2208j  0.0873-0.2208j -0.1207+0.0123j -0.1207-0.0123j -0.1   +0.0043j -0.1   -0.0043j -0.0743+0.0552j -0.0743-0.0552j]\nL2H0: A_norm=2.4382, sr=12.31, symfrac=0.695, B_norm=16.307, top_s=[0.6949 0.6297 0.6024 0.5633 0.5301 0.5065], eig=[ 0.0526+0.306j   0.0526-0.306j   0.0363+0.183j   0.0363-0.183j   0.1184+0.0804j  0.1184-0.0804j -0.0766+0.0978j -0.0766-0.0978j]\nL2H1: A_norm=2.0681, sr=15.42, symfrac=0.711, B_norm=17.722, top_s=[0.5267 0.4494 0.4325 0.4171 0.4097 0.3951], eig=[ 0.0271+0.1441j  0.0271-0.1441j  0.1258+0.0113j  0.1258-0.0113j  0.1092+0.0374j  0.1092-0.0374j -0.084 +0.0709j -0.084 -0.0709j]\nL2H2: A_norm=1.6133, sr=7.06, symfrac=0.706, B_norm=20.575, top_s=[0.6072 0.503  0.4013 0.3172 0.2979 0.2866], eig=[ 0.0363+0.1743j  0.0363-0.1743j -0.1058+0.j      0.0675+0.0552j  0.0675-0.0552j -0.0836-0.0147j -0.0836+0.0147j -0.0761+0.0139j]\nL2H3: A_norm=2.0871, sr=13.79, symfrac=0.698, B_norm=17.506, top_s=[0.562  0.5256 0.4916 0.4403 0.4116 0.3945], eig=[ 0.0718+0.3068j  0.0718-0.3068j -0.0764+0.084j  -0.0764-0.084j  -0.1022-0.0418j -0.1022+0.0418j -0.1042+0.0055j -0.1042-0.0055j]\nL2H4: A_norm=2.3444, sr=13.94, symfrac=0.698, B_norm=16.624, top_s=[0.6279 0.6043 0.5322 0.5082 0.467  0.4514], eig=[ 0.0613+0.3024j  0.0613-0.3024j  0.0769+0.1016j  0.0769-0.1016j -0.0811-0.091j  -0.0811+0.091j  -0.1183+0.j     -0.1109+0.0277j]\nL2H5: A_norm=2.2511, sr=12.43, symfrac=0.697, B_norm=16.684, top_s=[0.6384 0.5397 0.4937 0.4762 0.4679 0.4519], eig=[ 0.0458+0.3023j  0.0458-0.3023j -0.1412+0.j     -0.0716+0.1085j -0.0716-0.1085j -0.1192+0.0373j -0.1192-0.0373j  0.0509+0.1089j]\nL3H0: A_norm=2.2327, sr=24.45, symfrac=0.725, B_norm=22.598, top_s=[0.4515 0.4199 0.4181 0.4053 0.4008 0.3946], eig=[0.1821+0.j     0.1411+0.0785j 0.1411-0.0785j 0.1581-0.0188j 0.1581+0.0188j 0.1434+0.0562j 0.1434-0.0562j 0.1412+0.0357j]\nL3H1: A_norm=1.8309, sr=31.24, symfrac=0.778, B_norm=40.869, top_s=[0.3276 0.3174 0.3092 0.3059 0.3008 0.2979], eig=[0.171 +0.0673j 0.171 -0.0673j 0.1739+0.0596j 0.1739-0.0596j 0.1486+0.0815j 0.1486-0.0815j 0.1641-0.0312j 0.1641+0.0312j]\nL3H2: A_norm=1.5058, sr=10.23, symfrac=0.706, B_norm=26.882, top_s=[0.4708 0.4111 0.3556 0.3321 0.3087 0.2904], eig=[ 0.0301+0.1338j  0.0301-0.1338j  0.081 +0.0387j  0.081 -0.0387j -0.0348+0.0816j -0.0348-0.0816j  0.0823+0.j     -0.0765+0.j    ]\nL3H3: A_norm=1.5992, sr=10.34, symfrac=0.701, B_norm=27.431, top_s=[0.4974 0.4025 0.3358 0.3039 0.2956 0.2949], eig=[ 0.0233+0.1587j  0.0233-0.1587j  0.099 +0.j     -0.0228+0.0945j -0.0228-0.0945j  0.0762+0.0363j  0.0762-0.0363j  0.0778+0.j    ]\nL3H4: A_norm=1.8560, sr=12.32, symfrac=0.707, B_norm=24.198, top_s=[0.5288 0.4125 0.3981 0.3736 0.3603 0.3361], eig=[0.0677+0.0916j 0.0677-0.0916j 0.0027+0.1082j 0.0027-0.1082j 0.0982+0.0395j 0.0982-0.0395j 0.1025+0.j     0.0706+0.0664j]\nL3H5: A_norm=0.8349, sr=10.36, symfrac=0.716, B_norm=45.401, top_s=[0.2594 0.2349 0.2122 0.1963 0.1906 0.1595], eig=[0.0754+0.035j  0.0754-0.035j  0.0779+0.j     0.021 +0.0476j 0.021 -0.0476j 0.0222+0.0351j 0.0222-0.0351j 0.0376+0.0108j]\n\nPAIRWISE QK COSINES WITHIN EACH LAYER\nL 0\n[[ 1.      0.209  -0.0156 -0.0138  0.1151  0.0181]\n [ 0.209   1.     -0.0039 -0.0356  0.2091 -0.0084]\n [-0.0156 -0.0039  1.      0.2393 -0.0061 -0.0256]\n [-0.0138 -0.0356  0.2393  1.     -0.0536 -0.0579]\n [ 0.1151  0.2091 -0.0061 -0.0536  1.      0.1721]\n [ 0.0181 -0.0084 -0.0256 -0.0579  0.1721  1.    ]]\nL 1\n[[1.     0.578  0.4169 0.4486 0.4554 0.4652]\n [0.578  1.     0.5504 0.4889 0.5564 0.5281]\n [0.4169 0.5504 1.     0.6087 0.6676 0.5649]\n [0.4486 0.4889 0.6087 1.     0.6442 0.5634]\n [0.4554 0.5564 0.6676 0.6442 1.     0.5778]\n [0.4652 0.5281 0.5649 0.5634 0.5778 1.    ]]\nL 2\n[[1.     0.0783 0.1327 0.1034 0.112  0.1062]\n [0.0783 1.     0.0931 0.0855 0.0767 0.0704]\n [0.1327 0.0931 1.     0.1188 0.1265 0.1248]\n [0.1034 0.0855 0.1188 1.     0.1198 0.1131]\n [0.112  0.0767 0.1265 0.1198 1.     0.1074]\n [0.1062 0.0704 0.1248 0.1131 0.1074 1.    ]]\nL 3\n[[1.     0.0706 0.0444 0.036  0.0755 0.03  ]\n [0.0706 1.     0.0208 0.0236 0.0301 0.045 ]\n [0.0444 0.0208 1.     0.0946 0.1035 0.0545]\n [0.036  0.0236 0.0946 1.     0.0836 0.0652]\n [0.0755 0.0301 0.1035 0.0836 1.     0.0493]\n [0.03   0.045  0.0545 0.0652 0.0493 1.    ]]\n\nL0H2 FULL NONZERO SINGULAR SPECTRUM\n[1.415  1.2749 1.2149 1.2061 1.159  1.0942 1.0863 1.0219 0.9558 0.9159 0.8613 0.7816 0.7279 0.7214 0.5146 0.4919 0.4289 0.3761 0.3219 0.29   0.189  0.1656 0.1282 0.1157 0.1096\n 0.0981 0.0751 0.0561 0.0492 0.0435 0.0371 0.0336 0.0302 0.0272 0.0242 0.0196 0.0195 0.0157 0.0145 0.013  0.0111 0.0096 0.0088 0.0086 0.0077 0.0073 0.0065 0.0064 0.0059 0.0048\n 0.0043 0.0039 0.0034 0.0032 0.0031 0.0025 0.0024 0.002  0.0019 0.0018 0.0016 0.0015 0.0014 0.0011]\n\nL0H2 TOP EIGENVALUES OF UNWEIGHTED Q.T K/8\n[-0.8672+5.7867j -0.8672-5.7867j  1.92  +4.997j   1.92  -4.997j   4.415 +2.6762j  4.415 -2.6762j -4.9189+0.j     -4.1248+2.3614j -4.1248-2.3614j  2.2873+4.0016j  2.2873-4.0016j\n  3.8125+0.j      3.0783+0.5985j  3.0783-0.5985j -2.3696-1.5219j -2.3696+1.5219j -1.8129+0.j     -1.2196+1.0581j -1.2196-1.0581j  1.0283+1.2431j  1.0283-1.2431j -0.764 +0.j\n  0.1563+0.6708j  0.1563-0.6708j -0.2455+0.579j  -0.2455-0.579j   0.6239+0.j     -0.4258+0.j      0.1483+0.2689j  0.1483-0.2689j -0.0088-0.1973j -0.0088+0.1973j]\n\nRESIDUAL COORDINATE CONCENTRATION\nL0: top_coordinates=[162  60  35 223 246   4 180 184 300 311 239 136 178 222  72  65]; fraction_top16=0.3677; gains=[1.0341 0.9844 0.937  0.98   0.8815 0.8678 0.9294 0.8446 0.9371 0.9443 0.9385 0.8959 0.8898 0.8362 0.7062 0.8244]\nL1: top_coordinates=[222  72  78 273 179 302 351 254 136 200 111 211  42 252 314 114]; fraction_top16=0.4125; gains=[1.1239 0.7995 0.7811 0.4613 0.6558 0.7671 0.6989 0.3414 0.4917 0.6776 0.6917 0.6343 0.657  0.5774 0.6457 0.4324]\nL2: top_coordinates=[222  78 101  94 321 181 100 117 250 126  11 247 122 205 264 225]; fraction_top16=0.1398; gains=[1.0144 0.9057 0.7889 0.817  0.8106 0.7813 0.7841 0.8122 0.813  0.8108 0.7635 0.7678 0.7934 0.8028 0.795  0.7764]\nL3: top_coordinates=[222  78 187 164  98 177 147  83 178 201  27 232 357 143 295 318]; fraction_top16=0.0724; gains=[0.8271 0.7626 0.8177 0.8323 0.8021 0.8597 0.8001 0.8151 0.8204 0.8132 0.8432 0.8405 0.7982 0.8161 0.81   0.8387]\n\nDIRECT OV ALIGNMENT WITH LATER QK (matrix composition only)\nFor each later head: strongest earlier B on key side; enrichment = ||A B||F/(||A||F||B||F/sqrt(384)).\nL1H0: [('L0H5', np.float64(10.954), np.float64(0.0004)), ('L0H0', np.float64(10.712), np.float64(0.0036)), ('L0H1', np.float64(10.363), np.float64(0.0016)), ('L0H4', np.float64(10.234), np.float64(0.0031)), ('L0H3', np.float64(3.044), np.float64(-0.0061))]\nL1H1: [('L0H5', np.float64(10.982), np.float64(0.0017)), ('L0H1', np.float64(9.858), np.float64(0.0033)), ('L0H0', np.float64(9.79), np.float64(0.0047)), ('L0H4', np.float64(9.51), np.float64(0.0052)), ('L0H3', np.float64(2.864), np.float64(-0.005))]\nL1H2: [('L0H5', np.float64(10.822), np.float64(0.002)), ('L0H1', np.float64(9.857), np.float64(0.0042)), ('L0H0', np.float64(9.775), np.float64(0.0051)), ('L0H4', np.float64(9.47), np.float64(0.0057)), ('L0H3', np.float64(3.131), np.float64(-0.0019))]\nL1H3: [('L0H5', np.float64(11.072), np.float64(0.0025)), ('L0H0', np.float64(10.019), np.float64(0.0058)), ('L0H1', np.float64(9.935), np.float64(0.0047)), ('L0H4', np.float64(9.625), np.float64(0.0062)), ('L0H3', np.float64(2.943), np.float64(-0.0034))]\nL1H4: [('L0H5', np.float64(11.164), np.float64(0.0041)), ('L0H0', np.float64(10.567), np.float64(0.0075)), ('L0H1', np.float64(10.381), np.float64(0.0068)), ('L0H4', np.float64(10.099), np.float64(0.0083)), ('L0H3', np.float64(3.341), np.float64(-0.0014))]\nL1H5: [('L0H5', np.float64(10.958), np.float64(-0.001)), ('L0H1', np.float64(9.864), np.float64(0.0018)), ('L0H0', np.float64(9.855), np.float64(0.0039)), ('L0H4', np.float64(9.48), np.float64(0.0038)), ('L0H3', np.float64(3.016), np.float64(-0.0046))]\nL2H0: [('L0H5', np.float64(4.344), np.float64(0.0037)), ('L0H1', np.float64(3.956), np.float64(0.0031)), ('L0H0', np.float64(3.888), np.float64(0.004)), ('L0H4', np.float64(3.749), np.float64(0.0049)), ('L0H3', np.float64(1.843), np.float64(-0.0099))]\nL2H1: [('L0H5', np.float64(3.173), np.float64(0.0037)), ('L0H1', np.float64(2.807), np.float64(0.0023)), ('L0H0', np.float64(2.721), np.float64(0.0036)), ('L0H4', np.float64(2.689), np.float64(0.0039)), ('L0H3', np.float64(1.536), np.float64(-0.0066))]\nL2H2: [('L0H5', np.float64(5.912), np.float64(0.0017)), ('L0H1', np.float64(5.461), np.float64(0.0044)), ('L0H0', np.float64(5.288), np.float64(0.0056)), ('L0H4', np.float64(5.152), np.float64(0.0069)), ('L0H3', np.float64(2.704), np.float64(-0.0064))]\nL2H3: [('L0H5', np.float64(4.295), np.float64(0.005)), ('L0H1', np.float64(4.068), np.float64(0.004)), ('L0H0', np.float64(4.057), np.float64(0.0056)), ('L0H4', np.float64(3.918), np.float64(0.0059)), ('L0H3', np.float64(1.816), np.float64(-0.0066))]\nL2H4: [('L0H5', np.float64(4.133), np.float64(0.0051)), ('L0H1', np.float64(4.02), np.float64(0.0055)), ('L0H0', np.float64(4.017), np.float64(0.0073)), ('L0H4', np.float64(3.866), np.float64(0.0077)), ('L0H3', np.float64(1.929), np.float64(-0.0087))]\nL2H5: [('L0H5', np.float64(4.191), np.float64(0.004)), ('L0H1', np.float64(3.946), np.float64(0.0037)), ('L0H0', np.float64(3.926), np.float64(0.0056)), ('L0H4', np.float64(3.798), np.float64(0.0056)), ('L0H3', np.float64(1.812), np.float64(-0.0095))]\nL3H0: [('L0H5', np.float64(1.743), np.float64(0.004)), ('L0H0', np.float64(1.607), np.float64(0.0028)), ('L0H1', np.float64(1.584), np.float64(0.003)), ('L0H4', np.float64(1.581), np.float64(0.0044)), ('L1H5', np.float64(1.074), np.float64(-0.0161))]\nL3H1: [('L0H2', np.float64(1.586), np.float64(0.0598)), ('L1H3', np.float64(1.449), np.float64(0.0392)), ('L2H3', np.float64(1.335), np.float64(0.0248)), ('L2H2', np.float64(1.309), np.float64(0.0243)), ('L2H1', np.float64(1.272), np.float64(0.0638))]\nL3H2: [('L0H5', np.float64(4.268), np.float64(0.0052)), ('L0H0', np.float64(3.656), np.float64(0.0044)), ('L0H1', np.float64(3.484), np.float64(0.0029)), ('L0H4', np.float64(3.405), np.float64(0.0041)), ('L0H3', np.float64(1.379), np.float64(-0.0042))]\nL3H3: [('L0H5', np.float64(4.065), np.float64(0.0035)), ('L0H0', np.float64(3.504), np.float64(0.003)), ('L0H1', np.float64(3.441), np.float64(0.0014)), ('L0H4', np.float64(3.373), np.float64(0.0025)), ('L0H3', np.float64(1.357), np.float64(-0.0013))]\nL3H4: [('L0H5', np.float64(3.368), np.float64(0.0084)), ('L0H0', np.float64(2.886), np.float64(0.0068)), ('L0H1', np.float64(2.849), np.float64(0.0072)), ('L0H4', np.float64(2.789), np.float64(0.0082)), ('L0H3', np.float64(1.28), np.float64(-0.001))]\nL3H5: [('L0H0', np.float64(1.439), np.float64(-0.0035)), ('L0H3', np.float64(1.414), np.float64(-0.0029)), ('L0H5', np.float64(1.38), np.float64(-0.0022)), ('L0H4', np.float64(1.341), np.float64(-0.0033)), ('L0H1', np.float64(1.281), np.float64(-0.0036))]\n",
  "seconds": 1.384,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "60426258bb806e3d2ef398544e512d4327d88bf7cd24314ede1e59ed1c61fc1c",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
GAIN-WEIGHTED PRODUCTS: A=diag(ln1_w) Q.T K diag(ln1_w)/8, B=O V diag(ln1_w)
These are matrix products only; no LayerNorm, attention, or MLP evaluations.
L0H0: A_norm=0.0546, sr=1.13, symfrac=0.715, B_norm=9.043, top_s=[0.0514 0.0114 0.0071 0.0046 0.0043 0.0036], eig=[ 0.0072+0.j     -0.004 +0.j     -0.0024+0.j     -0.0005+0.0014j -0.0005-0.0014j  0.0002+0.0012j  0.0002-0.0012j -0.0011+0.0004j]
L0H1: A_norm=0.0295, sr=1.40, symfrac=0.724, B_norm=6.966, top_s=[0.025  0.0095 0.0053 0.0044 0.0035 0.0031], eig=[-0.0043+0.0011j -0.0043-0.0011j  0.002 +0.j     -0.0015+0.0007j -0.0015-0.0007j -0.0012+0.j     -0.0008+0.0008j -0.0008-0.0008j]
L0H2: A_norm=4.0784, sr=8.31, symfrac=0.690, B_norm=2.540, top_s=[1.415  1.2749 1.2149 1.2061 1.159  1.0942], eig=[ 0.6322+0.8142j  0.6322-0.8142j -0.23  +0.9352j -0.23  -0.9352j  0.3332+0.8752j  0.3332-0.8752j -0.7788+0.3341j -0.7788-0.3341j]
L0H3: A_norm=0.3946, sr=1.21, symfrac=0.860, B_norm=1.766, top_s=[0.3588 0.1191 0.0776 0.0425 0.0326 0.0273], eig=[ 0.2617+0.j      0.0777+0.j      0.0101+0.0309j  0.0101-0.0309j -0.0173+0.0052j -0.0173-0.0052j -0.0175+0.j     -0.0148+0.0026j]
L0H4: A_norm=0.1063, sr=1.42, symfrac=0.709, B_norm=4.034, top_s=[0.0892 0.0536 0.0165 0.0065 0.0052 0.0041], eig=[-0.0279+0.0279j -0.0279-0.0279j -0.0078+0.j     -0.0022+0.j     -0.0011+0.0014j -0.0011-0.0014j -0.0015+0.j      0.0005-0.0011j]
L0H5: A_norm=0.0776, sr=1.04, symfrac=0.762, B_norm=9.311, top_s=[0.0759 0.0108 0.0046 0.0037 0.0034 0.0029], eig=[-0.0311+0.j      0.0007+0.0017j  0.0007-0.0017j -0.0013+0.0003j -0.0013-0.0003j -0.0005+0.0011j -0.0005-0.0011j  0.0006-0.0008j]
L1H0: A_norm=0.8874, sr=2.20, symfrac=0.710, B_norm=10.122, top_s=[0.5978 0.4823 0.2477 0.194  0.1316 0.1118], eig=[ 0.0276+0.1058j  0.0276-0.1058j -0.0784+0.j     -0.0652+0.0282j -0.0652-0.0282j -0.055 +0.007j  -0.055 -0.007j  -0.0469+0.j    ]
L1H1: A_norm=1.3157, sr=2.23, symfrac=0.714, B_norm=10.333, top_s=[0.8818 0.6663 0.4114 0.2669 0.203  0.1785], eig=[ 0.069 +0.1912j  0.069 -0.1912j -0.1533+0.j     -0.1067+0.0244j -0.1067-0.0244j -0.1076+0.0063j -0.1076-0.0063j -0.0862+0.j    ]
L1H2: A_norm=1.5512, sr=2.43, symfrac=0.713, B_norm=9.181, top_s=[0.9947 0.8604 0.3835 0.3254 0.2644 0.2271], eig=[ 0.0955+0.246j   0.0955-0.246j  -0.1678+0.j     -0.1413+0.0645j -0.1413-0.0645j -0.1349+0.0089j -0.1349-0.0089j -0.1036+0.0298j]
L1H3: A_norm=1.3759, sr=2.24, symfrac=0.713, B_norm=10.051, top_s=[0.9196 0.7386 0.4078 0.2536 0.2205 0.1718], eig=[ 0.0931+0.2031j  0.0931-0.2031j -0.1754+0.j     -0.1129+0.0083j -0.1129-0.0083j -0.1071+0.j     -0.0401+0.091j  -0.0401-0.091j ]
L1H4: A_norm=1.4507, sr=2.34, symfrac=0.711, B_norm=10.401, top_s=[0.9477 0.8684 0.3597 0.259  0.1933 0.1725], eig=[ 0.0609+0.2141j  0.0609-0.2141j -0.1348+0.0091j -0.1348-0.0091j -0.1275+0.j     -0.0988+0.j     -0.0816+0.0532j -0.0816-0.0532j]
L1H5: A_norm=1.3383, sr=2.24, symfrac=0.706, B_norm=10.620, top_s=[0.8932 0.7393 0.3722 0.2296 0.204  0.1849], eig=[ 0.0873+0.2208j  0.0873-0.2208j -0.1207+0.0123j -0.1207-0.0123j -0.1   +0.0043j -0.1   -0.0043j -0.0743+0.0552j -0.0743-0.0552j]
L2H0: A_norm=2.4382, sr=12.31, symfrac=0.695, B_norm=16.307, top_s=[0.6949 0.6297 0.6024 0.5633 0.5301 0.5065], eig=[ 0.0526+0.306j   0.0526-0.306j   0.0363+0.183j   0.0363-0.183j   0.1184+0.0804j  0.1184-0.0804j -0.0766+0.0978j -0.0766-0.0978j]
L2H1: A_norm=2.0681, sr=15.42, symfrac=0.711, B_norm=17.722, top_s=[0.5267 0.4494 0.4325 0.4171 0.4097 0.3951], eig=[ 0.0271+0.1441j  0.0271-0.1441j  0.1258+0.0113j  0.1258-0.0113j  0.1092+0.0374j  0.1092-0.0374j -0.084 +0.0709j -0.084 -0.0709j]
L2H2: A_norm=1.6133, sr=7.06, symfrac=0.706, B_norm=20.575, top_s=[0.6072 0.503  0.4013 0.3172 0.2979 0.2866], eig=[ 0.0363+0.1743j  0.0363-0.1743j -0.1058+0.j      0.0675+0.0552j  0.0675-0.0552j -0.0836-0.0147j -0.0836+0.0147j -0.0761+0.0139j]
L2H3: A_norm=2.0871, sr=13.79, symfrac=0.698, B_norm=17.506, top_s=[0.562  0.5256 0.4916 0.4403 0.4116 0.3945], eig=[ 0.0718+0.3068j  0.0718-0.3068j -0.0764+0.084j  -0.0764-0.084j  -0.1022-0.0418j -0.1022+0.0418j -0.1042+0.0055j -0.1042-0.0055j]
L2H4: A_norm=2.3444, sr=13.94, symfrac=0.698, B_norm=16.624, top_s=[0.6279 0.6043 0.5322 0.5082 0.467  0.4514], eig=[ 0.0613+0.3024j  0.0613-0.3024j  0.0769+0.1016j  0.0769-0.1016j -0.0811-0.091j  -0.0811+0.091j  -0.1183+0.j     -0.1109+0.0277j]
L2H5: A_norm=2.2511, sr=12.43, symfrac=0.697, B_norm=16.684, top_s=[0.6384 0.5397 0.4937 0.4762 0.4679 0.4519], eig=[ 0.0458+0.3023j  0.0458-0.3023j -0.1412+0.j     -0.0716+0.1085j -0.0716-0.1085j -0.1192+0.0373j -0.1192-0.0373j  0.0509+0.1089j]
L3H0: A_norm=2.2327, sr=24.45, symfrac=0.725, B_norm=22.598, top_s=[0.4515 0.4199 0.4181 0.4053 0.4008 0.3946], eig=[0.1821+0.j     0.1411+0.0785j 0.1411-0.0785j 0.1581-0.0188j 0.1581+0.0188j 0.1434+0.0562j 0.1434-0.0562j 0.1412+0.0357j]
L3H1: A_norm=1.8309, sr=31.24, symfrac=0.778, B_norm=40.869, top_s=[0.3276 0.3174 0.3092 0.3059 0.3008 0.2979], eig=[0.171 +0.0673j 0.171 -0.0673j 0.1739+0.0596j 0.1739-0.0596j 0.1486+0.0815j 0.1486-0.0815j 0.1641-0.0312j 0.1641+0.0312j]
L3H2: A_norm=1.5058, sr=10.23, symfrac=0.706, B_norm=26.882, top_s=[0.4708 0.4111 0.3556 0.3321 0.3087 0.2904], eig=[ 0.0301+0.1338j  0.0301-0.1338j  0.081 +0.0387j  0.081 -0.0387j -0.0348+0.0816j -0.0348-0.0816j  0.0823+0.j     -0.0765+0.j    ]
L3H3: A_norm=1.5992, sr=10.34, symfrac=0.701, B_norm=27.431, top_s=[0.4974 0.4025 0.3358 0.3039 0.2956 0.2949], eig=[ 0.0233+0.1587j  0.0233-0.1587j  0.099 +0.j     -0.0228+0.0945j -0.0228-0.0945j  0.0762+0.0363j  0.0762-0.0363j  0.0778+0.j    ]
L3H4: A_norm=1.8560, sr=12.32, symfrac=0.707, B_norm=24.198, top_s=[0.5288 0.4125 0.3981 0.3736 0.3603 0.3361], eig=[0.0677+0.0916j 0.0677-0.0916j 0.0027+0.1082j 0.0027-0.1082j 0.0982+0.0395j 0.0982-0.0395j 0.1025+0.j     0.0706+0.0664j]
L3H5: A_norm=0.8349, sr=10.36, symfrac=0.716, B_norm=45.401, top_s=[0.2594 0.2349 0.2122 0.1963 0.1906 0.1595], eig=[0.0754+0.035j  0.0754-0.035j  0.0779+0.j     0.021 +0.0476j 0.021 -0.0476j 0.0222+0.0351j 0.0222-0.0351j 0.0376+0.0108j]

PAIRWISE QK COSINES WITHIN EACH LAYER
L 0
[[ 1.      0.209  -0.0156 -0.0138  0.1151  0.0181]
 [ 0.209   1.     -0.0039 -0.0356  0.2091 -0.0084]
 [-0.0156 -0.0039  1.      0.2393 -0.0061 -0.0256]
 [-0.0138 -0.0356  0.2393  1.     -0.0536 -0.0579]
 [ 0.1151  0.2091 -0.0061 -0.0536  1.      0.1721]
 [ 0.0181 -0.0084 -0.0256 -0.0579  0.1721  1.    ]]
L 1
[[1.     0.578  0.4169 0.4486 0.4554 0.4652]
 [0.578  1.     0.5504 0.4889 0.5564 0.5281]
 [0.4169 0.5504 1.     0.6087 0.6676 0.5649]
 [0.4486 0.4889 0.6087 1.     0.6442 0.5634]
 [0.4554 0.5564 0.6676 0.6442 1.     0.5778]
 [0.4652 0.5281 0.5649 0.5634 0.5778 1.    ]]
L 2
[[1.     0.0783 0.1327 0.1034 0.112  0.1062]
 [0.0783 1.     0.0931 0.0855 0.0767 0.0704]
 [0.1327 0.0931 1.     0.1188 0.1265 0.1248]
 [0.1034 0.0855 0.1188 1.     0.1198 0.1131]
 [0.112  0.0767 0.1265 0.1198 1.     0.1074]
 [0.1062 0.0704 0.1248 0.1131 0.1074 1.    ]]
L 3
[[1.     0.0706 0.0444 0.036  0.0755 0.03  ]
 [0.0706 1.     0.0208 0.0236 0.0301 0.045 ]
 [0.0444 0.0208 1.     0.0946 0.1035 0.0545]
 [0.036  0.0236 0.0946 1.     0.0836 0.0652]
 [0.0755 0.0301 0.1035 0.0836 1.     0.0493]
 [0.03   0.045  0.0545 0.0652 0.0493 1.    ]]

L0H2 FULL NONZERO SINGULAR SPECTRUM
[1.415  1.2749 1.2149 1.2061 1.159  1.0942 1.0863 1.0219 0.9558 0.9159 0.8613 0.7816 0.7279 0.7214 0.5146 0.4919 0.4289 0.3761 0.3219 0.29   0.189  0.1656 0.1282 0.1157 0.1096
 0.0981 0.0751 0.0561 0.0492 0.0435 0.0371 0.0336 0.0302 0.0272 0.0242 0.0196 0.0195 0.0157 0.0145 0.013  0.0111 0.0096 0.0088 0.0086 0.0077 0.0073 0.0065 0.0064 0.0059 0.0048
 0.0043 0.0039 0.0034 0.0032 0.0031 0.0025 0.0024 0.002  0.0019 0.0018 0.0016 0.0015 0.0014 0.0011]

L0H2 TOP EIGENVALUES OF UNWEIGHTED Q.T K/8
[-0.8672+5.7867j -0.8672-5.7867j  1.92  +4.997j   1.92  -4.997j   4.415 +2.6762j  4.415 -2.6762j -4.9189+0.j     -4.1248+2.3614j -4.1248-2.3614j  2.2873+4.0016j  2.2873-4.0016j
  3.8125+0.j      3.0783+0.5985j  3.0783-0.5985j -2.3696-1.5219j -2.3696+1.5219j -1.8129+0.j     -1.2196+1.0581j -1.2196-1.0581j  1.0283+1.2431j  1.0283-1.2431j -0.764 +0.j
  0.1563+0.6708j  0.1563-0.6708j -0.2455+0.579j  -0.2455-0.579j   0.6239+0.j     -0.4258+0.j      0.1483+0.2689j  0.1483-0.2689j -0.0088-0.1973j -0.0088+0.1973j]

RESIDUAL COORDINATE CONCENTRATION
L0: top_coordinates=[162  60  35 223 246   4 180 184 300 311 239 136 178 222  72  65]; fraction_top16=0.3677; gains=[1.0341 0.9844 0.937  0.98   0.8815 0.8678 0.9294 0.8446 0.9371 0.9443 0.9385 0.8959 0.8898 0.8362 0.7062 0.8244]
L1: top_coordinates=[222  72  78 273 179 302 351 254 136 200 111 211  42 252 314 114]; fraction_top16=0.4125; gains=[1.1239 0.7995 0.7811 0.4613 0.6558 0.7671 0.6989 0.3414 0.4917 0.6776 0.6917 0.6343 0.657  0.5774 0.6457 0.4324]
L2: top_coordinates=[222  78 101  94 321 181 100 117 250 126  11 247 122 205 264 225]; fraction_top16=0.1398; gains=[1.0144 0.9057 0.7889 0.817  0.8106 0.7813 0.7841 0.8122 0.813  0.8108 0.7635 0.7678 0.7934 0.8028 0.795  0.7764]
L3: top_coordinates=[222  78 187 164  98 177 147  83 178 201  27 232 357 143 295 318]; fraction_top16=0.0724; gains=[0.8271 0.7626 0.8177 0.8323 0.8021 0.8597 0.8001 0.8151 0.8204 0.8132 0.8432 0.8405 0.7982 0.8161 0.81   0.8387]

DIRECT OV ALIGNMENT WITH LATER QK (matrix composition only)
For each later head: strongest earlier B on key side; enrichment = ||A B||F/(||A||F||B||F/sqrt(384)).
L1H0: [('L0H5', np.float64(10.954), np.float64(0.0004)), ('L0H0', np.float64(10.712), np.float64(0.0036)), ('L0H1', np.float64(10.363), np.float64(0.0016)), ('L0H4', np.float64(10.234), np.float64(0.0031)), ('L0H3', np.float64(3.044), np.float64(-0.0061))]
L1H1: [('L0H5', np.float64(10.982), np.float64(0.0017)), ('L0H1', np.float64(9.858), np.float64(0.0033)), ('L0H0', np.float64(9.79), np.float64(0.0047)), ('L0H4', np.float64(9.51), np.float64(0.0052)), ('L0H3', np.float64(2.864), np.float64(-0.005))]
L1H2: [('L0H5', np.float64(10.822), np.float64(0.002)), ('L0H1', np.float64(9.857), np.float64(0.0042)), ('L0H0', np.float64(9.775), np.float64(0.0051)), ('L0H4', np.float64(9.47), np.float64(0.0057)), ('L0H3', np.float64(3.131), np.float64(-0.0019))]
L1H3: [('L0H5', np.float64(11.072), np.float64(0.0025)), ('L0H0', np.float64(10.019), np.float64(0.0058)), ('L0H1', np.float64(9.935), np.float64(0.0047)), ('L0H4', np.float64(9.625), np.float64(0.0062)), ('L0H3', np.float64(2.943), np.float64(-0.0034))]
L1H4: [('L0H5', np.float64(11.164), np.float64(0.0041)), ('L0H0', np.float64(10.567), np.float64(0.0075)), ('L0H1', np.float64(10.381), np.float64(0.0068)), ('L0H4', np.float64(10.099), np.float64(0.0083)), ('L0H3', np.float64(3.341), np.float64(-0.0014))]
L1H5: [('L0H5', np.float64(10.958), np.float64(-0.001)), ('L0H1', np.float64(9.864), np.float64(0.0018)), ('L0H0', np.float64(9.855), np.float64(0.0039)), ('L0H4', np.float64(9.48), np.float64(0.0038)), ('L0H3', np.float64(3.016), np.float64(-0.0046))]
L2H0: [('L0H5', np.float64(4.344), np.float64(0.0037)), ('L0H1', np.float64(3.956), np.float64(0.0031)), ('L0H0', np.float64(3.888), np.float64(0.004)), ('L0H4', np.float64(3.749), np.float64(0.0049)), ('L0H3', np.float64(1.843), np.float64(-0.0099))]
L2H1: [('L0H5', np.float64(3.173), np.float64(0.0037)), ('L0H1', np.float64(2.807), np.float64(0.0023)), ('L0H0', np.float64(2.721), np.float64(0.0036)), ('L0H4', np.float64(2.689), np.float64(0.0039)), ('L0H3', np.float64(1.536), np.float64(-0.0066))]
L2H2: [('L0H5', np.float64(5.912), np.float64(0.0017)), ('L0H1', np.float64(5.461), np.float64(0.0044)), ('L0H0', np.float64(5.288), np.float64(0.0056)), ('L0H4', np.float64(5.152), np.float64(0.0069)), ('L0H3', np.float64(2.704), np.float64(-0.0064))]
L2H3: [('L0H5', np.float64(4.295), np.float64(0.005)), ('L0H1', np.float64(4.068), np.float64(0.004)), ('L0H0', np.float64(4.057), np.float64(0.0056)), ('L0H4', np.float64(3.918), np.float64(0.0059)), ('L0H3', np.float64(1.816), np.float64(-0.0066))]
L2H4: [('L0H5', np.float64(4.133), np.float64(0.0051)), ('L0H1', np.float64(4.02), np.float64(0.0055)), ('L0H0', np.float64(4.017), np.float64(0.0073)), ('L0H4', np.float64(3.866), np.float64(0.0077)), ('L0H3', np.float64(1.929), np.float64(-0.0087))]
L2H5: [('L0H5', np.float64(4.191), np.float64(0.004)), ('L0H1', np.float64(3.946), np.float64(0.0037)), ('L0H0', np.float64(3.926), np.float64(0.0056)), ('L0H4', np.float64(3.798), np.float64(0.0056)), ('L0H3', np.float64(1.812), np.float64(-0.0095))]
L3H0: [('L0H5', np.float64(1.743), np.float64(0.004)), ('L0H0', np.float64(1.607), np.float64(0.0028)), ('L0H1', np.float64(1.584), np.float64(0.003)), ('L0H4', np.float64(1.581), np.float64(0.0044)), ('L1H5', np.float64(1.074), np.float64(-0.0161))]
L3H1: [('L0H2', np.float64(1.586), np.float64(0.0598)), ('L1H3', np.float64(1.449), np.float64(0.0392)), ('L2H3', np.float64(1.335), np.float64(0.0248)), ('L2H2', np.float64(1.309), np.float64(0.0243)), ('L2H1', np.float64(1.272), np.float64(0.0638))]
L3H2: [('L0H5', np.float64(4.268), np.float64(0.0052)), ('L0H0', np.float64(3.656), np.float64(0.0044)), ('L0H1', np.float64(3.484), np.float64(0.0029)), ('L0H4', np.float64(3.405), np.float64(0.0041)), ('L0H3', np.float64(1.379), np.float64(-0.0042))]
L3H3: [('L0H5', np.float64(4.065), np.float64(0.0035)), ('L0H0', np.float64(3.504), np.float64(0.003)), ('L0H1', np.float64(3.441), np.float64(0.0014)), ('L0H4', np.float64(3.373), np.float64(0.0025)), ('L0H3', np.float64(1.357), np.float64(-0.0013))]
L3H4: [('L0H5', np.float64(3.368), np.float64(0.0084)), ('L0H0', np.float64(2.886), np.float64(0.0068)), ('L0H1', np.float64(2.849), np.float64(0.0072)), ('L0H4', np.float64(2.789), np.float64(0.0082)), ('L0H3', np.float64(1.28), np.float64(-0.001))]
L3H5: [('L0H0', np.float64(1.439), np.float64(-0.0035)), ('L0H3', np.float64(1.414), np.float64(-0.0029)), ('L0H5', np.float64(1.38), np.float64(-0.0022)), ('L0H4', np.float64(1.341), np.float64(-0.0033)), ('L0H1', np.float64(1.281), np.float64(-0.0036))]
t003 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 13,937 characters
OUTPUT of run 2 of at most 100:
```
GAIN-WEIGHTED PRODUCTS: A=diag(ln1_w) Q.T K diag(ln1_w)/8, B=O V diag(ln1_w)
These are matrix products only; no LayerNorm, attention, or MLP evaluations.
L0H0: A_norm=0.0546, sr=1.13, symfrac=0.715, B_norm=9.043, top_s=[0.0514 0.0114 0.0071 0.0046 0.0043 0.0036], eig=[ 0.0072+0.j     -0.004 +0.j     -0.0024+0.j     -0.0005+0.0014j -0.0005-0.0014j  0.0002+0.0012j  0.0002-0.0012j -0.0011+0.0004j]
L0H1: A_norm=0.0295, sr=1.40, symfrac=0.724, B_norm=6.966, top_s=[0.025  0.0095 0.0053 0.0044 0.0035 0.0031], eig=[-0.0043+0.0011j -0.0043-0.0011j  0.002 +0.j     -0.0015+0.0007j -0.0015-0.0007j -0.0012+0.j     -0.0008+0.0008j -0.0008-0.0008j]
L0H2: A_norm=4.0784, sr=8.31, symfrac=0.690, B_norm=2.540, top_s=[1.415  1.2749 1.2149 1.2061 1.159  1.0942], eig=[ 0.6322+0.8142j  0.6322-0.8142j -0.23  +0.9352j -0.23  -0.9352j  0.3332+0.8752j  0.3332-0.8752j -0.7788+0.3341j -0.7788-0.3341j]
L0H3: A_norm=0.3946, sr=1.21, symfrac=0.860, B_norm=1.766, top_s=[0.3588 0.1191 0.0776 0.0425 0.0326 0.0273], eig=[ 0.2617+0.j      0.0777+0.j      0.0101+0.0309j  0.0101-0.0309j -0.0173+0.0052j -0.0173-0.0052j -0.0175+0.j     -0.0148+0.0026j]
L0H4: A_norm=0.1063, sr=1.42, symfrac=0.709, B_norm=4.034, top_s=[0.0892 0.0536 0.0165 0.0065 0.0052 0.0041], eig=[-0.0279+0.0279j -0.0279-0.0279j -0.0078+0.j     -0.0022+0.j     -0.0011+0.0014j -0.0011-0.0014j -0.0015+0.j      0.0005-0.0011j]
L0H5: A_norm=0.0776, sr=1.04, symfrac=0.762, B_norm=9.311, top_s=[0.0759 0.0108 0.0046 0.0037 0.0034 0.0029], eig=[-0.0311+0.j      0.0007+0.0017j  0.0007-0.0017j -0.0013+0.0003j -0.0013-0.0003j -0.0005+0.0011j -0.0005-0.0011j  0.0006-0.0008j]
L1H0: A_norm=0.8874, sr=2.20, symfrac=0.710, B_norm=10.122, top_s=[0.5978 0.4823 0.2477 0.194  0.1316 0.1118], eig=[ 0.0276+0.1058j  0.0276-0.1058j -0.0784+0.j     -0.0652+0.0282j -0.0652-0.0282j -0.055 +0.007j  -0.055 -0.007j  -0.0469+0.j    ]
L1H1: A_norm=1.3157, sr=2.23, symfrac=0.714, B_norm=10.333, top_s=[0.8818 0.6663 0.4114 0.2669 0.203  0.1785], eig=[ 0.069 +0.1912j  0.069 -0.1912j -0.1533+0.j     -0.1067+0.0244j -0.1067-0.0244j -0.1076+0.0063j -0.1076-0.0063j -0.0862+0.j    ]
L1H2: A_norm=1.5512, sr=2.43, symfrac=0.713, B_norm=9.181, top_s=[0.9947 0.8604 0.3835 0.3254 0.2644 0.2271], eig=[ 0.0955+0.246j   0.0955-0.246j  -0.1678+0.j     -0.1413+0.0645j -0.1413-0.0645j -0.1349+0.0089j -0.1349-0.0089j -0.1036+0.0298j]
L1H3: A_norm=1.3759, sr=2.24, symfrac=0.713, B_norm=10.051, top_s=[0.9196 0.7386 0.4078 0.2536 0.2205 0.1718], eig=[ 0.0931+0.2031j  0.0931-0.2031j -0.1754+0.j     -0.1129+0.0083j -0.1129-0.0083j -0.1071+0.j     -0.0401+0.091j  -0.0401-0.091j ]
L1H4: A_norm=1.4507, sr=2.34, symfrac=0.711, B_norm=10.401, top_s=[0.9477 0.8684 0.3597 0.259  0.1933 0.1725], eig=[ 0.0609+0.2141j  0.0609-0.2141j -0.1348+0.0091j -0.1348-0.0091j -0.1275+0.j     -0.0988+0.j     -0.0816+0.0532j -0.0816-0.0532j]
L1H5: A_norm=1.3383, sr=2.24, symfrac=0.706, B_norm=10.620, top_s=[0.8932 0.7393 0.3722 0.2296 0.204  0.1849], eig=[ 0.0873+0.2208j  0.0873-0.2208j -0.1207+0.0123j -0.1207-0.0123j -0.1   +0.0043j -0.1   -0.0043j -0.0743+0.0552j -0.0743-0.0552j]
L2H0: A_norm=2.4382, sr=12.31, symfrac=0.695, B_norm=16.307, top_s=[0.6949 0.6297 0.6024 0.5633 0.5301 0.5065], eig=[ 0.0526+0.306j   0.0526-0.306j   0.0363+0.183j   0.0363-0.183j   0.1184+0.0804j  0.1184-0.0804j -0.0766+0.0978j -0.0766-0.0978j]
L2H1: A_norm=2.0681, sr=15.42, symfrac=0.711, B_norm=17.722, top_s=[0.5267 0.4494 0.4325 0.4171 0.4097 0.3951], eig=[ 0.0271+0.1441j  0.0271-0.1441j  0.1258+0.0113j  0.1258-0.0113j  0.1092+0.0374j  0.1092-0.0374j -0.084 +0.0709j -0.084 -0.0709j]
L2H2: A_norm=1.6133, sr=7.06, symfrac=0.706, B_norm=20.575, top_s=[0.6072 0.503  0.4013 0.3172 0.2979 0.2866], eig=[ 0.0363+0.1743j  0.0363-0.1743j -0.1058+0.j      0.0675+0.0552j  0.0675-0.0552j -0.0836-0.0147j -0.0836+0.0147j -0.0761+0.0139j]
L2H3: A_norm=2.0871, sr=13.79, symfrac=0.698, B_norm=17.506, top_s=[0.562  0.5256 0.4916 0.4403 0.4116 0.3945], eig=[ 0.0718+0.3068j  0.0718-0.3068j -0.0764+0.084j  -0.0764-0.084j  -0.1022-0.0418j -0.1022+0.0418j -0.1042+0.0055j -0.1042-0.0055j]
L2H4: A_norm=2.3444, sr=13.94, symfrac=0.698, B_norm=16.624, top_s=[0.6279 0.6043 0.5322 0.5082 0.467  0.4514], eig=[ 0.0613+0.3024j  0.0613-0.3024j  0.0769+0.1016j  0.0769-0.1016j -0.0811-0.091j  -0.0811+0.091j  -0.1183+0.j     -0.1109+0.0277j]
L2H5: A_norm=2.2511, sr=12.43, symfrac=0.697, B_norm=16.684, top_s=[0.6384 0.5397 0.4937 0.4762 0.4679 0.4519], eig=[ 0.0458+0.3023j  0.0458-0.3023j -0.1412+0.j     -0.0716+0.1085j -0.0716-0.1085j -0.1192+0.0373j -0.1192-0.0373j  0.0509+0.1089j]
L3H0: A_norm=2.2327, sr=24.45, symfrac=0.725, B_norm=22.598, top_s=[0.4515 0.4199 0.4181 0.4053 0.4008 0.3946], eig=[0.1821+0.j     0.1411+0.0785j 0.1411-0.0785j 0.1581-0.0188j 0.1581+0.0188j 0.1434+0.0562j 0.1434-0.0562j 0.1412+0.0357j]
L3H1: A_norm=1.8309, sr=31.24, symfrac=0.778, B_norm=40.869, top_s=[0.3276 0.3174 0.3092 0.3059 0.3008 0.2979], eig=[0.171 +0.0673j 0.171 -0.0673j 0.1739+0.0596j 0.1739-0.0596j 0.1486+0.0815j 0.1486-0.0815j 0.1641-0.0312j 0.1641+0.0312j]
L3H2: A_norm=1.5058, sr=10.23, symfrac=0.706, B_norm=26.882, top_s=[0.4708 0.4111 0.3556 0.3321 0.3087 0.2904], eig=[ 0.0301+0.1338j  0.0301-0.1338j  0.081 +0.0387j  0.081 -0.0387j -0.0348+0.0816j -0.0348-0.0816j  0.0823+0.j     -0.0765+0.j    ]
L3H3: A_norm=1.5992, sr=10.34, symfrac=0.701, B_norm=27.431, top_s=[0.4974 0.4025 0.3358 0.3039 0.2956 0.2949], eig=[ 0.0233+0.1587j  0.0233-0.1587j  0.099 +0.j     -0.0228+0.0945j -0.0228-0.0945j  0.0762+0.0363j  0.0762-0.0363j  0.0778+0.j    ]
L3H4: A_norm=1.8560, sr=12.32, symfrac=0.707, B_norm=24.198, top_s=[0.5288 0.4125 0.3981 0.3736 0.3603 0.3361], eig=[0.0677+0.0916j 0.0677-0.0916j 0.0027+0.1082j 0.0027-0.1082j 0.0982+0.0395j 0.0982-0.0395j 0.1025+0.j     0.0706+0.0664j]
L3H5: A_norm=0.8349, sr=10.36, symfrac=0.716, B_norm=45.401, top_s=[0.2594 0.2349 0.2122 0.1963 0.1906 0.1595], eig=[0.0754+0.035j  0.0754-0.035j  0.0779+0.j     0.021 +0.0476j 0.021 -0.0476j 0.0222+0.0351j 0.0222-0.0351j 0.0376+0.0108j]

PAIRWISE QK COSINES WITHIN EACH LAYER
L 0
[[ 1.      0.209  -0.0156 -0.0138  0.1151  0.0181]
 [ 0.209   1.     -0.0039 -0.0356  0.2091 -0.0084]
 [-0.0156 -0.0039  1.      0.2393 -0.0061 -0.0256]
 [-0.0138 -0.0356  0.2393  1.     -0.0536 -0.0579]
 [ 0.1151  0.2091 -0.0061 -0.0536  1.      0.1721]
 [ 0.0181 -0.0084 -0.0256 -0.0579  0.1721  1.    ]]
L 1
[[1.     0.578  0.4169 0.4486 0.4554 0.4652]
 [0.578  1.     0.5504 0.4889 0.5564 0.5281]
 [0.4169 0.5504 1.     0.6087 0.6676 0.5649]
 [0.4486 0.4889 0.6087 1.     0.6442 0.5634]
 [0.4554 0.5564 0.6676 0.6442 1.     0.5778]
 [0.4652 0.5281 0.5649 0.5634 0.5778 1.    ]]
L 2
[[1.     0.0783 0.1327 0.1034 0.112  0.1062]
 [0.0783 1.     0.0931 0.0855 0.0767 0.0704]
 [0.1327 0.0931 1.     0.1188 0.1265 0.1248]
 [0.1034 0.0855 0.1188 1.     0.1198 0.1131]
 [0.112  0.0767 0.1265 0.1198 1.     0.1074]
 [0.1062 0.0704 0.1248 0.1131 0.1074 1.    ]]
L 3
[[1.     0.0706 0.0444 0.036  0.0755 0.03  ]
 [0.0706 1.     0.0208 0.0236 0.0301 0.045 ]
 [0.0444 0.0208 1.     0.0946 0.1035 0.0545]
 [0.036  0.0236 0.0946 1.     0.0836 0.0652]
 [0.0755 0.0301 0.1035 0.0836 1.     0.0493]
 [0.03   0.045  0.0545 0.0652 0.0493 1.    ]]

L0H2 FULL NONZERO SINGULAR SPECTRUM
[1.415  1.2749 1.2149 1.2061 1.159  1.0942 1.0863 1.0219 0.9558 0.9159 0.8613 0.7816 0.7279 0.7214 0.5146 0.4919 0.4289 0.3761 0.3219 0.29   0.189  0.1656 0.1282 0.1157 0.1096
 0.0981 0.0751 0.0561 0.0492 0.0435 0.0371 0.0336 0.0302 0.0272 0.0242 0.0196 0.0195 0.0157 0.0145 0.013  0.0111 0.0096 0.0088 0.0086 0.0077 0.0073 0.0065 0.0064 0.0059 0.0048
 0.0043 0.0039 0.0034 0.0032 0.0031 0.0025 0.0024 0.002  0.0019 0.0018 0.0016 0.0015 0.0014 0.0011]

L0H2 TOP EIGENVALUES OF UNWEIGHTED Q.T K/8
[-0.8672+5.7867j -0.8672-5.7867j  1.92  +4.997j   1.92  -4.997j   4.415 +2.6762j  4.415 -2.6762j -4.9189+0.j     -4.1248+2.3614j -4.1248-2.3614j  2.2873+4.0016j  2.2873-4.0016j
  3.8125+0.j      3.0783+0.5985j  3.0783-0.5985j -2.3696-1.5219j -2.3696+1.5219j -1.8129+0.j     -1.2196+1.0581j -1.2196-1.0581j  1.0283+1.2431j  1.0283-1.2431j -0.764 +0.j
  0.1563+0.6708j  0.1563-0.6708j -0.2455+0.579j  -0.2455-0.579j   0.6239+0.j     -0.4258+0.j      0.1483+0.2689j  0.1483-0.2689j -0.0088-0.1973j -0.0088+0.1973j]

RESIDUAL COORDINATE CONCENTRATION
L0: top_coordinates=[162  60  35 223 246   4 180 184 300 311 239 136 178 222  72  65]; fraction_top16=0.3677; gains=[1.0341 0.9844 0.937  0.98   0.8815 0.8678 0.9294 0.8446 0.9371 0.9443 0.9385 0.8959 0.8898 0.8362 0.7062 0.8244]
L1: top_coordinates=[222  72  78 273 179 302 351 254 136 200 111 211  42 252 314 114]; fraction_top16=0.4125; gains=[1.1239 0.7995 0.7811 0.4613 0.6558 0.7671 0.6989 0.3414 0.4917 0.6776 0.6917 0.6343 0.657  0.5774 0.6457 0.4324]
L2: top_coordinates=[222  78 101  94 321 181 100 117 250 126  11 247 122 205 264 225]; fraction_top16=0.1398; gains=[1.0144 0.9057 0.7889 0.817  0.8106 0.7813 0.7841 0.8122 0.813  0.8108 0.7635 0.7678 0.7934 0.8028 0.795  0.7764]
L3: top_coordinates=[222  78 187 164  98 177 147  83 178 201  27 232 357 143 295 318]; fraction_top16=0.0724; gains=[0.8271 0.7626 0.8177 0.8323 0.8021 0.8597 0.8001 0.8151 0.8204 0.8132 0.8432 0.8405 0.7982 0.8161 0.81   0.8387]

DIRECT OV ALIGNMENT WITH LATER QK (matrix composition only)
For each later head: strongest earlier B on key side; enrichment = ||A B||F/(||A||F||B||F/sqrt(384)).
L1H0: [('L0H5', np.float64(10.954), np.float64(0.0004)), ('L0H0', np.float64(10.712), np.float64(0.0036)), ('L0H1', np.float64(10.363), np.float64(0.0016)), ('L0H4', np.float64(10.234), np.float64(0.0031)), ('L0H3', np.float64(3.044), np.float64(-0.0061))]
L1H1: [('L0H5', np.float64(10.982), np.float64(0.0017)), ('L0H1', np.float64(9.858), np.float64(0.0033)), ('L0H0', np.float64(9.79), np.float64(0.0047)), ('L0H4', np.float64(9.51), np.float64(0.0052)), ('L0H3', np.float64(2.864), np.float64(-0.005))]
L1H2: [('L0H5', np.float64(10.822), np.float64(0.002)), ('L0H1', np.float64(9.857), np.float64(0.0042)), ('L0H0', np.float64(9.775), np.float64(0.0051)), ('L0H4', np.float64(9.47), np.float64(0.0057)), ('L0H3', np.float64(3.131), np.float64(-0.0019))]
L1H3: [('L0H5', np.float64(11.072), np.float64(0.0025)), ('L0H0', np.float64(10.019), np.float64(0.0058)), ('L0H1', np.float64(9.935), np.float64(0.0047)), ('L0H4', np.float64(9.625), np.float64(0.0062)), ('L0H3', np.float64(2.943), np.float64(-0.0034))]
L1H4: [('L0H5', np.float64(11.164), np.float64(0.0041)), ('L0H0', np.float64(10.567), np.float64(0.0075)), ('L0H1', np.float64(10.381), np.float64(0.0068)), ('L0H4', np.float64(10.099), np.float64(0.0083)), ('L0H3', np.float64(3.341), np.float64(-0.0014))]
L1H5: [('L0H5', np.float64(10.958), np.float64(-0.001)), ('L0H1', np.float64(9.864), np.float64(0.0018)), ('L0H0', np.float64(9.855), np.float64(0.0039)), ('L0H4', np.float64(9.48), np.float64(0.0038)), ('L0H3', np.float64(3.016), np.float64(-0.0046))]
L2H0: [('L0H5', np.float64(4.344), np.float64(0.0037)), ('L0H1', np.float64(3.956), np.float64(0.0031)), ('L0H0', np.float64(3.888), np.float64(0.004)), ('L0H4', np.float64(3.749), np.float64(0.0049)), ('L0H3', np.float64(1.843), np.float64(-0.0099))]
L2H1: [('L0H5', np.float64(3.173), np.float64(0.0037)), ('L0H1', np.float64(2.807), np.float64(0.0023)), ('L0H0', np.float64(2.721), np.float64(0.0036)), ('L0H4', np.float64(2.689), np.float64(0.0039)), ('L0H3', np.float64(1.536), np.float64(-0.0066))]
L2H2: [('L0H5', np.float64(5.912), np.float64(0.0017)), ('L0H1', np.float64(5.461), np.float64(0.0044)), ('L0H0', np.float64(5.288), np.float64(0.0056)), ('L0H4', np.float64(5.152), np.float64(0.0069)), ('L0H3', np.float64(2.704), np.float64(-0.0064))]
L2H3: [('L0H5', np.float64(4.295), np.float64(0.005)), ('L0H1', np.float64(4.068), np.float64(0.004)), ('L0H0', np.float64(4.057), np.float64(0.0056)), ('L0H4', np.float64(3.918), np.float64(0.0059)), ('L0H3', np.float64(1.816), np.float64(-0.0066))]
L2H4: [('L0H5', np.float64(4.133), np.float64(0.0051)), ('L0H1', np.float64(4.02), np.float64(0.0055)), ('L0H0', np.float64(4.017), np.float64(0.0073)), ('L0H4', np.float64(3.866), np.float64(0.0077)), ('L0H3', np.float64(1.929), np.float64(-0.0087))]
L2H5: [('L0H5', np.float64(4.191), np.float64(0.004)), ('L0H1', np.float64(3.946), np.float64(0.0037)), ('L0H0', np.float64(3.926), np.float64(0.0056)), ('L0H4', np.float64(3.798), np.float64(0.0056)), ('L0H3', np.float64(1.812), np.float64(-0.0095))]
L3H0: [('L0H5', np.float64(1.743), np.float64(0.004)), ('L0H0', np.float64(1.607), np.float64(0.0028)), ('L0H1', np.float64(1.584), np.float64(0.003)), ('L0H4', np.float64(1.581), np.float64(0.0044)), ('L1H5', np.float64(1.074), np.float64(-0.0161))]
L3H1: [('L0H2', np.float64(1.586), np.float64(0.0598)), ('L1H3', np.float64(1.449), np.float64(0.0392)), ('L2H3', np.float64(1.335), np.float64(0.0248)), ('L2H2', np.float64(1.309), np.float64(0.0243)), ('L2H1', np.float64(1.272), np.float64(0.0638))]
L3H2: [('L0H5', np.float64(4.268), np.float64(0.0052)), ('L0H0', np.float64(3.656), np.float64(0.0044)), ('L0H1', np.float64(3.484), np.float64(0.0029)), ('L0H4', np.float64(3.405), np.float64(0.0041)), ('L0H3', np.float64(1.379), np.float64(-0.0042))]
L3H3: [('L0H5', np.float64(4.065), np.float64(0.0035)), ('L0H0', np.float64(3.504), np.float64(0.003)), ('L0H1', np.float64(3.441), np.float64(0.0014)), ('L0H4', np.float64(3.373), np.float64(0.0025)), ('L0H3', np.float64(1.357), np.float64(-0.0013))]
L3H4: [('L0H5', np.float64(3.368), np.float64(0.0084)), ('L0H0', np.float64(2.886), np.float64(0.0068)), ('L0H1', np.float64(2.849), np.float64(0.0072)), ('L0H4', np.float64(2.789), np.float64(0.0082)), ('L0H3', np.float64(1.28), np.float64(-0.001))]
L3H5: [('L0H0', np.float64(1.439), np.float64(-0.0035)), ('L0H3', np.float64(1.414), np.float64(-0.0029)), ('L0H5', np.float64(1.38), np.float64(-0.0022)), ('L0H4', np.float64(1.341), np.float64(-0.0033)), ('L0H1', np.float64(1.281), np.float64(-0.0036))]

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from sklearn.cluster import KMeans
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4, suppress=True, linewidth=180)

with threadpool_limits(limits=2):
    print("SPECTRAL DETAIL: first 64 singular values")
    for name in ("W_v.0", "W_o.0", "W_fc.0", "W_proj.0", "W_proj.3"):
        a = np.asarray(W[name], dtype=float)
        print(name, la.svdvals(a)[:64])
    print("\nGAIN-WEIGHTED MLP INPUT SPECTRA")
    for l in range(4):
        a = np.asarray(W[f"W_fc.{l}"], dtype=float) * W[f"ln2_w.{l}"]
        print(f"L{l}", la.svdvals(a)[:40])

    print("\nL0 COORDINATES WITH LARGEST ln1_w")
    ix = np.argsort(-W["ln1_w.0"])[:35]
    print("coordinate, ln1_w, ln2_w, ln1_b, ln2_b")
    for i in ix:
        print(i, *[round(float(W[f"{n}.0"][i]),5) for n in ("ln1_w","ln2_w","ln1_b","ln2_b")])

    print("\nL0 VALUE-PATH SPECTRA AND PRINCIPAL INPUT DIRECTIONS")
    vv, oo = [], []
    for j in range(6):
        sl = slice(j*64,(j+1)*64)
        a = (np.asarray(W["W_o.0"][:,sl],dtype=float)
             @ np.asarray(W["W_v.0"][sl],dtype=float)) * W["ln1_w.0"]
        u,s,vt = la.svd(a, full_matrices=False)
        vv.append(vt[:4].T)
        oo.append(u[:4])
        print(f"H{j}: singular={s[:32]}; energy_first2={sum(s[:2]**2)/sum(s*s):.4f}")
    print("L0 value-input top-2 subspace overlaps (sum squared principal cosines / 2)")
    print(np.array([[la.norm(vv[i][:,:2].T@vv[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))
    print("L0 value-output top-2 subspace overlaps")
    print(np.array([[la.norm(oo[i][:,:2].T@oo[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))

    print("\nL0 MLP INPUT ROW GEOMETRY (no activations)")
    a = np.asarray(W["W_fc.0"],dtype=float)*W["ln2_w.0"]
    a /= la.norm(a,axis=1)[:,None]
    gram = a @ a.T
    np.fill_diagonal(gram,-2)
    print("nearest-row cosine percentiles",np.percentile(gram.max(axis=1),[0,10,25,50,75,90,99,100]))
    for n in (16,20,24,28,32,48):
        km = KMeans(n_clusters=n,n_init=5,random_state=0,max_iter=200).fit(a)
        sizes=np.bincount(km.labels_,minlength=n)
        print(f"k={n}: inertia={km.inertia_:.3f}, sizes={np.sort(sizes)[::-1]}")
```
Permitted experiment harness: python — full request and result
{
  "run": 3,
  "turn": "t003",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom sklearn.cluster import KMeans\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4, suppress=True, linewidth=180)\n\nwith threadpool_limits(limits=2):\n    print(\"SPECTRAL DETAIL: first 64 singular values\")\n    for name in (\"W_v.0\", \"W_o.0\", \"W_fc.0\", \"W_proj.0\", \"W_proj.3\"):\n        a = np.asarray(W[name], dtype=float)\n        print(name, la.svdvals(a)[:64])\n    print(\"\\nGAIN-WEIGHTED MLP INPUT SPECTRA\")\n    for l in range(4):\n        a = np.asarray(W[f\"W_fc.{l}\"], dtype=float) * W[f\"ln2_w.{l}\"]\n        print(f\"L{l}\", la.svdvals(a)[:40])\n\n    print(\"\\nL0 COORDINATES WITH LARGEST ln1_w\")\n    ix = np.argsort(-W[\"ln1_w.0\"])[:35]\n    print(\"coordinate, ln1_w, ln2_w, ln1_b, ln2_b\")\n    for i in ix:\n        print(i, *[round(float(W[f\"{n}.0\"][i]),5) for n in (\"ln1_w\",\"ln2_w\",\"ln1_b\",\"ln2_b\")])\n\n    print(\"\\nL0 VALUE-PATH SPECTRA AND PRINCIPAL INPUT DIRECTIONS\")\n    vv, oo = [], []\n    for j in range(6):\n        sl = slice(j*64,(j+1)*64)\n        a = (np.asarray(W[\"W_o.0\"][:,sl],dtype=float)\n             @ np.asarray(W[\"W_v.0\"][sl],dtype=float)) * W[\"ln1_w.0\"]\n        u,s,vt = la.svd(a, full_matrices=False)\n        vv.append(vt[:4].T)\n        oo.append(u[:4])\n        print(f\"H{j}: singular={s[:32]}; energy_first2={sum(s[:2]**2)/sum(s*s):.4f}\")\n    print(\"L0 value-input top-2 subspace overlaps (sum squared principal cosines / 2)\")\n    print(np.array([[la.norm(vv[i][:,:2].T@vv[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))\n    print(\"L0 value-output top-2 subspace overlaps\")\n    print(np.array([[la.norm(oo[i][:,:2].T@oo[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))\n\n    print(\"\\nL0 MLP INPUT ROW GEOMETRY (no activations)\")\n    a = np.asarray(W[\"W_fc.0\"],dtype=float)*W[\"ln2_w.0\"]\n    a /= la.norm(a,axis=1)[:,None]\n    gram = a @ a.T\n    np.fill_diagonal(gram,-2)\n    print(\"nearest-row cosine percentiles\",np.percentile(gram.max(axis=1),[0,10,25,50,75,90,99,100]))\n    for n in (16,20,24,28,32,48):\n        km = KMeans(n_clusters=n,n_init=5,random_state=0,max_iter=200).fit(a)\n        sizes=np.bincount(km.labels_,minlength=n)\n        print(f\"k={n}: inertia={km.inertia_:.3f}, sizes={np.sort(sizes)[::-1]}\")",
  "out": "SPECTRAL DETAIL: first 64 singular values\nW_v.0 [4.339  3.5292 3.2699 3.1656 2.9824 2.6465 2.589  2.5519 2.4356 2.3813 2.2563 2.1622 2.1304 2.103  2.0423 2.0167 2.0034 1.9733 1.9278 1.8974 1.843  1.7873 1.7114 1.6651 1.6284\n 1.5643 1.5578 1.4407 1.3995 1.3772 1.3468 1.267  1.2219 1.1591 1.0859 1.0458 1.0163 0.9989 0.9452 0.918  0.9023 0.8941 0.8747 0.8698 0.8407 0.836  0.8301 0.8156 0.7991 0.7797\n 0.7728 0.761  0.7424 0.7392 0.737  0.7134 0.7046 0.698  0.6952 0.6903 0.6821 0.6752 0.6674 0.6634]\nW_o.0 [7.1033 6.6074 4.2683 2.7702 2.5025 2.285  2.2638 2.2184 2.1506 2.1393 2.1046 2.0804 2.0587 2.0258 2.0033 1.9866 1.9284 1.8896 1.8362 1.8249 1.8036 1.7312 1.72   1.694  1.6852\n 1.581  1.5427 1.5182 1.5068 1.4282 1.3997 1.3493 1.3098 1.2986 1.2483 1.223  1.2019 1.1872 1.1695 1.1631 1.1417 1.1087 1.0812 1.0677 1.0357 1.019  1.0012 0.9591 0.947  0.9289\n 0.9225 0.9041 0.8788 0.8754 0.8576 0.8426 0.8181 0.7889 0.7648 0.7392 0.727  0.7233 0.7109 0.6925]\nW_fc.0 [18.1759 11.0178  8.7366  7.4517  7.2123  7.0621  7.0112  6.6204  6.5703  6.4203  6.3431  6.2896  6.1365  6.0433  5.9738  5.9183  5.8073  5.7794  5.5832  5.5127  5.4996  5.3597\n  5.1605  5.1196  5.0858  5.0318  4.8994  4.8794  4.8192  4.7315  4.6012  4.5329  4.4792  4.4415  4.2685  4.1981  4.0305  3.9735  3.8688  3.8244  3.7202  3.6825  3.646   3.5738\n  3.5158  3.505   3.4534  3.3692  3.306   3.1758  3.1598  3.0726  2.9595  2.8722  2.6961  2.6705  2.6058  2.5693  2.4385  2.428   2.3775  2.3361  2.2806  2.1801]\nW_proj.0 [9.8257 7.3508 6.2651 5.8693 5.7965 5.6574 5.5163 5.4501 5.3333 5.2422 5.1971 5.0938 4.98   4.9037 4.8631 4.8186 4.7927 4.6889 4.6268 4.5951 4.5572 4.5234 4.4109 4.3775 4.2832\n 4.2164 4.1432 4.1247 4.0484 4.0251 3.8942 3.847  3.8082 3.7925 3.7637 3.711  3.685  3.6355 3.5949 3.5248 3.4994 3.4792 3.4694 3.4219 3.3902 3.3592 3.3361 3.319  3.2783 3.2568\n 3.229  3.1968 3.1288 3.1055 3.0957 3.0538 3.0513 3.0075 2.9961 2.9672 2.9615 2.9112 2.9026 2.8389]\nW_proj.3 [26.3094 20.6845 19.6986 19.2612 18.5937 18.1973 17.8617 17.6671 17.0436 16.7494 16.0392 15.1511 14.7559 14.3258 14.05   13.4999 13.2929 12.5312 12.4015 11.9578 11.7405 11.5943\n 10.3667  8.3505  8.0636  7.9141  7.461   7.1656  5.8331  4.0718  3.2206  3.1387  2.8415  2.8001  2.6874  2.5707  2.4029  2.3282  2.2438  2.185   2.1809  2.1161  2.0811  2.0535\n  1.9958  1.9765  1.9297  1.9175  1.8651  1.8381  1.8126  1.7833  1.7778  1.7571  1.7451  1.7414  1.6998  1.6765  1.6739  1.6612  1.6449  1.6191  1.6111  1.5929]\n\nGAIN-WEIGHTED MLP INPUT SPECTRA\nL0 [17.9816 10.3591  7.6084  7.5097  7.1175  6.8347  6.7405  6.439   6.3704  6.2235  6.1386  6.0526  6.0158  5.9372  5.7949  5.628   5.5765  5.499   5.4214  5.3116  5.3027  5.1234\n  5.09    4.9658  4.9455  4.8399  4.8169  4.7325  4.5672  4.5588  4.4839  4.3472  4.149   4.0871  4.0525  3.9141  3.8337  3.768   3.7068  3.5294]\nL1 [23.4904 14.1651 13.7412 13.3157 13.0755 12.9632 12.7202 12.4226 12.3483 12.0439 11.906  11.6244 11.5691 11.4546 11.1852 10.8585 10.577  10.5403 10.4727 10.1821 10.0581  9.8769\n  9.775   9.7284  9.5726  9.3884  9.3459  9.2056  9.0978  9.0238  8.9788  8.6435  8.5359  8.4023  8.3452  8.1109  7.995   7.8484  7.826   7.6168]\nL2 [20.2932 10.5409 10.4277 10.2744 10.0541  9.9253  9.5587  9.5157  9.4505  9.3479  9.2877  9.2143  9.0823  9.0485  8.9676  8.9403  8.8429  8.8356  8.7218  8.6873  8.6376  8.6037\n  8.4606  8.4127  8.3758  8.3161  8.3012  8.2795  8.2182  8.1847  8.0945  8.0812  8.0257  7.973   7.9377  7.8765  7.852   7.838   7.7997  7.7951]\nL3 [18.1986 10.474  10.1177  9.445   9.2037  8.7251  8.617   8.5799  8.2956  8.2691  8.2355  8.1018  8.0235  7.9105  7.9051  7.8183  7.7495  7.7239  7.6286  7.5911  7.5352  7.5253\n  7.5003  7.415   7.3742  7.332   7.2762  7.267   7.2503  7.2101  7.2008  7.1303  7.1019  7.0661  7.0494  7.0045  6.9513  6.9127  6.8984  6.8923]\n\nL0 COORDINATES WITH LARGEST ln1_w\ncoordinate, ln1_w, ln2_w, ln1_b, ln2_b\n162 1.03406 0.44269 -0.007 0.00221\n60 0.98438 0.49495 0.02711 -0.05933\n223 0.98004 0.68755 0.00699 0.05474\n311 0.94434 0.52441 0.02456 0.07531\n239 0.93846 0.96967 -0.09544 -0.08229\n300 0.93709 0.46151 0.00207 0.00974\n35 0.93697 0.68432 -0.07234 0.0298\n180 0.92941 0.69632 0.06105 0.01824\n136 0.89586 0.30164 -0.03865 0.1331\n59 0.89479 0.63694 -0.00429 0.03496\n178 0.88977 0.37873 0.00169 0.0187\n246 0.88154 0.57342 -0.02605 0.04629\n4 0.86783 0.47592 0.00022 -0.09541\n105 0.85993 0.6648 0.04374 -0.02429\n99 0.85641 0.42047 0.04597 -0.10799\n114 0.84995 0.245 -0.00659 0.10056\n228 0.84498 0.55902 0.04006 -0.08181\n184 0.84465 0.29165 0.01436 -0.06085\n152 0.84267 0.3314 -0.00598 -0.08693\n222 0.83616 0.05277 -0.03995 -0.4986\n158 0.83012 0.6504 -0.02086 -0.09451\n65 0.82441 0.27407 0.01379 -0.00238\n360 0.82004 0.4628 -0.04769 0.31383\n356 0.7999 0.69298 0.0547 0.08692\n319 0.79251 0.65175 -0.01788 -0.00531\n351 0.78555 0.81831 -0.00188 -0.06211\n301 0.78044 1.0098 0.03566 -0.03083\n273 0.78008 0.30099 0.06177 -0.18984\n54 0.77874 0.42829 -0.00541 -0.0598\n79 0.77741 0.84056 -0.01931 0.02771\n240 0.77181 0.65877 0.02462 -0.01645\n143 0.75805 0.79522 -0.00626 0.05273\n127 0.74971 0.56445 0.00121 0.09279\n320 0.7384 0.68517 -0.02942 0.03814\n39 0.72702 0.90721 0.02931 0.05081\n\nL0 VALUE-PATH SPECTRA AND PRINCIPAL INPUT DIRECTIONS\nH0: singular=[8.074  3.3277 2.097  0.6725 0.5606 0.3153 0.2578 0.1926 0.1456 0.1409 0.1159 0.1131 0.1007 0.0853 0.0812 0.0725 0.0713 0.0656 0.0613 0.0571 0.0531 0.049  0.0471 0.0457 0.0431\n 0.0396 0.039  0.0363 0.0352 0.0335 0.0317 0.0305]; energy_first2=0.9326\nH1: singular=[5.6611 3.8272 1.0646 0.5023 0.4285 0.2701 0.2169 0.1664 0.1516 0.1122 0.0945 0.0925 0.0829 0.0771 0.0715 0.0676 0.0621 0.0567 0.0553 0.0525 0.05   0.0447 0.0436 0.042  0.0384\n 0.0362 0.0357 0.0323 0.0322 0.0312 0.0302 0.0285]; energy_first2=0.9623\nH2: singular=[0.7873 0.718  0.6827 0.6713 0.6378 0.6013 0.5692 0.5583 0.5504 0.5222 0.5151 0.4872 0.4805 0.4696 0.4391 0.4211 0.4006 0.3931 0.3471 0.3044 0.2772 0.2754 0.2565 0.2286 0.2181\n 0.205  0.1814 0.1707 0.1628 0.1593 0.1314 0.1184]; energy_first2=0.1760\n\n[stderr]\nTraceback (most recent call last):\n  File \"<stdin>\", line 34, in <module>\n  File \"/env/lib/python3.12/site-packages/scipy/linalg/_decomp_svd.py\", line 207, in svd\n    _format_emit_errors_warnings(err_lst, lapack_driver)\n  File \"/env/lib/python3.12/site-packages/scipy/linalg/_decomp_svd.py\", line 26, in _format_emit_errors_warnings\n    raise LinAlgError(f\"SVD did not converge for slice = {num}.\")\nnumpy.linalg.LinAlgError: SVD did not converge for slice = 0.\n",
  "seconds": 1.313,
  "rc": 1,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "1952ea088d3d95ad6fddf236bbb9a0892bcaeea389ac860dde5055ea786a0d07",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
SPECTRAL DETAIL: first 64 singular values
W_v.0 [4.339  3.5292 3.2699 3.1656 2.9824 2.6465 2.589  2.5519 2.4356 2.3813 2.2563 2.1622 2.1304 2.103  2.0423 2.0167 2.0034 1.9733 1.9278 1.8974 1.843  1.7873 1.7114 1.6651 1.6284
 1.5643 1.5578 1.4407 1.3995 1.3772 1.3468 1.267  1.2219 1.1591 1.0859 1.0458 1.0163 0.9989 0.9452 0.918  0.9023 0.8941 0.8747 0.8698 0.8407 0.836  0.8301 0.8156 0.7991 0.7797
 0.7728 0.761  0.7424 0.7392 0.737  0.7134 0.7046 0.698  0.6952 0.6903 0.6821 0.6752 0.6674 0.6634]
W_o.0 [7.1033 6.6074 4.2683 2.7702 2.5025 2.285  2.2638 2.2184 2.1506 2.1393 2.1046 2.0804 2.0587 2.0258 2.0033 1.9866 1.9284 1.8896 1.8362 1.8249 1.8036 1.7312 1.72   1.694  1.6852
 1.581  1.5427 1.5182 1.5068 1.4282 1.3997 1.3493 1.3098 1.2986 1.2483 1.223  1.2019 1.1872 1.1695 1.1631 1.1417 1.1087 1.0812 1.0677 1.0357 1.019  1.0012 0.9591 0.947  0.9289
 0.9225 0.9041 0.8788 0.8754 0.8576 0.8426 0.8181 0.7889 0.7648 0.7392 0.727  0.7233 0.7109 0.6925]
W_fc.0 [18.1759 11.0178  8.7366  7.4517  7.2123  7.0621  7.0112  6.6204  6.5703  6.4203  6.3431  6.2896  6.1365  6.0433  5.9738  5.9183  5.8073  5.7794  5.5832  5.5127  5.4996  5.3597
  5.1605  5.1196  5.0858  5.0318  4.8994  4.8794  4.8192  4.7315  4.6012  4.5329  4.4792  4.4415  4.2685  4.1981  4.0305  3.9735  3.8688  3.8244  3.7202  3.6825  3.646   3.5738
  3.5158  3.505   3.4534  3.3692  3.306   3.1758  3.1598  3.0726  2.9595  2.8722  2.6961  2.6705  2.6058  2.5693  2.4385  2.428   2.3775  2.3361  2.2806  2.1801]
W_proj.0 [9.8257 7.3508 6.2651 5.8693 5.7965 5.6574 5.5163 5.4501 5.3333 5.2422 5.1971 5.0938 4.98   4.9037 4.8631 4.8186 4.7927 4.6889 4.6268 4.5951 4.5572 4.5234 4.4109 4.3775 4.2832
 4.2164 4.1432 4.1247 4.0484 4.0251 3.8942 3.847  3.8082 3.7925 3.7637 3.711  3.685  3.6355 3.5949 3.5248 3.4994 3.4792 3.4694 3.4219 3.3902 3.3592 3.3361 3.319  3.2783 3.2568
 3.229  3.1968 3.1288 3.1055 3.0957 3.0538 3.0513 3.0075 2.9961 2.9672 2.9615 2.9112 2.9026 2.8389]
W_proj.3 [26.3094 20.6845 19.6986 19.2612 18.5937 18.1973 17.8617 17.6671 17.0436 16.7494 16.0392 15.1511 14.7559 14.3258 14.05   13.4999 13.2929 12.5312 12.4015 11.9578 11.7405 11.5943
 10.3667  8.3505  8.0636  7.9141  7.461   7.1656  5.8331  4.0718  3.2206  3.1387  2.8415  2.8001  2.6874  2.5707  2.4029  2.3282  2.2438  2.185   2.1809  2.1161  2.0811  2.0535
  1.9958  1.9765  1.9297  1.9175  1.8651  1.8381  1.8126  1.7833  1.7778  1.7571  1.7451  1.7414  1.6998  1.6765  1.6739  1.6612  1.6449  1.6191  1.6111  1.5929]

GAIN-WEIGHTED MLP INPUT SPECTRA
L0 [17.9816 10.3591  7.6084  7.5097  7.1175  6.8347  6.7405  6.439   6.3704  6.2235  6.1386  6.0526  6.0158  5.9372  5.7949  5.628   5.5765  5.499   5.4214  5.3116  5.3027  5.1234
  5.09    4.9658  4.9455  4.8399  4.8169  4.7325  4.5672  4.5588  4.4839  4.3472  4.149   4.0871  4.0525  3.9141  3.8337  3.768   3.7068  3.5294]
L1 [23.4904 14.1651 13.7412 13.3157 13.0755 12.9632 12.7202 12.4226 12.3483 12.0439 11.906  11.6244 11.5691 11.4546 11.1852 10.8585 10.577  10.5403 10.4727 10.1821 10.0581  9.8769
  9.775   9.7284  9.5726  9.3884  9.3459  9.2056  9.0978  9.0238  8.9788  8.6435  8.5359  8.4023  8.3452  8.1109  7.995   7.8484  7.826   7.6168]
L2 [20.2932 10.5409 10.4277 10.2744 10.0541  9.9253  9.5587  9.5157  9.4505  9.3479  9.2877  9.2143  9.0823  9.0485  8.9676  8.9403  8.8429  8.8356  8.7218  8.6873  8.6376  8.6037
  8.4606  8.4127  8.3758  8.3161  8.3012  8.2795  8.2182  8.1847  8.0945  8.0812  8.0257  7.973   7.9377  7.8765  7.852   7.838   7.7997  7.7951]
L3 [18.1986 10.474  10.1177  9.445   9.2037  8.7251  8.617   8.5799  8.2956  8.2691  8.2355  8.1018  8.0235  7.9105  7.9051  7.8183  7.7495  7.7239  7.6286  7.5911  7.5352  7.5253
  7.5003  7.415   7.3742  7.332   7.2762  7.267   7.2503  7.2101  7.2008  7.1303  7.1019  7.0661  7.0494  7.0045  6.9513  6.9127  6.8984  6.8923]

L0 COORDINATES WITH LARGEST ln1_w
coordinate, ln1_w, ln2_w, ln1_b, ln2_b
162 1.03406 0.44269 -0.007 0.00221
60 0.98438 0.49495 0.02711 -0.05933
223 0.98004 0.68755 0.00699 0.05474
311 0.94434 0.52441 0.02456 0.07531
239 0.93846 0.96967 -0.09544 -0.08229
300 0.93709 0.46151 0.00207 0.00974
35 0.93697 0.68432 -0.07234 0.0298
180 0.92941 0.69632 0.06105 0.01824
136 0.89586 0.30164 -0.03865 0.1331
59 0.89479 0.63694 -0.00429 0.03496
178 0.88977 0.37873 0.00169 0.0187
246 0.88154 0.57342 -0.02605 0.04629
4 0.86783 0.47592 0.00022 -0.09541
105 0.85993 0.6648 0.04374 -0.02429
99 0.85641 0.42047 0.04597 -0.10799
114 0.84995 0.245 -0.00659 0.10056
228 0.84498 0.55902 0.04006 -0.08181
184 0.84465 0.29165 0.01436 -0.06085
152 0.84267 0.3314 -0.00598 -0.08693
222 0.83616 0.05277 -0.03995 -0.4986
158 0.83012 0.6504 -0.02086 -0.09451
65 0.82441 0.27407 0.01379 -0.00238
360 0.82004 0.4628 -0.04769 0.31383
356 0.7999 0.69298 0.0547 0.08692
319 0.79251 0.65175 -0.01788 -0.00531
351 0.78555 0.81831 -0.00188 -0.06211
301 0.78044 1.0098 0.03566 -0.03083
273 0.78008 0.30099 0.06177 -0.18984
54 0.77874 0.42829 -0.00541 -0.0598
79 0.77741 0.84056 -0.01931 0.02771
240 0.77181 0.65877 0.02462 -0.01645
143 0.75805 0.79522 -0.00626 0.05273
127 0.74971 0.56445 0.00121 0.09279
320 0.7384 0.68517 -0.02942 0.03814
39 0.72702 0.90721 0.02931 0.05081

L0 VALUE-PATH SPECTRA AND PRINCIPAL INPUT DIRECTIONS
H0: singular=[8.074  3.3277 2.097  0.6725 0.5606 0.3153 0.2578 0.1926 0.1456 0.1409 0.1159 0.1131 0.1007 0.0853 0.0812 0.0725 0.0713 0.0656 0.0613 0.0571 0.0531 0.049  0.0471 0.0457 0.0431
 0.0396 0.039  0.0363 0.0352 0.0335 0.0317 0.0305]; energy_first2=0.9326
H1: singular=[5.6611 3.8272 1.0646 0.5023 0.4285 0.2701 0.2169 0.1664 0.1516 0.1122 0.0945 0.0925 0.0829 0.0771 0.0715 0.0676 0.0621 0.0567 0.0553 0.0525 0.05   0.0447 0.0436 0.042  0.0384
 0.0362 0.0357 0.0323 0.0322 0.0312 0.0302 0.0285]; energy_first2=0.9623
H2: singular=[0.7873 0.718  0.6827 0.6713 0.6378 0.6013 0.5692 0.5583 0.5504 0.5222 0.5151 0.4872 0.4805 0.4696 0.4391 0.4211 0.4006 0.3931 0.3471 0.3044 0.2772 0.2754 0.2565 0.2286 0.2181
 0.205  0.1814 0.1707 0.1628 0.1593 0.1314 0.1184]; energy_first2=0.1760

[stderr]
Traceback (most recent call last):
  File "<stdin>", line 34, in <module>
  File "/env/lib/python3.12/site-packages/scipy/linalg/_decomp_svd.py", line 207, in svd
    _format_emit_errors_warnings(err_lst, lapack_driver)
  File "/env/lib/python3.12/site-packages/scipy/linalg/_decomp_svd.py", line 26, in _format_emit_errors_warnings
    raise LinAlgError(f"SVD did not converge for slice = {num}.")
numpy.linalg.LinAlgError: SVD did not converge for slice = 0.
t004 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 6,496 characters
OUTPUT of run 3 of at most 100:
```
SPECTRAL DETAIL: first 64 singular values
W_v.0 [4.339  3.5292 3.2699 3.1656 2.9824 2.6465 2.589  2.5519 2.4356 2.3813 2.2563 2.1622 2.1304 2.103  2.0423 2.0167 2.0034 1.9733 1.9278 1.8974 1.843  1.7873 1.7114 1.6651 1.6284
 1.5643 1.5578 1.4407 1.3995 1.3772 1.3468 1.267  1.2219 1.1591 1.0859 1.0458 1.0163 0.9989 0.9452 0.918  0.9023 0.8941 0.8747 0.8698 0.8407 0.836  0.8301 0.8156 0.7991 0.7797
 0.7728 0.761  0.7424 0.7392 0.737  0.7134 0.7046 0.698  0.6952 0.6903 0.6821 0.6752 0.6674 0.6634]
W_o.0 [7.1033 6.6074 4.2683 2.7702 2.5025 2.285  2.2638 2.2184 2.1506 2.1393 2.1046 2.0804 2.0587 2.0258 2.0033 1.9866 1.9284 1.8896 1.8362 1.8249 1.8036 1.7312 1.72   1.694  1.6852
 1.581  1.5427 1.5182 1.5068 1.4282 1.3997 1.3493 1.3098 1.2986 1.2483 1.223  1.2019 1.1872 1.1695 1.1631 1.1417 1.1087 1.0812 1.0677 1.0357 1.019  1.0012 0.9591 0.947  0.9289
 0.9225 0.9041 0.8788 0.8754 0.8576 0.8426 0.8181 0.7889 0.7648 0.7392 0.727  0.7233 0.7109 0.6925]
W_fc.0 [18.1759 11.0178  8.7366  7.4517  7.2123  7.0621  7.0112  6.6204  6.5703  6.4203  6.3431  6.2896  6.1365  6.0433  5.9738  5.9183  5.8073  5.7794  5.5832  5.5127  5.4996  5.3597
  5.1605  5.1196  5.0858  5.0318  4.8994  4.8794  4.8192  4.7315  4.6012  4.5329  4.4792  4.4415  4.2685  4.1981  4.0305  3.9735  3.8688  3.8244  3.7202  3.6825  3.646   3.5738
  3.5158  3.505   3.4534  3.3692  3.306   3.1758  3.1598  3.0726  2.9595  2.8722  2.6961  2.6705  2.6058  2.5693  2.4385  2.428   2.3775  2.3361  2.2806  2.1801]
W_proj.0 [9.8257 7.3508 6.2651 5.8693 5.7965 5.6574 5.5163 5.4501 5.3333 5.2422 5.1971 5.0938 4.98   4.9037 4.8631 4.8186 4.7927 4.6889 4.6268 4.5951 4.5572 4.5234 4.4109 4.3775 4.2832
 4.2164 4.1432 4.1247 4.0484 4.0251 3.8942 3.847  3.8082 3.7925 3.7637 3.711  3.685  3.6355 3.5949 3.5248 3.4994 3.4792 3.4694 3.4219 3.3902 3.3592 3.3361 3.319  3.2783 3.2568
 3.229  3.1968 3.1288 3.1055 3.0957 3.0538 3.0513 3.0075 2.9961 2.9672 2.9615 2.9112 2.9026 2.8389]
W_proj.3 [26.3094 20.6845 19.6986 19.2612 18.5937 18.1973 17.8617 17.6671 17.0436 16.7494 16.0392 15.1511 14.7559 14.3258 14.05   13.4999 13.2929 12.5312 12.4015 11.9578 11.7405 11.5943
 10.3667  8.3505  8.0636  7.9141  7.461   7.1656  5.8331  4.0718  3.2206  3.1387  2.8415  2.8001  2.6874  2.5707  2.4029  2.3282  2.2438  2.185   2.1809  2.1161  2.0811  2.0535
  1.9958  1.9765  1.9297  1.9175  1.8651  1.8381  1.8126  1.7833  1.7778  1.7571  1.7451  1.7414  1.6998  1.6765  1.6739  1.6612  1.6449  1.6191  1.6111  1.5929]

GAIN-WEIGHTED MLP INPUT SPECTRA
L0 [17.9816 10.3591  7.6084  7.5097  7.1175  6.8347  6.7405  6.439   6.3704  6.2235  6.1386  6.0526  6.0158  5.9372  5.7949  5.628   5.5765  5.499   5.4214  5.3116  5.3027  5.1234
  5.09    4.9658  4.9455  4.8399  4.8169  4.7325  4.5672  4.5588  4.4839  4.3472  4.149   4.0871  4.0525  3.9141  3.8337  3.768   3.7068  3.5294]
L1 [23.4904 14.1651 13.7412 13.3157 13.0755 12.9632 12.7202 12.4226 12.3483 12.0439 11.906  11.6244 11.5691 11.4546 11.1852 10.8585 10.577  10.5403 10.4727 10.1821 10.0581  9.8769
  9.775   9.7284  9.5726  9.3884  9.3459  9.2056  9.0978  9.0238  8.9788  8.6435  8.5359  8.4023  8.3452  8.1109  7.995   7.8484  7.826   7.6168]
L2 [20.2932 10.5409 10.4277 10.2744 10.0541  9.9253  9.5587  9.5157  9.4505  9.3479  9.2877  9.2143  9.0823  9.0485  8.9676  8.9403  8.8429  8.8356  8.7218  8.6873  8.6376  8.6037
  8.4606  8.4127  8.3758  8.3161  8.3012  8.2795  8.2182  8.1847  8.0945  8.0812  8.0257  7.973   7.9377  7.8765  7.852   7.838   7.7997  7.7951]
L3 [18.1986 10.474  10.1177  9.445   9.2037  8.7251  8.617   8.5799  8.2956  8.2691  8.2355  8.1018  8.0235  7.9105  7.9051  7.8183  7.7495  7.7239  7.6286  7.5911  7.5352  7.5253
  7.5003  7.415   7.3742  7.332   7.2762  7.267   7.2503  7.2101  7.2008  7.1303  7.1019  7.0661  7.0494  7.0045  6.9513  6.9127  6.8984  6.8923]

L0 COORDINATES WITH LARGEST ln1_w
coordinate, ln1_w, ln2_w, ln1_b, ln2_b
162 1.03406 0.44269 -0.007 0.00221
60 0.98438 0.49495 0.02711 -0.05933
223 0.98004 0.68755 0.00699 0.05474
311 0.94434 0.52441 0.02456 0.07531
239 0.93846 0.96967 -0.09544 -0.08229
300 0.93709 0.46151 0.00207 0.00974
35 0.93697 0.68432 -0.07234 0.0298
180 0.92941 0.69632 0.06105 0.01824
136 0.89586 0.30164 -0.03865 0.1331
59 0.89479 0.63694 -0.00429 0.03496
178 0.88977 0.37873 0.00169 0.0187
246 0.88154 0.57342 -0.02605 0.04629
4 0.86783 0.47592 0.00022 -0.09541
105 0.85993 0.6648 0.04374 -0.02429
99 0.85641 0.42047 0.04597 -0.10799
114 0.84995 0.245 -0.00659 0.10056
228 0.84498 0.55902 0.04006 -0.08181
184 0.84465 0.29165 0.01436 -0.06085
152 0.84267 0.3314 -0.00598 -0.08693
222 0.83616 0.05277 -0.03995 -0.4986
158 0.83012 0.6504 -0.02086 -0.09451
65 0.82441 0.27407 0.01379 -0.00238
360 0.82004 0.4628 -0.04769 0.31383
356 0.7999 0.69298 0.0547 0.08692
319 0.79251 0.65175 -0.01788 -0.00531
351 0.78555 0.81831 -0.00188 -0.06211
301 0.78044 1.0098 0.03566 -0.03083
273 0.78008 0.30099 0.06177 -0.18984
54 0.77874 0.42829 -0.00541 -0.0598
79 0.77741 0.84056 -0.01931 0.02771
240 0.77181 0.65877 0.02462 -0.01645
143 0.75805 0.79522 -0.00626 0.05273
127 0.74971 0.56445 0.00121 0.09279
320 0.7384 0.68517 -0.02942 0.03814
39 0.72702 0.90721 0.02931 0.05081

L0 VALUE-PATH SPECTRA AND PRINCIPAL INPUT DIRECTIONS
H0: singular=[8.074  3.3277 2.097  0.6725 0.5606 0.3153 0.2578 0.1926 0.1456 0.1409 0.1159 0.1131 0.1007 0.0853 0.0812 0.0725 0.0713 0.0656 0.0613 0.0571 0.0531 0.049  0.0471 0.0457 0.0431
 0.0396 0.039  0.0363 0.0352 0.0335 0.0317 0.0305]; energy_first2=0.9326
H1: singular=[5.6611 3.8272 1.0646 0.5023 0.4285 0.2701 0.2169 0.1664 0.1516 0.1122 0.0945 0.0925 0.0829 0.0771 0.0715 0.0676 0.0621 0.0567 0.0553 0.0525 0.05   0.0447 0.0436 0.042  0.0384
 0.0362 0.0357 0.0323 0.0322 0.0312 0.0302 0.0285]; energy_first2=0.9623
H2: singular=[0.7873 0.718  0.6827 0.6713 0.6378 0.6013 0.5692 0.5583 0.5504 0.5222 0.5151 0.4872 0.4805 0.4696 0.4391 0.4211 0.4006 0.3931 0.3471 0.3044 0.2772 0.2754 0.2565 0.2286 0.2181
 0.205  0.1814 0.1707 0.1628 0.1593 0.1314 0.1184]; energy_first2=0.1760

[stderr]
Traceback (most recent call last):
  File "<stdin>", line 34, in <module>
  File "/env/lib/python3.12/site-packages/scipy/linalg/_decomp_svd.py", line 207, in svd
    _format_emit_errors_warnings(err_lst, lapack_driver)
  File "/env/lib/python3.12/site-packages/scipy/linalg/_decomp_svd.py", line 26, in _format_emit_errors_warnings
    raise LinAlgError(f"SVD did not converge for slice = {num}.")
numpy.linalg.LinAlgError: SVD did not converge for slice = 0.

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from sklearn.cluster import KMeans
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4, suppress=True, linewidth=180)

def svd(a):
    return la.svd(a, full_matrices=False, lapack_driver="gesvd")
def normrows(a):
    return a / np.maximum(la.norm(a,axis=1,keepdims=True),1e-12)

with threadpool_limits(limits=2):
    g = np.asarray(W["ln1_w.0"], dtype=float)
    q = np.asarray(W["W_q.0"][128:192],dtype=float)*g
    k = np.asarray(W["W_k.0"][128:192],dtype=float)*g
    _,ps,pvt = svd(np.vstack([q,k]))
    pos = pvt[:32].T
    print("L0H2 stacked Q/K spectrum:",ps[:48])
    vv,oo,bb=[],[],[]
    print("\nL0 OV PRODUCTS")
    for j in range(6):
        sl=slice(j*64,(j+1)*64)
        b=(np.asarray(W["W_o.0"][:,sl],dtype=float) @ np.asarray(W["W_v.0"][sl],dtype=float))*g
        u,s,vt=svd(b)
        vv.append(vt[:4].T); oo.append(u[:4]); bb.append(b)
        print(f"H{j}: top_s={s[:8]}, top2_energy={sum(s[:2]**2)/sum(s*s):.4f}, "
              f"top2_input_overlap_QK32={la.norm(pos.T@vt[:2].T)**2/2:.4f}")
        for side,vec in (("read",vt[0]),("write",u[:,0])):
            ix=np.argsort(-abs(vec))[:8]
            print(side, list(zip(ix.tolist(),np.round(vec[ix],4).tolist())))
    print("Input top-2 subspace overlaps:")
    print(np.array([[la.norm(vv[i][:,:2].T@vv[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))
    print("Output top-2 subspace overlaps:")
    print(np.array([[la.norm(oo[i][:,:2].T@oo[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))

    print("\nL0 MLP ROW GEOMETRY")
    f=np.asarray(W["W_fc.0"],dtype=float)*W["ln2_w.0"]
    for name,a in (("F",f),("F_times_OV_H2",f@bb[2])):
        a=normrows(a)
        gram=a@a.T
        np.fill_diagonal(gram,-2)
        print(name,"nearest cosine percentiles",np.percentile(gram.max(axis=1),[0,10,25,50,75,90,99,100]))
        for n in (16,24,28,40):
            km=KMeans(n_clusters=n,n_init=3,random_state=0,max_iter=150).fit(a)
            sizes=np.bincount(km.labels_,minlength=n)
            print(f"k={n}: inertia={km.inertia_:.3f}, sizes={np.sort(sizes)[::-1]}")

    print("\nMLP READ/WRITE PRODUCTS: spectra and trace (no ReLU)")
    for l in range(4):
        f=np.asarray(W[f"W_fc.{l}"],dtype=float)*W[f"ln2_w.{l}"]
        p=np.asarray(W[f"W_proj.{l}"],dtype=float)
        a=p@f
        s=la.svdvals(a)
        print(f"L{l}: norm={la.norm(a):.3f}, trcos={np.trace(a)/(la.norm(a)*np.sqrt(384)):.4f}, top_s={s[:10]}")
```
Permitted experiment harness: python — full request and result
{
  "run": 4,
  "turn": "t004",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom sklearn.cluster import KMeans\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4, suppress=True, linewidth=180)\n\ndef svd(a):\n    return la.svd(a, full_matrices=False, lapack_driver=\"gesvd\")\ndef normrows(a):\n    return a / np.maximum(la.norm(a,axis=1,keepdims=True),1e-12)\n\nwith threadpool_limits(limits=2):\n    g = np.asarray(W[\"ln1_w.0\"], dtype=float)\n    q = np.asarray(W[\"W_q.0\"][128:192],dtype=float)*g\n    k = np.asarray(W[\"W_k.0\"][128:192],dtype=float)*g\n    _,ps,pvt = svd(np.vstack([q,k]))\n    pos = pvt[:32].T\n    print(\"L0H2 stacked Q/K spectrum:\",ps[:48])\n    vv,oo,bb=[],[],[]\n    print(\"\\nL0 OV PRODUCTS\")\n    for j in range(6):\n        sl=slice(j*64,(j+1)*64)\n        b=(np.asarray(W[\"W_o.0\"][:,sl],dtype=float) @ np.asarray(W[\"W_v.0\"][sl],dtype=float))*g\n        u,s,vt=svd(b)\n        vv.append(vt[:4].T); oo.append(u[:4]); bb.append(b)\n        print(f\"H{j}: top_s={s[:8]}, top2_energy={sum(s[:2]**2)/sum(s*s):.4f}, \"\n              f\"top2_input_overlap_QK32={la.norm(pos.T@vt[:2].T)**2/2:.4f}\")\n        for side,vec in ((\"read\",vt[0]),(\"write\",u[:,0])):\n            ix=np.argsort(-abs(vec))[:8]\n            print(side, list(zip(ix.tolist(),np.round(vec[ix],4).tolist())))\n    print(\"Input top-2 subspace overlaps:\")\n    print(np.array([[la.norm(vv[i][:,:2].T@vv[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))\n    print(\"Output top-2 subspace overlaps:\")\n    print(np.array([[la.norm(oo[i][:,:2].T@oo[j][:,:2])**2/2 for j in range(6)] for i in range(6)]))\n\n    print(\"\\nL0 MLP ROW GEOMETRY\")\n    f=np.asarray(W[\"W_fc.0\"],dtype=float)*W[\"ln2_w.0\"]\n    for name,a in ((\"F\",f),(\"F_times_OV_H2\",f@bb[2])):\n        a=normrows(a)\n        gram=a@a.T\n        np.fill_diagonal(gram,-2)\n        print(name,\"nearest cosine percentiles\",np.percentile(gram.max(axis=1),[0,10,25,50,75,90,99,100]))\n        for n in (16,24,28,40):\n            km=KMeans(n_clusters=n,n_init=3,random_state=0,max_iter=150).fit(a)\n            sizes=np.bincount(km.labels_,minlength=n)\n            print(f\"k={n}: inertia={km.inertia_:.3f}, sizes={np.sort(sizes)[::-1]}\")\n\n    print(\"\\nMLP READ/WRITE PRODUCTS: spectra and trace (no ReLU)\")\n    for l in range(4):\n        f=np.asarray(W[f\"W_fc.{l}\"],dtype=float)*W[f\"ln2_w.{l}\"]\n        p=np.asarray(W[f\"W_proj.{l}\"],dtype=float)\n        a=p@f\n        s=la.svdvals(a)\n        print(f\"L{l}: norm={la.norm(a):.3f}, trcos={np.trace(a)/(la.norm(a)*np.sqrt(384)):.4f}, top_s={s[:10]}\")",
  "out": "L0H2 stacked Q/K spectrum: [5.0883 4.7015 4.4796 4.4228 4.3022 4.145  4.0379 3.9839 3.8495 3.7937 3.6476 3.5694 3.4852 3.2257 2.9036 2.8416 2.6236 2.4409 2.3484 2.1949 1.9175 1.8722 1.6528 1.5108 1.4407\n 1.4151 1.2959 1.2137 1.1136 1.089  0.9981 0.9436 0.8861 0.8692 0.8258 0.7734 0.7053 0.6979 0.6678 0.6553 0.6213 0.5938 0.5758 0.5637 0.5248 0.5048 0.4956 0.476 ]\n\nL0 OV PRODUCTS\nH0: top_s=[8.074  3.3277 2.097  0.6725 0.5606 0.3153 0.2578 0.1926], top2_energy=0.9326, top2_input_overlap_QK32=0.8858\nread [(162, -0.3592), (223, 0.3113), (311, 0.3029), (300, -0.294), (59, -0.2334), (351, -0.2276), (152, 0.2016), (178, -0.1997)]\nwrite [(222, -0.9217), (360, 0.2573), (254, 0.143), (25, -0.0994), (197, 0.0816), (103, -0.0705), (273, -0.067), (136, 0.0491)]\nH1: top_s=[5.6611 3.8272 1.0646 0.5023 0.4285 0.2701 0.2169 0.1664], top2_energy=0.9623, top2_input_overlap_QK32=0.9510\nread [(162, -0.3438), (223, 0.3314), (300, -0.2948), (311, 0.248), (178, -0.2459), (59, -0.2304), (114, -0.1999), (152, 0.1835)]\nwrite [(222, -0.8721), (360, 0.3589), (103, -0.1802), (254, 0.1698), (25, -0.1215), (197, 0.0829), (136, 0.0666), (72, 0.0544)]\nH2: top_s=[0.7873 0.718  0.6827 0.6713 0.6378 0.6013 0.5692 0.5583], top2_energy=0.1760, top2_input_overlap_QK32=0.1426\nread [(301, -0.4618), (59, 0.2464), (240, 0.2027), (40, -0.1828), (311, 0.1796), (103, -0.1778), (278, -0.1744), (4, 0.1699)]\nwrite [(210, 0.2205), (302, 0.2109), (347, -0.1602), (217, -0.1502), (39, 0.1303), (311, -0.1297), (299, 0.1286), (119, 0.1233)]\nH3: top_s=[1.0319 0.9651 0.6173 0.5046 0.4477 0.227  0.2017 0.1835], top2_energy=0.6400, top2_input_overlap_QK32=0.4414\nread [(239, 0.3383), (360, 0.3223), (35, 0.3201), (180, -0.2328), (273, -0.1996), (320, 0.1901), (76, -0.1603), (279, 0.1587)]\nwrite [(360, 0.6568), (78, 0.4234), (103, -0.3748), (222, -0.2994), (254, 0.1448), (197, 0.1127), (307, -0.0935), (25, -0.0861)]\nH4: top_s=[3.3958 1.6424 1.0818 0.6073 0.379  0.3274 0.2314 0.2045], top2_energy=0.8745, top2_input_overlap_QK32=0.8987\nread [(162, 0.3448), (223, -0.3362), (300, 0.3015), (311, -0.271), (59, 0.2592), (178, 0.2212), (319, 0.1843), (114, 0.1833)]\nwrite [(222, 0.8891), (360, -0.3487), (103, 0.1699), (254, -0.1412), (25, 0.0857), (197, -0.0684), (273, 0.0593), (136, -0.0432)]\nH5: top_s=[8.3316 3.605  1.6787 0.7676 0.5106 0.3909 0.3445 0.3052], top2_energy=0.9507, top2_input_overlap_QK32=0.9317\nread [(223, -0.3183), (60, -0.2921), (311, -0.2893), (300, 0.2715), (162, 0.243), (246, 0.2315), (59, 0.208), (319, 0.2076)]\nwrite [(222, 0.6501), (78, 0.6419), (72, -0.3032), (360, 0.1722), (103, -0.088), (273, -0.0784), (25, -0.0756), (254, 0.0672)]\nInput top-2 subspace overlaps:\n[[1.     0.7903 0.014  0.2851 0.7882 0.7202]\n [0.7903 1.     0.0109 0.1098 0.8786 0.8835]\n [0.014  0.0109 1.     0.0671 0.0084 0.011 ]\n [0.2851 0.1098 0.0671 1.     0.1033 0.0597]\n [0.7882 0.8786 0.0084 0.1033 1.     0.8349]\n [0.7202 0.8835 0.011  0.0597 0.8349 1.    ]]\nOutput top-2 subspace overlaps:\n[[0.     0.     0.     0.     0.     0.    ]\n [0.     0.     0.     0.     0.     0.    ]\n [0.     0.     0.0001 0.0001 0.     0.    ]\n [0.     0.     0.0001 0.0002 0.     0.    ]\n [0.     0.     0.     0.     0.     0.    ]\n [0.     0.     0.     0.     0.     0.    ]]\n\nL0 MLP ROW GEOMETRY\nF nearest cosine percentiles [0.3033 0.3919 0.4239 0.4632 0.5537 0.8533 0.9778 0.9873]\nk=16: inertia=1108.373, sizes=[361 342 270 106 103  77  64  41  31  29  27  25  21  17  13   9]\nk=24: inertia=1077.770, sizes=[272 162 153 136 130 113 111  99  57  34  31  28  28  27  21  21  20  16  15  14  13  13  13   9]\nk=28: inertia=1063.817, sizes=[222 160 155 151 141 109 106  99  89  38  27  27  25  21  19  18  15  14  14  13  13  12  10   9   9   8   7   5]\nk=40: inertia=1027.594, sizes=[162 135 123 104  99  95  69  64  63  60  55  52  51  46  34  33  30  20  20  19  18  14  13  13  12  12  12  11  10  10   9   9   9   8   8   8   8   7   6   5]\nF_times_OV_H2 nearest cosine percentiles [0.4643 0.642  0.6856 0.737  0.7833 0.8506 0.9891 0.9926]\nk=16: inertia=982.551, sizes=[143 132 112 112 111 104 102  90  89  88  84  79  78  77  75  60]\nk=24: inertia=929.819, sizes=[100  90  86  78  78  78  77  72  70  64  64  63  59  59  58  57  55  55  52  51  47  47  44  32]\nk=28: inertia=906.507, sizes=[85 71 67 66 66 64 64 64 64 62 58 58 57 56 56 56 54 53 52 51 48 45 42 39 39 39 38 22]\nk=40: inertia=857.700, sizes=[55 53 53 51 50 50 50 50 48 48 47 46 46 45 43 42 42 42 41 40 40 39 37 36 36 35 34 34 34 33 29 29 29 26 25 24 23 19 18 14]\n\nMLP READ/WRITE PRODUCTS: spectra and trace (no ReLU)\nL0: norm=182.768, trcos=0.0304, top_s=[126.4401  39.8521  32.4939  30.8028  28.0393  27.0859  26.1037  25.6267  23.9582  23.8516]\nL1: norm=173.862, trcos=-0.1114, top_s=[76.2158 32.9514 27.9667 26.8703 25.256  24.4786 24.094  23.1971 23.0743 22.4406]\nL2: norm=207.921, trcos=-0.2183, top_s=[68.0228 29.9279 27.2496 26.6717 26.1322 25.8145 25.3264 25.0969 24.4946 24.238 ]\nL3: norm=232.570, trcos=-0.2033, top_s=[133.2948  55.4455  48.8196  45.1388  38.6838  38.2303  37.4412  36.2021  35.7122  35.1299]\n",
  "seconds": 2.045,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "8b115ffd5910a213105d07b9f69e5b78819de0166ad6a3632b840ad69b4ee1d0",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
L0H2 stacked Q/K spectrum: [5.0883 4.7015 4.4796 4.4228 4.3022 4.145  4.0379 3.9839 3.8495 3.7937 3.6476 3.5694 3.4852 3.2257 2.9036 2.8416 2.6236 2.4409 2.3484 2.1949 1.9175 1.8722 1.6528 1.5108 1.4407
 1.4151 1.2959 1.2137 1.1136 1.089  0.9981 0.9436 0.8861 0.8692 0.8258 0.7734 0.7053 0.6979 0.6678 0.6553 0.6213 0.5938 0.5758 0.5637 0.5248 0.5048 0.4956 0.476 ]

L0 OV PRODUCTS
H0: top_s=[8.074  3.3277 2.097  0.6725 0.5606 0.3153 0.2578 0.1926], top2_energy=0.9326, top2_input_overlap_QK32=0.8858
read [(162, -0.3592), (223, 0.3113), (311, 0.3029), (300, -0.294), (59, -0.2334), (351, -0.2276), (152, 0.2016), (178, -0.1997)]
write [(222, -0.9217), (360, 0.2573), (254, 0.143), (25, -0.0994), (197, 0.0816), (103, -0.0705), (273, -0.067), (136, 0.0491)]
H1: top_s=[5.6611 3.8272 1.0646 0.5023 0.4285 0.2701 0.2169 0.1664], top2_energy=0.9623, top2_input_overlap_QK32=0.9510
read [(162, -0.3438), (223, 0.3314), (300, -0.2948), (311, 0.248), (178, -0.2459), (59, -0.2304), (114, -0.1999), (152, 0.1835)]
write [(222, -0.8721), (360, 0.3589), (103, -0.1802), (254, 0.1698), (25, -0.1215), (197, 0.0829), (136, 0.0666), (72, 0.0544)]
H2: top_s=[0.7873 0.718  0.6827 0.6713 0.6378 0.6013 0.5692 0.5583], top2_energy=0.1760, top2_input_overlap_QK32=0.1426
read [(301, -0.4618), (59, 0.2464), (240, 0.2027), (40, -0.1828), (311, 0.1796), (103, -0.1778), (278, -0.1744), (4, 0.1699)]
write [(210, 0.2205), (302, 0.2109), (347, -0.1602), (217, -0.1502), (39, 0.1303), (311, -0.1297), (299, 0.1286), (119, 0.1233)]
H3: top_s=[1.0319 0.9651 0.6173 0.5046 0.4477 0.227  0.2017 0.1835], top2_energy=0.6400, top2_input_overlap_QK32=0.4414
read [(239, 0.3383), (360, 0.3223), (35, 0.3201), (180, -0.2328), (273, -0.1996), (320, 0.1901), (76, -0.1603), (279, 0.1587)]
write [(360, 0.6568), (78, 0.4234), (103, -0.3748), (222, -0.2994), (254, 0.1448), (197, 0.1127), (307, -0.0935), (25, -0.0861)]
H4: top_s=[3.3958 1.6424 1.0818 0.6073 0.379  0.3274 0.2314 0.2045], top2_energy=0.8745, top2_input_overlap_QK32=0.8987
read [(162, 0.3448), (223, -0.3362), (300, 0.3015), (311, -0.271), (59, 0.2592), (178, 0.2212), (319, 0.1843), (114, 0.1833)]
write [(222, 0.8891), (360, -0.3487), (103, 0.1699), (254, -0.1412), (25, 0.0857), (197, -0.0684), (273, 0.0593), (136, -0.0432)]
H5: top_s=[8.3316 3.605  1.6787 0.7676 0.5106 0.3909 0.3445 0.3052], top2_energy=0.9507, top2_input_overlap_QK32=0.9317
read [(223, -0.3183), (60, -0.2921), (311, -0.2893), (300, 0.2715), (162, 0.243), (246, 0.2315), (59, 0.208), (319, 0.2076)]
write [(222, 0.6501), (78, 0.6419), (72, -0.3032), (360, 0.1722), (103, -0.088), (273, -0.0784), (25, -0.0756), (254, 0.0672)]
Input top-2 subspace overlaps:
[[1.     0.7903 0.014  0.2851 0.7882 0.7202]
 [0.7903 1.     0.0109 0.1098 0.8786 0.8835]
 [0.014  0.0109 1.     0.0671 0.0084 0.011 ]
 [0.2851 0.1098 0.0671 1.     0.1033 0.0597]
 [0.7882 0.8786 0.0084 0.1033 1.     0.8349]
 [0.7202 0.8835 0.011  0.0597 0.8349 1.    ]]
Output top-2 subspace overlaps:
[[0.     0.     0.     0.     0.     0.    ]
 [0.     0.     0.     0.     0.     0.    ]
 [0.     0.     0.0001 0.0001 0.     0.    ]
 [0.     0.     0.0001 0.0002 0.     0.    ]
 [0.     0.     0.     0.     0.     0.    ]
 [0.     0.     0.     0.     0.     0.    ]]

L0 MLP ROW GEOMETRY
F nearest cosine percentiles [0.3033 0.3919 0.4239 0.4632 0.5537 0.8533 0.9778 0.9873]
k=16: inertia=1108.373, sizes=[361 342 270 106 103  77  64  41  31  29  27  25  21  17  13   9]
k=24: inertia=1077.770, sizes=[272 162 153 136 130 113 111  99  57  34  31  28  28  27  21  21  20  16  15  14  13  13  13   9]
k=28: inertia=1063.817, sizes=[222 160 155 151 141 109 106  99  89  38  27  27  25  21  19  18  15  14  14  13  13  12  10   9   9   8   7   5]
k=40: inertia=1027.594, sizes=[162 135 123 104  99  95  69  64  63  60  55  52  51  46  34  33  30  20  20  19  18  14  13  13  12  12  12  11  10  10   9   9   9   8   8   8   8   7   6   5]
F_times_OV_H2 nearest cosine percentiles [0.4643 0.642  0.6856 0.737  0.7833 0.8506 0.9891 0.9926]
k=16: inertia=982.551, sizes=[143 132 112 112 111 104 102  90  89  88  84  79  78  77  75  60]
k=24: inertia=929.819, sizes=[100  90  86  78  78  78  77  72  70  64  64  63  59  59  58  57  55  55  52  51  47  47  44  32]
k=28: inertia=906.507, sizes=[85 71 67 66 66 64 64 64 64 62 58 58 57 56 56 56 54 53 52 51 48 45 42 39 39 39 38 22]
k=40: inertia=857.700, sizes=[55 53 53 51 50 50 50 50 48 48 47 46 46 45 43 42 42 42 41 40 40 39 37 36 36 35 34 34 34 33 29 29 29 26 25 24 23 19 18 14]

MLP READ/WRITE PRODUCTS: spectra and trace (no ReLU)
L0: norm=182.768, trcos=0.0304, top_s=[126.4401  39.8521  32.4939  30.8028  28.0393  27.0859  26.1037  25.6267  23.9582  23.8516]
L1: norm=173.862, trcos=-0.1114, top_s=[76.2158 32.9514 27.9667 26.8703 25.256  24.4786 24.094  23.1971 23.0743 22.4406]
L2: norm=207.921, trcos=-0.2183, top_s=[68.0228 29.9279 27.2496 26.6717 26.1322 25.8145 25.3264 25.0969 24.4946 24.238 ]
L3: norm=232.570, trcos=-0.2033, top_s=[133.2948  55.4455  48.8196  45.1388  38.6838  38.2303  37.4412  36.2021  35.7122  35.1299]
t005 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 5,092 characters
OUTPUT of run 4 of at most 100:
```
L0H2 stacked Q/K spectrum: [5.0883 4.7015 4.4796 4.4228 4.3022 4.145  4.0379 3.9839 3.8495 3.7937 3.6476 3.5694 3.4852 3.2257 2.9036 2.8416 2.6236 2.4409 2.3484 2.1949 1.9175 1.8722 1.6528 1.5108 1.4407
 1.4151 1.2959 1.2137 1.1136 1.089  0.9981 0.9436 0.8861 0.8692 0.8258 0.7734 0.7053 0.6979 0.6678 0.6553 0.6213 0.5938 0.5758 0.5637 0.5248 0.5048 0.4956 0.476 ]

L0 OV PRODUCTS
H0: top_s=[8.074  3.3277 2.097  0.6725 0.5606 0.3153 0.2578 0.1926], top2_energy=0.9326, top2_input_overlap_QK32=0.8858
read [(162, -0.3592), (223, 0.3113), (311, 0.3029), (300, -0.294), (59, -0.2334), (351, -0.2276), (152, 0.2016), (178, -0.1997)]
write [(222, -0.9217), (360, 0.2573), (254, 0.143), (25, -0.0994), (197, 0.0816), (103, -0.0705), (273, -0.067), (136, 0.0491)]
H1: top_s=[5.6611 3.8272 1.0646 0.5023 0.4285 0.2701 0.2169 0.1664], top2_energy=0.9623, top2_input_overlap_QK32=0.9510
read [(162, -0.3438), (223, 0.3314), (300, -0.2948), (311, 0.248), (178, -0.2459), (59, -0.2304), (114, -0.1999), (152, 0.1835)]
write [(222, -0.8721), (360, 0.3589), (103, -0.1802), (254, 0.1698), (25, -0.1215), (197, 0.0829), (136, 0.0666), (72, 0.0544)]
H2: top_s=[0.7873 0.718  0.6827 0.6713 0.6378 0.6013 0.5692 0.5583], top2_energy=0.1760, top2_input_overlap_QK32=0.1426
read [(301, -0.4618), (59, 0.2464), (240, 0.2027), (40, -0.1828), (311, 0.1796), (103, -0.1778), (278, -0.1744), (4, 0.1699)]
write [(210, 0.2205), (302, 0.2109), (347, -0.1602), (217, -0.1502), (39, 0.1303), (311, -0.1297), (299, 0.1286), (119, 0.1233)]
H3: top_s=[1.0319 0.9651 0.6173 0.5046 0.4477 0.227  0.2017 0.1835], top2_energy=0.6400, top2_input_overlap_QK32=0.4414
read [(239, 0.3383), (360, 0.3223), (35, 0.3201), (180, -0.2328), (273, -0.1996), (320, 0.1901), (76, -0.1603), (279, 0.1587)]
write [(360, 0.6568), (78, 0.4234), (103, -0.3748), (222, -0.2994), (254, 0.1448), (197, 0.1127), (307, -0.0935), (25, -0.0861)]
H4: top_s=[3.3958 1.6424 1.0818 0.6073 0.379  0.3274 0.2314 0.2045], top2_energy=0.8745, top2_input_overlap_QK32=0.8987
read [(162, 0.3448), (223, -0.3362), (300, 0.3015), (311, -0.271), (59, 0.2592), (178, 0.2212), (319, 0.1843), (114, 0.1833)]
write [(222, 0.8891), (360, -0.3487), (103, 0.1699), (254, -0.1412), (25, 0.0857), (197, -0.0684), (273, 0.0593), (136, -0.0432)]
H5: top_s=[8.3316 3.605  1.6787 0.7676 0.5106 0.3909 0.3445 0.3052], top2_energy=0.9507, top2_input_overlap_QK32=0.9317
read [(223, -0.3183), (60, -0.2921), (311, -0.2893), (300, 0.2715), (162, 0.243), (246, 0.2315), (59, 0.208), (319, 0.2076)]
write [(222, 0.6501), (78, 0.6419), (72, -0.3032), (360, 0.1722), (103, -0.088), (273, -0.0784), (25, -0.0756), (254, 0.0672)]
Input top-2 subspace overlaps:
[[1.     0.7903 0.014  0.2851 0.7882 0.7202]
 [0.7903 1.     0.0109 0.1098 0.8786 0.8835]
 [0.014  0.0109 1.     0.0671 0.0084 0.011 ]
 [0.2851 0.1098 0.0671 1.     0.1033 0.0597]
 [0.7882 0.8786 0.0084 0.1033 1.     0.8349]
 [0.7202 0.8835 0.011  0.0597 0.8349 1.    ]]
Output top-2 subspace overlaps:
[[0.     0.     0.     0.     0.     0.    ]
 [0.     0.     0.     0.     0.     0.    ]
 [0.     0.     0.0001 0.0001 0.     0.    ]
 [0.     0.     0.0001 0.0002 0.     0.    ]
 [0.     0.     0.     0.     0.     0.    ]
 [0.     0.     0.     0.     0.     0.    ]]

L0 MLP ROW GEOMETRY
F nearest cosine percentiles [0.3033 0.3919 0.4239 0.4632 0.5537 0.8533 0.9778 0.9873]
k=16: inertia=1108.373, sizes=[361 342 270 106 103  77  64  41  31  29  27  25  21  17  13   9]
k=24: inertia=1077.770, sizes=[272 162 153 136 130 113 111  99  57  34  31  28  28  27  21  21  20  16  15  14  13  13  13   9]
k=28: inertia=1063.817, sizes=[222 160 155 151 141 109 106  99  89  38  27  27  25  21  19  18  15  14  14  13  13  12  10   9   9   8   7   5]
k=40: inertia=1027.594, sizes=[162 135 123 104  99  95  69  64  63  60  55  52  51  46  34  33  30  20  20  19  18  14  13  13  12  12  12  11  10  10   9   9   9   8   8   8   8   7   6   5]
F_times_OV_H2 nearest cosine percentiles [0.4643 0.642  0.6856 0.737  0.7833 0.8506 0.9891 0.9926]
k=16: inertia=982.551, sizes=[143 132 112 112 111 104 102  90  89  88  84  79  78  77  75  60]
k=24: inertia=929.819, sizes=[100  90  86  78  78  78  77  72  70  64  64  63  59  59  58  57  55  55  52  51  47  47  44  32]
k=28: inertia=906.507, sizes=[85 71 67 66 66 64 64 64 64 62 58 58 57 56 56 56 54 53 52 51 48 45 42 39 39 39 38 22]
k=40: inertia=857.700, sizes=[55 53 53 51 50 50 50 50 48 48 47 46 46 45 43 42 42 42 41 40 40 39 37 36 36 35 34 34 34 33 29 29 29 26 25 24 23 19 18 14]

MLP READ/WRITE PRODUCTS: spectra and trace (no ReLU)
L0: norm=182.768, trcos=0.0304, top_s=[126.4401  39.8521  32.4939  30.8028  28.0393  27.0859  26.1037  25.6267  23.9582  23.8516]
L1: norm=173.862, trcos=-0.1114, top_s=[76.2158 32.9514 27.9667 26.8703 25.256  24.4786 24.094  23.1971 23.0743 22.4406]
L2: norm=207.921, trcos=-0.2183, top_s=[68.0228 29.9279 27.2496 26.6717 26.1322 25.8145 25.3264 25.0969 24.4946 24.238 ]
L3: norm=232.570, trcos=-0.2033, top_s=[133.2948  55.4455  48.8196  45.1388  38.6838  38.2303  37.4412  36.2021  35.7122  35.1299]

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4, suppress=True, linewidth=180)

def svd(a):
    return la.svd(a, full_matrices=False, lapack_driver="gesvd")
def whiten(g):
    e,u=la.eigh(g)
    return (u/np.sqrt(np.maximum(e, e[-1]*1e-6)))@u.T

with threadpool_limits(limits=2):
    g=np.asarray(W["ln1_w.0"],dtype=float)
    bs=[]
    for j in range(6):
        s=slice(j*64,(j+1)*64)
        bs.append((np.asarray(W["W_o.0"][:,s],dtype=float)@np.asarray(W["W_v.0"][s],dtype=float))*g)
    u,s,vt=svd(np.hstack([bs[j] for j in (0,1,4,5)]))
    R=u[:,:3]
    print("COMMON L0 BROAD-HEAD WRITE SUBSPACE: singular values",s[:12])
    print("Fraction of each head's OV energy in that 3D write subspace:")
    print([round(la.norm(R.T@b)**2/la.norm(b)**2,4) for b in bs])
    print("Top coordinates of its three orthonormal directions:")
    for z in range(3):
        ix=np.argsort(-abs(R[:,z]))[:8]
        print(z, list(zip(ix.tolist(),np.round(R[ix,z],4).tolist())))

    print("\nLATER QK FRACTION USING COMMON 3D WRITE SUBSPACE")
    print("query-side, key-side, both-sides; bias-derived key coefficient fraction in this subspace")
    for l in range(1,4):
        g1=np.asarray(W[f"ln1_w.{l}"],dtype=float)
        b1=np.asarray(W[f"ln1_b.{l}"],dtype=float)
        for j in range(6):
            sl=slice(64*j,64*j+64)
            q=np.asarray(W[f"W_q.{l}"][sl],dtype=float)
            k=np.asarray(W[f"W_k.{l}"][sl],dtype=float)
            raw=q.T@k/8
            a=(g1[:,None]*raw)*g1
            keycoef=(b1@raw)*g1
            vals=[la.norm(R.T@a)**2/la.norm(a)**2,
                  la.norm(a@R)**2/la.norm(a)**2,
                  la.norm(R.T@a@R)**2/la.norm(a)**2,
                  la.norm(keycoef@R)**2/(la.norm(keycoef)**2+1e-30)]
            print(f"L{l}H{j}",np.round(vals,4))
            if l==1:
                print("3D coefficient matrix",np.round(R.T@a@R,4).tolist(),
                      "key-linear coefficients",np.round(keycoef@R,4).tolist())

    print("\nL0H2 RAW OV SINGULAR VALUES")
    braw=np.asarray(W["W_o.0"][:,128:192],dtype=float)@np.asarray(W["W_v.0"][128:192],dtype=float)
    print(svd(braw)[1][:64])

    print("\nMLP ROW CROSS-COVARIANCES IN L0H2 READ SUBSPACES")
    print("X = M T; Y = M B T. This compares weight-row geometry, without evaluating any neuron.")
    M=np.asarray(W["W_fc.0"],dtype=float)*W["ln2_w.0"]
    for gain in (False,True):
        B=bs[2] if gain else braw
        _,_,vt=svd(B)
        for rank in (16,24,28,40):
            T=vt[:rank].T
            X=M@T
            Y=M@B@T
            X=X-X.mean(axis=0)
            Y=Y-Y.mean(axis=0)
            X/=la.norm(X)
            Y/=la.norm(Y)
            G=(X.T@X+Y.T@Y)/2
            iw=whiten(G)
            C=iw@((X.T@Y+Y.T@X)/2)@iw
            e=la.eigvalsh(C)
            print(f"gain={gain}, rank={rank}: Frobenius_cos={np.sum(X*Y):.4f}, "
                  f"sym_generalized_eigs={np.round(e,3)}")
```
Permitted experiment harness: python — full request and result
{
  "run": 5,
  "turn": "t005",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4, suppress=True, linewidth=180)\n\ndef svd(a):\n    return la.svd(a, full_matrices=False, lapack_driver=\"gesvd\")\ndef whiten(g):\n    e,u=la.eigh(g)\n    return (u/np.sqrt(np.maximum(e, e[-1]*1e-6)))@u.T\n\nwith threadpool_limits(limits=2):\n    g=np.asarray(W[\"ln1_w.0\"],dtype=float)\n    bs=[]\n    for j in range(6):\n        s=slice(j*64,(j+1)*64)\n        bs.append((np.asarray(W[\"W_o.0\"][:,s],dtype=float)@np.asarray(W[\"W_v.0\"][s],dtype=float))*g)\n    u,s,vt=svd(np.hstack([bs[j] for j in (0,1,4,5)]))\n    R=u[:,:3]\n    print(\"COMMON L0 BROAD-HEAD WRITE SUBSPACE: singular values\",s[:12])\n    print(\"Fraction of each head's OV energy in that 3D write subspace:\")\n    print([round(la.norm(R.T@b)**2/la.norm(b)**2,4) for b in bs])\n    print(\"Top coordinates of its three orthonormal directions:\")\n    for z in range(3):\n        ix=np.argsort(-abs(R[:,z]))[:8]\n        print(z, list(zip(ix.tolist(),np.round(R[ix,z],4).tolist())))\n\n    print(\"\\nLATER QK FRACTION USING COMMON 3D WRITE SUBSPACE\")\n    print(\"query-side, key-side, both-sides; bias-derived key coefficient fraction in this subspace\")\n    for l in range(1,4):\n        g1=np.asarray(W[f\"ln1_w.{l}\"],dtype=float)\n        b1=np.asarray(W[f\"ln1_b.{l}\"],dtype=float)\n        for j in range(6):\n            sl=slice(64*j,64*j+64)\n            q=np.asarray(W[f\"W_q.{l}\"][sl],dtype=float)\n            k=np.asarray(W[f\"W_k.{l}\"][sl],dtype=float)\n            raw=q.T@k/8\n            a=(g1[:,None]*raw)*g1\n            keycoef=(b1@raw)*g1\n            vals=[la.norm(R.T@a)**2/la.norm(a)**2,\n                  la.norm(a@R)**2/la.norm(a)**2,\n                  la.norm(R.T@a@R)**2/la.norm(a)**2,\n                  la.norm(keycoef@R)**2/(la.norm(keycoef)**2+1e-30)]\n            print(f\"L{l}H{j}\",np.round(vals,4))\n            if l==1:\n                print(\"3D coefficient matrix\",np.round(R.T@a@R,4).tolist(),\n                      \"key-linear coefficients\",np.round(keycoef@R,4).tolist())\n\n    print(\"\\nL0H2 RAW OV SINGULAR VALUES\")\n    braw=np.asarray(W[\"W_o.0\"][:,128:192],dtype=float)@np.asarray(W[\"W_v.0\"][128:192],dtype=float)\n    print(svd(braw)[1][:64])\n\n    print(\"\\nMLP ROW CROSS-COVARIANCES IN L0H2 READ SUBSPACES\")\n    print(\"X = M T; Y = M B T. This compares weight-row geometry, without evaluating any neuron.\")\n    M=np.asarray(W[\"W_fc.0\"],dtype=float)*W[\"ln2_w.0\"]\n    for gain in (False,True):\n        B=bs[2] if gain else braw\n        _,_,vt=svd(B)\n        for rank in (16,24,28,40):\n            T=vt[:rank].T\n            X=M@T\n            Y=M@B@T\n            X=X-X.mean(axis=0)\n            Y=Y-Y.mean(axis=0)\n            X/=la.norm(X)\n            Y/=la.norm(Y)\n            G=(X.T@X+Y.T@Y)/2\n            iw=whiten(G)\n            C=iw@((X.T@Y+Y.T@X)/2)@iw\n            e=la.eigvalsh(C)\n            print(f\"gain={gain}, rank={rank}: Frobenius_cos={np.sum(X*Y):.4f}, \"\n                  f\"sym_generalized_eigs={np.round(e,3)}\")",
  "out": "COMMON L0 BROAD-HEAD WRITE SUBSPACE: singular values [11.996   8.4336  3.3624  1.312   0.9992  0.8615  0.7301  0.6058  0.5661  0.5402  0.4827  0.436 ]\nFraction of each head's OV energy in that 3D write subspace:\n[np.float64(0.976), np.float64(0.9754), np.float64(0.0071), np.float64(0.3208), np.float64(0.923), np.float64(0.971)]\nTop coordinates of its three orthonormal directions:\n0 [(222, 0.9268), (78, 0.2902), (360, -0.1298), (72, -0.126), (254, -0.0778), (25, 0.0414), (197, -0.0354), (103, 0.0353)]\n1 [(78, 0.7045), (360, 0.4506), (72, -0.3277), (103, -0.2017), (254, 0.2004), (25, -0.1744), (222, -0.1615), (273, -0.1318)]\n2 [(72, -0.4814), (103, 0.4745), (360, -0.4638), (78, 0.1951), (222, -0.1875), (273, -0.1063), (130, 0.1011), (307, 0.1)]\n\nLATER QK FRACTION USING COMMON 3D WRITE SUBSPACE\nquery-side, key-side, both-sides; bias-derived key coefficient fraction in this subspace\nL1H0 [0.0678 0.6412 0.0555 0.8698]\n3D coefficient matrix [[0.0519, -0.1085, -0.0667], [0.1361, 0.0631, 0.0309], [0.0217, 0.0219, 0.0196]] key-linear coefficients [0.8921, 0.7926, 0.4225]\nL1H1 [0.0756 0.6003 0.0625 0.9031]\n3D coefficient matrix [[0.0621, -0.1722, -0.103], [0.221, 0.0837, 0.0433], [0.0624, 0.0402, 0.0302]] key-linear coefficients [1.637, 1.254, 0.6797]\nL1H2 [0.069  0.634  0.0601 0.9041]\n3D coefficient matrix [[0.0548, -0.1987, -0.1216], [0.2606, 0.0802, 0.0387], [0.0943, 0.0414, 0.0292]] key-linear coefficients [1.8573, 1.4128, 0.7806]\nL1H3 [0.0676 0.6351 0.0565 0.8899]\n3D coefficient matrix [[0.0568, -0.1671, -0.1069], [0.2241, 0.0806, 0.0392], [0.0652, 0.0315, 0.0309]] key-linear coefficients [1.6221, 1.3243, 0.731]\nL1H4 [0.0646 0.6915 0.0547 0.9054]\n3D coefficient matrix [[0.0621, -0.1784, -0.1092], [0.234, 0.0729, 0.0346], [0.061, 0.0404, 0.0309]] key-linear coefficients [1.7177, 1.1775, 0.6418]\nL1H5 [0.0784 0.6307 0.0642 0.8894]\n3D coefficient matrix [[0.0676, -0.1796, -0.1128], [0.2279, 0.0752, 0.0385], [0.0585, 0.0454, 0.0312]] key-linear coefficients [1.2025, 0.9804, 0.5698]\nL2H0 [0.0309 0.097  0.0234 0.8566]\nL2H1 [0.0106 0.0469 0.0069 0.7939]\nL2H2 [0.0254 0.1854 0.0183 0.8053]\nL2H3 [0.0418 0.0967 0.0332 0.8732]\nL2H4 [0.0338 0.098  0.0263 0.8644]\nL2H5 [0.0345 0.0928 0.0266 0.8574]\nL3H0 [0.0026 0.0136 0.0007 0.1503]\nL3H1 [0.0043 0.0033 0.0004 0.0366]\nL3H2 [0.0147 0.071  0.0079 0.5941]\nL3H3 [0.0184 0.0671 0.0115 0.6708]\nL3H4 [0.007  0.0452 0.0035 0.52  ]\nL3H5 [0.0093 0.0106 0.0005 0.0378]\n\nL0H2 RAW OV SINGULAR VALUES\n[4.0009 3.853  3.7432 3.5437 3.4491 3.313  3.2443 3.1768 3.0968 2.9694 2.8864 2.7299 2.6846 2.4768 2.4314 2.0744 1.9734 1.7097 1.3762 1.1317 1.0096 0.97   0.8126 0.7541 0.6761\n 0.6298 0.564  0.5469 0.4967 0.4718 0.4228 0.3839 0.3639 0.3453 0.3144 0.2761 0.2338 0.2223 0.1995 0.1695 0.1627 0.1369 0.1276 0.1113 0.1091 0.0945 0.0924 0.0748 0.0696 0.0642\n 0.0588 0.0557 0.0526 0.0455 0.0437 0.04   0.0376 0.035  0.0327 0.0314 0.0283 0.0276 0.0243 0.0212]\n\nMLP ROW CROSS-COVARIANCES IN L0H2 READ SUBSPACES\nX = M T; Y = M B T. This compares weight-row geometry, without evaluating any neuron.\ngain=False, rank=16: Frobenius_cos=0.1478, sym_generalized_eigs=[-0.344 -0.223 -0.058 -0.017  0.03   0.093  0.131  0.159  0.178  0.208  0.251  0.296  0.317  0.347  0.377  0.413]\ngain=False, rank=24: Frobenius_cos=0.1313, sym_generalized_eigs=[-0.42  -0.337 -0.312 -0.23  -0.192 -0.122 -0.07  -0.043  0.042  0.055  0.095  0.096  0.145  0.172  0.192  0.222  0.234  0.271  0.336  0.363  0.374  0.45   0.461  0.548]\ngain=False, rank=28: Frobenius_cos=0.1279, sym_generalized_eigs=[-0.591 -0.529 -0.402 -0.374 -0.325 -0.242 -0.2   -0.168 -0.097 -0.079 -0.018  0.057  0.072  0.093  0.104  0.153  0.182  0.211  0.22   0.255  0.269  0.336  0.385  0.407  0.427\n  0.517  0.58   0.649]\ngain=False, rank=40: Frobenius_cos=0.1228, sym_generalized_eigs=[-0.79  -0.74  -0.679 -0.634 -0.607 -0.59  -0.512 -0.408 -0.4   -0.305 -0.287 -0.272 -0.229 -0.211 -0.179 -0.12  -0.092 -0.057 -0.018  0.021  0.027  0.076  0.089  0.142  0.204\n  0.218  0.267  0.294  0.319  0.373  0.401  0.441  0.47   0.481  0.581  0.595  0.649  0.705  0.724  0.828]\ngain=True, rank=16: Frobenius_cos=0.1322, sym_generalized_eigs=[-0.384 -0.246 -0.126 -0.07  -0.014  0.024  0.133  0.166  0.18   0.21   0.218  0.282  0.307  0.349  0.402  0.476]\ngain=True, rank=24: Frobenius_cos=0.1347, sym_generalized_eigs=[-0.456 -0.408 -0.324 -0.307 -0.214 -0.182 -0.108 -0.075 -0.031 -0.01   0.043  0.075  0.168  0.219  0.237  0.257  0.282  0.311  0.36   0.407  0.451  0.459  0.53   0.673]\ngain=True, rank=28: Frobenius_cos=0.1291, sym_generalized_eigs=[-0.698 -0.621 -0.433 -0.333 -0.328 -0.312 -0.25  -0.217 -0.158 -0.127 -0.083 -0.038 -0.011  0.038  0.112  0.183  0.212  0.249  0.268  0.297  0.333  0.363  0.396  0.448  0.502\n  0.528  0.625  0.71 ]\ngain=True, rank=40: Frobenius_cos=0.1242, sym_generalized_eigs=[-0.802 -0.72  -0.68  -0.642 -0.592 -0.573 -0.508 -0.501 -0.421 -0.366 -0.326 -0.306 -0.267 -0.225 -0.169 -0.153 -0.108 -0.071 -0.023  0.017  0.048  0.091  0.122  0.139  0.187\n  0.198  0.222  0.318  0.359  0.415  0.422  0.477  0.504  0.528  0.541  0.642  0.66   0.725  0.769  0.878]\n",
  "seconds": 0.72,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "467b7079dfee3544e8a71b72bfa251a76e82219ee9af26763a4579c85bb2ac85",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
COMMON L0 BROAD-HEAD WRITE SUBSPACE: singular values [11.996   8.4336  3.3624  1.312   0.9992  0.8615  0.7301  0.6058  0.5661  0.5402  0.4827  0.436 ]
Fraction of each head's OV energy in that 3D write subspace:
[np.float64(0.976), np.float64(0.9754), np.float64(0.0071), np.float64(0.3208), np.float64(0.923), np.float64(0.971)]
Top coordinates of its three orthonormal directions:
0 [(222, 0.9268), (78, 0.2902), (360, -0.1298), (72, -0.126), (254, -0.0778), (25, 0.0414), (197, -0.0354), (103, 0.0353)]
1 [(78, 0.7045), (360, 0.4506), (72, -0.3277), (103, -0.2017), (254, 0.2004), (25, -0.1744), (222, -0.1615), (273, -0.1318)]
2 [(72, -0.4814), (103, 0.4745), (360, -0.4638), (78, 0.1951), (222, -0.1875), (273, -0.1063), (130, 0.1011), (307, 0.1)]

LATER QK FRACTION USING COMMON 3D WRITE SUBSPACE
query-side, key-side, both-sides; bias-derived key coefficient fraction in this subspace
L1H0 [0.0678 0.6412 0.0555 0.8698]
3D coefficient matrix [[0.0519, -0.1085, -0.0667], [0.1361, 0.0631, 0.0309], [0.0217, 0.0219, 0.0196]] key-linear coefficients [0.8921, 0.7926, 0.4225]
L1H1 [0.0756 0.6003 0.0625 0.9031]
3D coefficient matrix [[0.0621, -0.1722, -0.103], [0.221, 0.0837, 0.0433], [0.0624, 0.0402, 0.0302]] key-linear coefficients [1.637, 1.254, 0.6797]
L1H2 [0.069  0.634  0.0601 0.9041]
3D coefficient matrix [[0.0548, -0.1987, -0.1216], [0.2606, 0.0802, 0.0387], [0.0943, 0.0414, 0.0292]] key-linear coefficients [1.8573, 1.4128, 0.7806]
L1H3 [0.0676 0.6351 0.0565 0.8899]
3D coefficient matrix [[0.0568, -0.1671, -0.1069], [0.2241, 0.0806, 0.0392], [0.0652, 0.0315, 0.0309]] key-linear coefficients [1.6221, 1.3243, 0.731]
L1H4 [0.0646 0.6915 0.0547 0.9054]
3D coefficient matrix [[0.0621, -0.1784, -0.1092], [0.234, 0.0729, 0.0346], [0.061, 0.0404, 0.0309]] key-linear coefficients [1.7177, 1.1775, 0.6418]
L1H5 [0.0784 0.6307 0.0642 0.8894]
3D coefficient matrix [[0.0676, -0.1796, -0.1128], [0.2279, 0.0752, 0.0385], [0.0585, 0.0454, 0.0312]] key-linear coefficients [1.2025, 0.9804, 0.5698]
L2H0 [0.0309 0.097  0.0234 0.8566]
L2H1 [0.0106 0.0469 0.0069 0.7939]
L2H2 [0.0254 0.1854 0.0183 0.8053]
L2H3 [0.0418 0.0967 0.0332 0.8732]
L2H4 [0.0338 0.098  0.0263 0.8644]
L2H5 [0.0345 0.0928 0.0266 0.8574]
L3H0 [0.0026 0.0136 0.0007 0.1503]
L3H1 [0.0043 0.0033 0.0004 0.0366]
L3H2 [0.0147 0.071  0.0079 0.5941]
L3H3 [0.0184 0.0671 0.0115 0.6708]
L3H4 [0.007  0.0452 0.0035 0.52  ]
L3H5 [0.0093 0.0106 0.0005 0.0378]

L0H2 RAW OV SINGULAR VALUES
[4.0009 3.853  3.7432 3.5437 3.4491 3.313  3.2443 3.1768 3.0968 2.9694 2.8864 2.7299 2.6846 2.4768 2.4314 2.0744 1.9734 1.7097 1.3762 1.1317 1.0096 0.97   0.8126 0.7541 0.6761
 0.6298 0.564  0.5469 0.4967 0.4718 0.4228 0.3839 0.3639 0.3453 0.3144 0.2761 0.2338 0.2223 0.1995 0.1695 0.1627 0.1369 0.1276 0.1113 0.1091 0.0945 0.0924 0.0748 0.0696 0.0642
 0.0588 0.0557 0.0526 0.0455 0.0437 0.04   0.0376 0.035  0.0327 0.0314 0.0283 0.0276 0.0243 0.0212]

MLP ROW CROSS-COVARIANCES IN L0H2 READ SUBSPACES
X = M T; Y = M B T. This compares weight-row geometry, without evaluating any neuron.
gain=False, rank=16: Frobenius_cos=0.1478, sym_generalized_eigs=[-0.344 -0.223 -0.058 -0.017  0.03   0.093  0.131  0.159  0.178  0.208  0.251  0.296  0.317  0.347  0.377  0.413]
gain=False, rank=24: Frobenius_cos=0.1313, sym_generalized_eigs=[-0.42  -0.337 -0.312 -0.23  -0.192 -0.122 -0.07  -0.043  0.042  0.055  0.095  0.096  0.145  0.172  0.192  0.222  0.234  0.271  0.336  0.363  0.374  0.45   0.461  0.548]
gain=False, rank=28: Frobenius_cos=0.1279, sym_generalized_eigs=[-0.591 -0.529 -0.402 -0.374 -0.325 -0.242 -0.2   -0.168 -0.097 -0.079 -0.018  0.057  0.072  0.093  0.104  0.153  0.182  0.211  0.22   0.255  0.269  0.336  0.385  0.407  0.427
  0.517  0.58   0.649]
gain=False, rank=40: Frobenius_cos=0.1228, sym_generalized_eigs=[-0.79  -0.74  -0.679 -0.634 -0.607 -0.59  -0.512 -0.408 -0.4   -0.305 -0.287 -0.272 -0.229 -0.211 -0.179 -0.12  -0.092 -0.057 -0.018  0.021  0.027  0.076  0.089  0.142  0.204
  0.218  0.267  0.294  0.319  0.373  0.401  0.441  0.47   0.481  0.581  0.595  0.649  0.705  0.724  0.828]
gain=True, rank=16: Frobenius_cos=0.1322, sym_generalized_eigs=[-0.384 -0.246 -0.126 -0.07  -0.014  0.024  0.133  0.166  0.18   0.21   0.218  0.282  0.307  0.349  0.402  0.476]
gain=True, rank=24: Frobenius_cos=0.1347, sym_generalized_eigs=[-0.456 -0.408 -0.324 -0.307 -0.214 -0.182 -0.108 -0.075 -0.031 -0.01   0.043  0.075  0.168  0.219  0.237  0.257  0.282  0.311  0.36   0.407  0.451  0.459  0.53   0.673]
gain=True, rank=28: Frobenius_cos=0.1291, sym_generalized_eigs=[-0.698 -0.621 -0.433 -0.333 -0.328 -0.312 -0.25  -0.217 -0.158 -0.127 -0.083 -0.038 -0.011  0.038  0.112  0.183  0.212  0.249  0.268  0.297  0.333  0.363  0.396  0.448  0.502
  0.528  0.625  0.71 ]
gain=True, rank=40: Frobenius_cos=0.1242, sym_generalized_eigs=[-0.802 -0.72  -0.68  -0.642 -0.592 -0.573 -0.508 -0.501 -0.421 -0.366 -0.326 -0.306 -0.267 -0.225 -0.169 -0.153 -0.108 -0.071 -0.023  0.017  0.048  0.091  0.122  0.139  0.187
  0.198  0.222  0.318  0.359  0.415  0.422  0.477  0.504  0.528  0.541  0.642  0.66   0.725  0.769  0.878]
t006 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 5,124 characters
OUTPUT of run 5 of at most 100:
```
COMMON L0 BROAD-HEAD WRITE SUBSPACE: singular values [11.996   8.4336  3.3624  1.312   0.9992  0.8615  0.7301  0.6058  0.5661  0.5402  0.4827  0.436 ]
Fraction of each head's OV energy in that 3D write subspace:
[np.float64(0.976), np.float64(0.9754), np.float64(0.0071), np.float64(0.3208), np.float64(0.923), np.float64(0.971)]
Top coordinates of its three orthonormal directions:
0 [(222, 0.9268), (78, 0.2902), (360, -0.1298), (72, -0.126), (254, -0.0778), (25, 0.0414), (197, -0.0354), (103, 0.0353)]
1 [(78, 0.7045), (360, 0.4506), (72, -0.3277), (103, -0.2017), (254, 0.2004), (25, -0.1744), (222, -0.1615), (273, -0.1318)]
2 [(72, -0.4814), (103, 0.4745), (360, -0.4638), (78, 0.1951), (222, -0.1875), (273, -0.1063), (130, 0.1011), (307, 0.1)]

LATER QK FRACTION USING COMMON 3D WRITE SUBSPACE
query-side, key-side, both-sides; bias-derived key coefficient fraction in this subspace
L1H0 [0.0678 0.6412 0.0555 0.8698]
3D coefficient matrix [[0.0519, -0.1085, -0.0667], [0.1361, 0.0631, 0.0309], [0.0217, 0.0219, 0.0196]] key-linear coefficients [0.8921, 0.7926, 0.4225]
L1H1 [0.0756 0.6003 0.0625 0.9031]
3D coefficient matrix [[0.0621, -0.1722, -0.103], [0.221, 0.0837, 0.0433], [0.0624, 0.0402, 0.0302]] key-linear coefficients [1.637, 1.254, 0.6797]
L1H2 [0.069  0.634  0.0601 0.9041]
3D coefficient matrix [[0.0548, -0.1987, -0.1216], [0.2606, 0.0802, 0.0387], [0.0943, 0.0414, 0.0292]] key-linear coefficients [1.8573, 1.4128, 0.7806]
L1H3 [0.0676 0.6351 0.0565 0.8899]
3D coefficient matrix [[0.0568, -0.1671, -0.1069], [0.2241, 0.0806, 0.0392], [0.0652, 0.0315, 0.0309]] key-linear coefficients [1.6221, 1.3243, 0.731]
L1H4 [0.0646 0.6915 0.0547 0.9054]
3D coefficient matrix [[0.0621, -0.1784, -0.1092], [0.234, 0.0729, 0.0346], [0.061, 0.0404, 0.0309]] key-linear coefficients [1.7177, 1.1775, 0.6418]
L1H5 [0.0784 0.6307 0.0642 0.8894]
3D coefficient matrix [[0.0676, -0.1796, -0.1128], [0.2279, 0.0752, 0.0385], [0.0585, 0.0454, 0.0312]] key-linear coefficients [1.2025, 0.9804, 0.5698]
L2H0 [0.0309 0.097  0.0234 0.8566]
L2H1 [0.0106 0.0469 0.0069 0.7939]
L2H2 [0.0254 0.1854 0.0183 0.8053]
L2H3 [0.0418 0.0967 0.0332 0.8732]
L2H4 [0.0338 0.098  0.0263 0.8644]
L2H5 [0.0345 0.0928 0.0266 0.8574]
L3H0 [0.0026 0.0136 0.0007 0.1503]
L3H1 [0.0043 0.0033 0.0004 0.0366]
L3H2 [0.0147 0.071  0.0079 0.5941]
L3H3 [0.0184 0.0671 0.0115 0.6708]
L3H4 [0.007  0.0452 0.0035 0.52  ]
L3H5 [0.0093 0.0106 0.0005 0.0378]

L0H2 RAW OV SINGULAR VALUES
[4.0009 3.853  3.7432 3.5437 3.4491 3.313  3.2443 3.1768 3.0968 2.9694 2.8864 2.7299 2.6846 2.4768 2.4314 2.0744 1.9734 1.7097 1.3762 1.1317 1.0096 0.97   0.8126 0.7541 0.6761
 0.6298 0.564  0.5469 0.4967 0.4718 0.4228 0.3839 0.3639 0.3453 0.3144 0.2761 0.2338 0.2223 0.1995 0.1695 0.1627 0.1369 0.1276 0.1113 0.1091 0.0945 0.0924 0.0748 0.0696 0.0642
 0.0588 0.0557 0.0526 0.0455 0.0437 0.04   0.0376 0.035  0.0327 0.0314 0.0283 0.0276 0.0243 0.0212]

MLP ROW CROSS-COVARIANCES IN L0H2 READ SUBSPACES
X = M T; Y = M B T. This compares weight-row geometry, without evaluating any neuron.
gain=False, rank=16: Frobenius_cos=0.1478, sym_generalized_eigs=[-0.344 -0.223 -0.058 -0.017  0.03   0.093  0.131  0.159  0.178  0.208  0.251  0.296  0.317  0.347  0.377  0.413]
gain=False, rank=24: Frobenius_cos=0.1313, sym_generalized_eigs=[-0.42  -0.337 -0.312 -0.23  -0.192 -0.122 -0.07  -0.043  0.042  0.055  0.095  0.096  0.145  0.172  0.192  0.222  0.234  0.271  0.336  0.363  0.374  0.45   0.461  0.548]
gain=False, rank=28: Frobenius_cos=0.1279, sym_generalized_eigs=[-0.591 -0.529 -0.402 -0.374 -0.325 -0.242 -0.2   -0.168 -0.097 -0.079 -0.018  0.057  0.072  0.093  0.104  0.153  0.182  0.211  0.22   0.255  0.269  0.336  0.385  0.407  0.427
  0.517  0.58   0.649]
gain=False, rank=40: Frobenius_cos=0.1228, sym_generalized_eigs=[-0.79  -0.74  -0.679 -0.634 -0.607 -0.59  -0.512 -0.408 -0.4   -0.305 -0.287 -0.272 -0.229 -0.211 -0.179 -0.12  -0.092 -0.057 -0.018  0.021  0.027  0.076  0.089  0.142  0.204
  0.218  0.267  0.294  0.319  0.373  0.401  0.441  0.47   0.481  0.581  0.595  0.649  0.705  0.724  0.828]
gain=True, rank=16: Frobenius_cos=0.1322, sym_generalized_eigs=[-0.384 -0.246 -0.126 -0.07  -0.014  0.024  0.133  0.166  0.18   0.21   0.218  0.282  0.307  0.349  0.402  0.476]
gain=True, rank=24: Frobenius_cos=0.1347, sym_generalized_eigs=[-0.456 -0.408 -0.324 -0.307 -0.214 -0.182 -0.108 -0.075 -0.031 -0.01   0.043  0.075  0.168  0.219  0.237  0.257  0.282  0.311  0.36   0.407  0.451  0.459  0.53   0.673]
gain=True, rank=28: Frobenius_cos=0.1291, sym_generalized_eigs=[-0.698 -0.621 -0.433 -0.333 -0.328 -0.312 -0.25  -0.217 -0.158 -0.127 -0.083 -0.038 -0.011  0.038  0.112  0.183  0.212  0.249  0.268  0.297  0.333  0.363  0.396  0.448  0.502
  0.528  0.625  0.71 ]
gain=True, rank=40: Frobenius_cos=0.1242, sym_generalized_eigs=[-0.802 -0.72  -0.68  -0.642 -0.592 -0.573 -0.508 -0.501 -0.421 -0.366 -0.326 -0.306 -0.267 -0.225 -0.169 -0.153 -0.108 -0.071 -0.023  0.017  0.048  0.091  0.122  0.139  0.187
  0.198  0.222  0.318  0.359  0.415  0.422  0.477  0.504  0.528  0.541  0.642  0.66   0.725  0.769  0.878]

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4, suppress=True, linewidth=180)

def svd(a):
    return la.svd(a,full_matrices=False,lapack_driver="gesvd")
def cos(a,b):
    return np.sum(a*b)/(la.norm(a)*la.norm(b)+1e-30)

with threadpool_limits(limits=2):
    A,B={},{}
    for l in range(4):
        g=np.asarray(W[f"ln1_w.{l}"],dtype=float)
        for h in range(6):
            sl=slice(h*64,h*64+64)
            q=np.asarray(W[f"W_q.{l}"][sl],dtype=float)*g
            k=np.asarray(W[f"W_k.{l}"][sl],dtype=float)*g
            A[l,h]=q.T@k/8
            B[l,h]=(np.asarray(W[f"W_o.{l}"][:,sl],dtype=float)@np.asarray(W[f"W_v.{l}"][sl],dtype=float))*g

    prev=B[0,2]
    _,_,vt=svd(prev)
    T=vt[:24].T
    U=prev@T
    print("DIRECT SHIFTED-MATCH SIGNATURES")
    print("tracecos=tr(A B)/(sqrt(384)||A B||F); positive_real_mass uses eigenvalues of A B.")
    for l in range(1,4):
        for h in range(6):
            a=A[l,h]
            c=a@prev
            e=la.eigvals(c)
            real_mass=np.maximum(e.real,0).sum()/(abs(e.real).sum()+1e-30)
            r=T.T@a@U
            match=np.trace(r)/(np.sqrt(24)*la.norm(r)+1e-30)
            print(f"L{l}H{h}: tracecos={np.trace(c)/(np.sqrt(384)*la.norm(c)):.4f}, "
                  f"positive_real_mass={real_mass:.4f}, "
                  f"token24_tracecos={match:.4f}, token24_symfrac={la.norm((r+r.T)/2)/(la.norm(r)+1e-30):.4f}")

    print("\nCIRCUIT TRACE-COSINES FOR ALL EARLIER HEADS")
    for l in (2,3):
        print("target layer",l,"columns", [f"L{m}H{h}" for m in range(l) for h in range(6)])
        for j in range(6):
            vals=[]
            for m in range(l):
                for h in range(6):
                    c=A[l,j]@B[m,h]
                    vals.append(np.trace(c)/(np.sqrt(384)*la.norm(c)+1e-30))
            print(f"L{l}H{j}",np.round(vals,4))

    print("\nHEAD VALUE-PATH ALIGNMENTS")
    print("Cosines among final-layer OV products:")
    print(np.array([[cos(B[3,i],B[3,j]) for j in range(6)] for i in range(6)]))
    print("Earlier OV products most aligned with each final-layer OV (direct tensor cosine):")
    for j in range(6):
        vals=sorted([(cos(B[3,j],B[l,h]),l,h) for l in range(3) for h in range(6)],reverse=True)
        print(f"L3H{j}",[(f"L{l}H{h}",round(v,4)) for v,l,h in vals[:4]])

    print("\nPOSITIONAL-CANDIDATE QK GEOMETRY OF L0 HEADS")
    _,_,v=svd(np.vstack([
        np.asarray(W["W_q.0"][128:192],dtype=float)*W["ln1_w.0"],
        np.asarray(W["W_k.0"][128:192],dtype=float)*W["ln1_w.0"]]))
    P=v[:32].T
    for j in range(6):
        a=A[0,j]
        print(f"L0H{j}: fraction_in_QK32={la.norm(P.T@a@P)**2/la.norm(a)**2:.4f}; "
              f"cosine_to_H2={cos(a,A[0,2]):.4f}")
```
Permitted experiment harness: python — full request and result
{
  "run": 6,
  "turn": "t006",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4, suppress=True, linewidth=180)\n\ndef svd(a):\n    return la.svd(a,full_matrices=False,lapack_driver=\"gesvd\")\ndef cos(a,b):\n    return np.sum(a*b)/(la.norm(a)*la.norm(b)+1e-30)\n\nwith threadpool_limits(limits=2):\n    A,B={},{}\n    for l in range(4):\n        g=np.asarray(W[f\"ln1_w.{l}\"],dtype=float)\n        for h in range(6):\n            sl=slice(h*64,h*64+64)\n            q=np.asarray(W[f\"W_q.{l}\"][sl],dtype=float)*g\n            k=np.asarray(W[f\"W_k.{l}\"][sl],dtype=float)*g\n            A[l,h]=q.T@k/8\n            B[l,h]=(np.asarray(W[f\"W_o.{l}\"][:,sl],dtype=float)@np.asarray(W[f\"W_v.{l}\"][sl],dtype=float))*g\n\n    prev=B[0,2]\n    _,_,vt=svd(prev)\n    T=vt[:24].T\n    U=prev@T\n    print(\"DIRECT SHIFTED-MATCH SIGNATURES\")\n    print(\"tracecos=tr(A B)/(sqrt(384)||A B||F); positive_real_mass uses eigenvalues of A B.\")\n    for l in range(1,4):\n        for h in range(6):\n            a=A[l,h]\n            c=a@prev\n            e=la.eigvals(c)\n            real_mass=np.maximum(e.real,0).sum()/(abs(e.real).sum()+1e-30)\n            r=T.T@a@U\n            match=np.trace(r)/(np.sqrt(24)*la.norm(r)+1e-30)\n            print(f\"L{l}H{h}: tracecos={np.trace(c)/(np.sqrt(384)*la.norm(c)):.4f}, \"\n                  f\"positive_real_mass={real_mass:.4f}, \"\n                  f\"token24_tracecos={match:.4f}, token24_symfrac={la.norm((r+r.T)/2)/(la.norm(r)+1e-30):.4f}\")\n\n    print(\"\\nCIRCUIT TRACE-COSINES FOR ALL EARLIER HEADS\")\n    for l in (2,3):\n        print(\"target layer\",l,\"columns\", [f\"L{m}H{h}\" for m in range(l) for h in range(6)])\n        for j in range(6):\n            vals=[]\n            for m in range(l):\n                for h in range(6):\n                    c=A[l,j]@B[m,h]\n                    vals.append(np.trace(c)/(np.sqrt(384)*la.norm(c)+1e-30))\n            print(f\"L{l}H{j}\",np.round(vals,4))\n\n    print(\"\\nHEAD VALUE-PATH ALIGNMENTS\")\n    print(\"Cosines among final-layer OV products:\")\n    print(np.array([[cos(B[3,i],B[3,j]) for j in range(6)] for i in range(6)]))\n    print(\"Earlier OV products most aligned with each final-layer OV (direct tensor cosine):\")\n    for j in range(6):\n        vals=sorted([(cos(B[3,j],B[l,h]),l,h) for l in range(3) for h in range(6)],reverse=True)\n        print(f\"L3H{j}\",[(f\"L{l}H{h}\",round(v,4)) for v,l,h in vals[:4]])\n\n    print(\"\\nPOSITIONAL-CANDIDATE QK GEOMETRY OF L0 HEADS\")\n    _,_,v=svd(np.vstack([\n        np.asarray(W[\"W_q.0\"][128:192],dtype=float)*W[\"ln1_w.0\"],\n        np.asarray(W[\"W_k.0\"][128:192],dtype=float)*W[\"ln1_w.0\"]]))\n    P=v[:32].T\n    for j in range(6):\n        a=A[0,j]\n        print(f\"L0H{j}: fraction_in_QK32={la.norm(P.T@a@P)**2/la.norm(a)**2:.4f}; \"\n              f\"cosine_to_H2={cos(a,A[0,2]):.4f}\")",
  "out": "DIRECT SHIFTED-MATCH SIGNATURES\ntracecos=tr(A B)/(sqrt(384)||A B||F); positive_real_mass uses eigenvalues of A B.\nL1H0: tracecos=-0.0058, positive_real_mass=0.3647, token24_tracecos=-0.0826, token24_symfrac=0.7322\nL1H1: tracecos=-0.0001, positive_real_mass=0.4984, token24_tracecos=-0.0047, token24_symfrac=0.7929\nL1H2: tracecos=0.0129, positive_real_mass=0.7087, token24_tracecos=0.1275, token24_symfrac=0.7309\nL1H3: tracecos=0.0119, positive_real_mass=0.7147, token24_tracecos=0.1500, token24_symfrac=0.7533\nL1H4: tracecos=0.0128, positive_real_mass=0.7262, token24_tracecos=0.1441, token24_symfrac=0.7240\nL1H5: tracecos=0.0039, positive_real_mass=0.6046, token24_tracecos=0.0461, token24_symfrac=0.7167\nL2H0: tracecos=-0.0041, positive_real_mass=0.4195, token24_tracecos=-0.0745, token24_symfrac=0.7069\nL2H1: tracecos=-0.0050, positive_real_mass=0.4182, token24_tracecos=-0.0863, token24_symfrac=0.7413\nL2H2: tracecos=0.0010, positive_real_mass=0.5172, token24_tracecos=0.0172, token24_symfrac=0.7189\nL2H3: tracecos=0.0027, positive_real_mass=0.5499, token24_tracecos=0.0639, token24_symfrac=0.7074\nL2H4: tracecos=-0.0073, positive_real_mass=0.3657, token24_tracecos=-0.1247, token24_symfrac=0.7092\nL2H5: tracecos=0.0003, positive_real_mass=0.5057, token24_tracecos=0.0129, token24_symfrac=0.7086\nL3H0: tracecos=-0.0021, positive_real_mass=0.4653, token24_tracecos=-0.0331, token24_symfrac=0.7044\nL3H1: tracecos=0.0598, positive_real_mass=0.9588, token24_tracecos=0.6237, token24_symfrac=0.9143\nL3H2: tracecos=0.0032, positive_real_mass=0.5545, token24_tracecos=0.0516, token24_symfrac=0.7135\nL3H3: tracecos=0.0012, positive_real_mass=0.5237, token24_tracecos=0.0177, token24_symfrac=0.6716\nL3H4: tracecos=-0.0049, positive_real_mass=0.4246, token24_tracecos=-0.0722, token24_symfrac=0.7652\nL3H5: tracecos=0.0024, positive_real_mass=0.5414, token24_tracecos=0.0416, token24_symfrac=0.7309\n\nCIRCUIT TRACE-COSINES FOR ALL EARLIER HEADS\ntarget layer 2 columns ['L0H0', 'L0H1', 'L0H2', 'L0H3', 'L0H4', 'L0H5', 'L1H0', 'L1H1', 'L1H2', 'L1H3', 'L1H4', 'L1H5']\nL2H0 [ 0.004   0.0031 -0.0041 -0.0099  0.0049  0.0037 -0.0219 -0.0047 -0.0048 -0.015  -0.0125 -0.0126]\nL2H1 [ 0.0036  0.0023 -0.005  -0.0066  0.0039  0.0037 -0.0148 -0.0207 -0.0087  0.0032 -0.0216 -0.0158]\nL2H2 [ 0.0056  0.0044  0.001  -0.0064  0.0069  0.0017 -0.0035 -0.0005  0.0026  0.0046  0.0089  0.0027]\nL2H3 [ 0.0056  0.004   0.0027 -0.0066  0.0059  0.005  -0.0092 -0.0072 -0.0081  0.0017 -0.0103 -0.0143]\nL2H4 [ 0.0073  0.0055 -0.0073 -0.0087  0.0077  0.0051 -0.0109 -0.0067 -0.0052 -0.003  -0.0092 -0.0147]\nL2H5 [ 0.0056  0.0037  0.0003 -0.0095  0.0056  0.004  -0.011  -0.0045  0.0004 -0.0145 -0.0063 -0.0197]\ntarget layer 3 columns ['L0H0', 'L0H1', 'L0H2', 'L0H3', 'L0H4', 'L0H5', 'L1H0', 'L1H1', 'L1H2', 'L1H3', 'L1H4', 'L1H5', 'L2H0', 'L2H1', 'L2H2', 'L2H3', 'L2H4', 'L2H5']\nL3H0 [ 0.0028  0.003  -0.0021 -0.003   0.0044  0.004  -0.0108 -0.0184 -0.0138 -0.0144 -0.0189 -0.0161 -0.0182 -0.0196 -0.0147 -0.0178 -0.0193 -0.0129]\nL3H1 [-0.003  -0.0047  0.0598  0.0009 -0.0066 -0.0045  0.0091  0.0321  0.0449  0.0392  0.0211  0.0019  0.02    0.0638  0.0243  0.0248  0.0108  0.0158]\nL3H2 [ 0.0044  0.0029  0.0032 -0.0042  0.0041  0.0052  0.0015  0.0003 -0.0034 -0.0011 -0.0017 -0.0019  0.0014 -0.0027 -0.0065 -0.0027 -0.0034 -0.0031]\nL3H3 [ 0.003   0.0014  0.0012 -0.0013  0.0025  0.0035 -0.0018 -0.0045 -0.0038 -0.0011 -0.0018 -0.      0.0009 -0.0024 -0.0036 -0.0039 -0.0031 -0.0008]\nL3H4 [ 0.0068  0.0072 -0.0049 -0.001   0.0082  0.0084 -0.0027 -0.0063 -0.0039 -0.0041 -0.0099 -0.0042  0.0008 -0.0029 -0.0047 -0.0032 -0.008  -0.0002]\nL3H5 [-0.0035 -0.0036  0.0024 -0.0029 -0.0033 -0.0022  0.0095  0.0049  0.0075  0.0045  0.0028  0.0063  0.0082  0.0111  0.0086  0.0162  0.0116  0.0101]\n\nHEAD VALUE-PATH ALIGNMENTS\nCosines among final-layer OV products:\n[[ 1.      0.0175 -0.0156 -0.0127  0.0179  0.0153]\n [ 0.0175  1.      0.0026  0.008   0.0062  0.0088]\n [-0.0156  0.0026  1.      0.0439  0.022   0.0461]\n [-0.0127  0.008   0.0439  1.      0.0177  0.1167]\n [ 0.0179  0.0062  0.022   0.0177  1.      0.0413]\n [ 0.0153  0.0088  0.0461  0.1167  0.0413  1.    ]]\nEarlier OV products most aligned with each final-layer OV (direct tensor cosine):\nL3H0 [('L2H1', np.float64(0.0264)), ('L2H3', np.float64(0.0152)), ('L2H5', np.float64(0.012)), ('L2H0', np.float64(0.0115))]\nL3H1 [('L2H5', np.float64(0.0111)), ('L2H2', np.float64(0.0081)), ('L2H3', np.float64(0.0068)), ('L0H3', np.float64(0.0036))]\nL3H2 [('L2H5', np.float64(0.0124)), ('L0H3', np.float64(0.0118)), ('L1H0', np.float64(0.0077)), ('L2H1', np.float64(0.0075))]\nL3H3 [('L0H4', np.float64(0.0197)), ('L0H0', np.float64(0.0166)), ('L0H1', np.float64(0.0163)), ('L0H5', np.float64(0.0148))]\nL3H4 [('L2H0', np.float64(0.0099)), ('L2H1', np.float64(0.008)), ('L1H1', np.float64(0.0079)), ('L1H5', np.float64(0.0077))]\nL3H5 [('L1H0', np.float64(0.0125)), ('L2H4', np.float64(0.0095)), ('L1H1', np.float64(0.0093)), ('L2H0', np.float64(0.0086))]\n\nPOSITIONAL-CANDIDATE QK GEOMETRY OF L0 HEADS\nL0H0: fraction_in_QK32=0.2391; cosine_to_H2=-0.0156\nL0H1: fraction_in_QK32=0.2564; cosine_to_H2=-0.0039\nL0H2: fraction_in_QK32=0.9772; cosine_to_H2=1.0000\nL0H3: fraction_in_QK32=0.7562; cosine_to_H2=0.2393\nL0H4: fraction_in_QK32=0.2328; cosine_to_H2=-0.0061\nL0H5: fraction_in_QK32=0.4134; cosine_to_H2=-0.0256\n",
  "seconds": 1.483,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "f80bedbe008ebe93db62366d820c6984477e5d10efe881a55112522879220dc4",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
DIRECT SHIFTED-MATCH SIGNATURES
tracecos=tr(A B)/(sqrt(384)||A B||F); positive_real_mass uses eigenvalues of A B.
L1H0: tracecos=-0.0058, positive_real_mass=0.3647, token24_tracecos=-0.0826, token24_symfrac=0.7322
L1H1: tracecos=-0.0001, positive_real_mass=0.4984, token24_tracecos=-0.0047, token24_symfrac=0.7929
L1H2: tracecos=0.0129, positive_real_mass=0.7087, token24_tracecos=0.1275, token24_symfrac=0.7309
L1H3: tracecos=0.0119, positive_real_mass=0.7147, token24_tracecos=0.1500, token24_symfrac=0.7533
L1H4: tracecos=0.0128, positive_real_mass=0.7262, token24_tracecos=0.1441, token24_symfrac=0.7240
L1H5: tracecos=0.0039, positive_real_mass=0.6046, token24_tracecos=0.0461, token24_symfrac=0.7167
L2H0: tracecos=-0.0041, positive_real_mass=0.4195, token24_tracecos=-0.0745, token24_symfrac=0.7069
L2H1: tracecos=-0.0050, positive_real_mass=0.4182, token24_tracecos=-0.0863, token24_symfrac=0.7413
L2H2: tracecos=0.0010, positive_real_mass=0.5172, token24_tracecos=0.0172, token24_symfrac=0.7189
L2H3: tracecos=0.0027, positive_real_mass=0.5499, token24_tracecos=0.0639, token24_symfrac=0.7074
L2H4: tracecos=-0.0073, positive_real_mass=0.3657, token24_tracecos=-0.1247, token24_symfrac=0.7092
L2H5: tracecos=0.0003, positive_real_mass=0.5057, token24_tracecos=0.0129, token24_symfrac=0.7086
L3H0: tracecos=-0.0021, positive_real_mass=0.4653, token24_tracecos=-0.0331, token24_symfrac=0.7044
L3H1: tracecos=0.0598, positive_real_mass=0.9588, token24_tracecos=0.6237, token24_symfrac=0.9143
L3H2: tracecos=0.0032, positive_real_mass=0.5545, token24_tracecos=0.0516, token24_symfrac=0.7135
L3H3: tracecos=0.0012, positive_real_mass=0.5237, token24_tracecos=0.0177, token24_symfrac=0.6716
L3H4: tracecos=-0.0049, positive_real_mass=0.4246, token24_tracecos=-0.0722, token24_symfrac=0.7652
L3H5: tracecos=0.0024, positive_real_mass=0.5414, token24_tracecos=0.0416, token24_symfrac=0.7309

CIRCUIT TRACE-COSINES FOR ALL EARLIER HEADS
target layer 2 columns ['L0H0', 'L0H1', 'L0H2', 'L0H3', 'L0H4', 'L0H5', 'L1H0', 'L1H1', 'L1H2', 'L1H3', 'L1H4', 'L1H5']
L2H0 [ 0.004   0.0031 -0.0041 -0.0099  0.0049  0.0037 -0.0219 -0.0047 -0.0048 -0.015  -0.0125 -0.0126]
L2H1 [ 0.0036  0.0023 -0.005  -0.0066  0.0039  0.0037 -0.0148 -0.0207 -0.0087  0.0032 -0.0216 -0.0158]
L2H2 [ 0.0056  0.0044  0.001  -0.0064  0.0069  0.0017 -0.0035 -0.0005  0.0026  0.0046  0.0089  0.0027]
L2H3 [ 0.0056  0.004   0.0027 -0.0066  0.0059  0.005  -0.0092 -0.0072 -0.0081  0.0017 -0.0103 -0.0143]
L2H4 [ 0.0073  0.0055 -0.0073 -0.0087  0.0077  0.0051 -0.0109 -0.0067 -0.0052 -0.003  -0.0092 -0.0147]
L2H5 [ 0.0056  0.0037  0.0003 -0.0095  0.0056  0.004  -0.011  -0.0045  0.0004 -0.0145 -0.0063 -0.0197]
target layer 3 columns ['L0H0', 'L0H1', 'L0H2', 'L0H3', 'L0H4', 'L0H5', 'L1H0', 'L1H1', 'L1H2', 'L1H3', 'L1H4', 'L1H5', 'L2H0', 'L2H1', 'L2H2', 'L2H3', 'L2H4', 'L2H5']
L3H0 [ 0.0028  0.003  -0.0021 -0.003   0.0044  0.004  -0.0108 -0.0184 -0.0138 -0.0144 -0.0189 -0.0161 -0.0182 -0.0196 -0.0147 -0.0178 -0.0193 -0.0129]
L3H1 [-0.003  -0.0047  0.0598  0.0009 -0.0066 -0.0045  0.0091  0.0321  0.0449  0.0392  0.0211  0.0019  0.02    0.0638  0.0243  0.0248  0.0108  0.0158]
L3H2 [ 0.0044  0.0029  0.0032 -0.0042  0.0041  0.0052  0.0015  0.0003 -0.0034 -0.0011 -0.0017 -0.0019  0.0014 -0.0027 -0.0065 -0.0027 -0.0034 -0.0031]
L3H3 [ 0.003   0.0014  0.0012 -0.0013  0.0025  0.0035 -0.0018 -0.0045 -0.0038 -0.0011 -0.0018 -0.      0.0009 -0.0024 -0.0036 -0.0039 -0.0031 -0.0008]
L3H4 [ 0.0068  0.0072 -0.0049 -0.001   0.0082  0.0084 -0.0027 -0.0063 -0.0039 -0.0041 -0.0099 -0.0042  0.0008 -0.0029 -0.0047 -0.0032 -0.008  -0.0002]
L3H5 [-0.0035 -0.0036  0.0024 -0.0029 -0.0033 -0.0022  0.0095  0.0049  0.0075  0.0045  0.0028  0.0063  0.0082  0.0111  0.0086  0.0162  0.0116  0.0101]

HEAD VALUE-PATH ALIGNMENTS
Cosines among final-layer OV products:
[[ 1.      0.0175 -0.0156 -0.0127  0.0179  0.0153]
 [ 0.0175  1.      0.0026  0.008   0.0062  0.0088]
 [-0.0156  0.0026  1.      0.0439  0.022   0.0461]
 [-0.0127  0.008   0.0439  1.      0.0177  0.1167]
 [ 0.0179  0.0062  0.022   0.0177  1.      0.0413]
 [ 0.0153  0.0088  0.0461  0.1167  0.0413  1.    ]]
Earlier OV products most aligned with each final-layer OV (direct tensor cosine):
L3H0 [('L2H1', np.float64(0.0264)), ('L2H3', np.float64(0.0152)), ('L2H5', np.float64(0.012)), ('L2H0', np.float64(0.0115))]
L3H1 [('L2H5', np.float64(0.0111)), ('L2H2', np.float64(0.0081)), ('L2H3', np.float64(0.0068)), ('L0H3', np.float64(0.0036))]
L3H2 [('L2H5', np.float64(0.0124)), ('L0H3', np.float64(0.0118)), ('L1H0', np.float64(0.0077)), ('L2H1', np.float64(0.0075))]
L3H3 [('L0H4', np.float64(0.0197)), ('L0H0', np.float64(0.0166)), ('L0H1', np.float64(0.0163)), ('L0H5', np.float64(0.0148))]
L3H4 [('L2H0', np.float64(0.0099)), ('L2H1', np.float64(0.008)), ('L1H1', np.float64(0.0079)), ('L1H5', np.float64(0.0077))]
L3H5 [('L1H0', np.float64(0.0125)), ('L2H4', np.float64(0.0095)), ('L1H1', np.float64(0.0093)), ('L2H0', np.float64(0.0086))]

POSITIONAL-CANDIDATE QK GEOMETRY OF L0 HEADS
L0H0: fraction_in_QK32=0.2391; cosine_to_H2=-0.0156
L0H1: fraction_in_QK32=0.2564; cosine_to_H2=-0.0039
L0H2: fraction_in_QK32=0.9772; cosine_to_H2=1.0000
L0H3: fraction_in_QK32=0.7562; cosine_to_H2=0.2393
L0H4: fraction_in_QK32=0.2328; cosine_to_H2=-0.0061
L0H5: fraction_in_QK32=0.4134; cosine_to_H2=-0.0256
t007 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 5,355 characters
OUTPUT of run 6 of at most 100:
```
DIRECT SHIFTED-MATCH SIGNATURES
tracecos=tr(A B)/(sqrt(384)||A B||F); positive_real_mass uses eigenvalues of A B.
L1H0: tracecos=-0.0058, positive_real_mass=0.3647, token24_tracecos=-0.0826, token24_symfrac=0.7322
L1H1: tracecos=-0.0001, positive_real_mass=0.4984, token24_tracecos=-0.0047, token24_symfrac=0.7929
L1H2: tracecos=0.0129, positive_real_mass=0.7087, token24_tracecos=0.1275, token24_symfrac=0.7309
L1H3: tracecos=0.0119, positive_real_mass=0.7147, token24_tracecos=0.1500, token24_symfrac=0.7533
L1H4: tracecos=0.0128, positive_real_mass=0.7262, token24_tracecos=0.1441, token24_symfrac=0.7240
L1H5: tracecos=0.0039, positive_real_mass=0.6046, token24_tracecos=0.0461, token24_symfrac=0.7167
L2H0: tracecos=-0.0041, positive_real_mass=0.4195, token24_tracecos=-0.0745, token24_symfrac=0.7069
L2H1: tracecos=-0.0050, positive_real_mass=0.4182, token24_tracecos=-0.0863, token24_symfrac=0.7413
L2H2: tracecos=0.0010, positive_real_mass=0.5172, token24_tracecos=0.0172, token24_symfrac=0.7189
L2H3: tracecos=0.0027, positive_real_mass=0.5499, token24_tracecos=0.0639, token24_symfrac=0.7074
L2H4: tracecos=-0.0073, positive_real_mass=0.3657, token24_tracecos=-0.1247, token24_symfrac=0.7092
L2H5: tracecos=0.0003, positive_real_mass=0.5057, token24_tracecos=0.0129, token24_symfrac=0.7086
L3H0: tracecos=-0.0021, positive_real_mass=0.4653, token24_tracecos=-0.0331, token24_symfrac=0.7044
L3H1: tracecos=0.0598, positive_real_mass=0.9588, token24_tracecos=0.6237, token24_symfrac=0.9143
L3H2: tracecos=0.0032, positive_real_mass=0.5545, token24_tracecos=0.0516, token24_symfrac=0.7135
L3H3: tracecos=0.0012, positive_real_mass=0.5237, token24_tracecos=0.0177, token24_symfrac=0.6716
L3H4: tracecos=-0.0049, positive_real_mass=0.4246, token24_tracecos=-0.0722, token24_symfrac=0.7652
L3H5: tracecos=0.0024, positive_real_mass=0.5414, token24_tracecos=0.0416, token24_symfrac=0.7309

CIRCUIT TRACE-COSINES FOR ALL EARLIER HEADS
target layer 2 columns ['L0H0', 'L0H1', 'L0H2', 'L0H3', 'L0H4', 'L0H5', 'L1H0', 'L1H1', 'L1H2', 'L1H3', 'L1H4', 'L1H5']
L2H0 [ 0.004   0.0031 -0.0041 -0.0099  0.0049  0.0037 -0.0219 -0.0047 -0.0048 -0.015  -0.0125 -0.0126]
L2H1 [ 0.0036  0.0023 -0.005  -0.0066  0.0039  0.0037 -0.0148 -0.0207 -0.0087  0.0032 -0.0216 -0.0158]
L2H2 [ 0.0056  0.0044  0.001  -0.0064  0.0069  0.0017 -0.0035 -0.0005  0.0026  0.0046  0.0089  0.0027]
L2H3 [ 0.0056  0.004   0.0027 -0.0066  0.0059  0.005  -0.0092 -0.0072 -0.0081  0.0017 -0.0103 -0.0143]
L2H4 [ 0.0073  0.0055 -0.0073 -0.0087  0.0077  0.0051 -0.0109 -0.0067 -0.0052 -0.003  -0.0092 -0.0147]
L2H5 [ 0.0056  0.0037  0.0003 -0.0095  0.0056  0.004  -0.011  -0.0045  0.0004 -0.0145 -0.0063 -0.0197]
target layer 3 columns ['L0H0', 'L0H1', 'L0H2', 'L0H3', 'L0H4', 'L0H5', 'L1H0', 'L1H1', 'L1H2', 'L1H3', 'L1H4', 'L1H5', 'L2H0', 'L2H1', 'L2H2', 'L2H3', 'L2H4', 'L2H5']
L3H0 [ 0.0028  0.003  -0.0021 -0.003   0.0044  0.004  -0.0108 -0.0184 -0.0138 -0.0144 -0.0189 -0.0161 -0.0182 -0.0196 -0.0147 -0.0178 -0.0193 -0.0129]
L3H1 [-0.003  -0.0047  0.0598  0.0009 -0.0066 -0.0045  0.0091  0.0321  0.0449  0.0392  0.0211  0.0019  0.02    0.0638  0.0243  0.0248  0.0108  0.0158]
L3H2 [ 0.0044  0.0029  0.0032 -0.0042  0.0041  0.0052  0.0015  0.0003 -0.0034 -0.0011 -0.0017 -0.0019  0.0014 -0.0027 -0.0065 -0.0027 -0.0034 -0.0031]
L3H3 [ 0.003   0.0014  0.0012 -0.0013  0.0025  0.0035 -0.0018 -0.0045 -0.0038 -0.0011 -0.0018 -0.      0.0009 -0.0024 -0.0036 -0.0039 -0.0031 -0.0008]
L3H4 [ 0.0068  0.0072 -0.0049 -0.001   0.0082  0.0084 -0.0027 -0.0063 -0.0039 -0.0041 -0.0099 -0.0042  0.0008 -0.0029 -0.0047 -0.0032 -0.008  -0.0002]
L3H5 [-0.0035 -0.0036  0.0024 -0.0029 -0.0033 -0.0022  0.0095  0.0049  0.0075  0.0045  0.0028  0.0063  0.0082  0.0111  0.0086  0.0162  0.0116  0.0101]

HEAD VALUE-PATH ALIGNMENTS
Cosines among final-layer OV products:
[[ 1.      0.0175 -0.0156 -0.0127  0.0179  0.0153]
 [ 0.0175  1.      0.0026  0.008   0.0062  0.0088]
 [-0.0156  0.0026  1.      0.0439  0.022   0.0461]
 [-0.0127  0.008   0.0439  1.      0.0177  0.1167]
 [ 0.0179  0.0062  0.022   0.0177  1.      0.0413]
 [ 0.0153  0.0088  0.0461  0.1167  0.0413  1.    ]]
Earlier OV products most aligned with each final-layer OV (direct tensor cosine):
L3H0 [('L2H1', np.float64(0.0264)), ('L2H3', np.float64(0.0152)), ('L2H5', np.float64(0.012)), ('L2H0', np.float64(0.0115))]
L3H1 [('L2H5', np.float64(0.0111)), ('L2H2', np.float64(0.0081)), ('L2H3', np.float64(0.0068)), ('L0H3', np.float64(0.0036))]
L3H2 [('L2H5', np.float64(0.0124)), ('L0H3', np.float64(0.0118)), ('L1H0', np.float64(0.0077)), ('L2H1', np.float64(0.0075))]
L3H3 [('L0H4', np.float64(0.0197)), ('L0H0', np.float64(0.0166)), ('L0H1', np.float64(0.0163)), ('L0H5', np.float64(0.0148))]
L3H4 [('L2H0', np.float64(0.0099)), ('L2H1', np.float64(0.008)), ('L1H1', np.float64(0.0079)), ('L1H5', np.float64(0.0077))]
L3H5 [('L1H0', np.float64(0.0125)), ('L2H4', np.float64(0.0095)), ('L1H1', np.float64(0.0093)), ('L2H0', np.float64(0.0086))]

POSITIONAL-CANDIDATE QK GEOMETRY OF L0 HEADS
L0H0: fraction_in_QK32=0.2391; cosine_to_H2=-0.0156
L0H1: fraction_in_QK32=0.2564; cosine_to_H2=-0.0039
L0H2: fraction_in_QK32=0.9772; cosine_to_H2=1.0000
L0H3: fraction_in_QK32=0.7562; cosine_to_H2=0.2393
L0H4: fraction_in_QK32=0.2328; cosine_to_H2=-0.0061
L0H5: fraction_in_QK32=0.4134; cosine_to_H2=-0.0256

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from scipy.stats import skew, kurtosis
from sklearn.cluster import KMeans
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4,suppress=True,linewidth=180)

def svd(a):
    return la.svd(a,full_matrices=False,lapack_driver="gesvd")

with threadpool_limits(limits=2):
    print("MLP NEURON NORM QUANTILES: input row / output column")
    for l in range(4):
        f=np.asarray(W[f"W_fc.{l}"],dtype=float)
        p=np.asarray(W[f"W_proj.{l}"],dtype=float)
        print(l,np.percentile(la.norm(f,axis=1),[0,5,25,50,75,95,100]),
              np.percentile(la.norm(p,axis=0),[0,5,25,50,75,95,100]))

    print("\nLAST MLP OUTPUT-COLUMN GEOMETRY")
    P=np.asarray(W["W_proj.3"],dtype=float).T
    norms=la.norm(P,axis=1)
    Z=P/norms[:,None]
    gram=Z@Z.T
    np.fill_diagonal(gram,-2)
    print("nearest cosine percentiles:",np.percentile(gram.max(axis=1),[0,10,25,50,75,90,99,100]))
    for n in (20,24,28,32):
        km=KMeans(n_clusters=n,n_init=5,random_state=0,max_iter=200).fit(Z)
        sizes=np.bincount(km.labels_,minlength=n)
        c_norm=la.norm(km.cluster_centers_,axis=1)
        order=np.argsort(-sizes)
        print("k",n,"inertia",round(km.inertia_,3))
        print("cluster sizes",sizes[order])
        print("centroid norms",np.round(c_norm[order],3))

    print("\nINDEPENDENT-COMPONENT DECOMPOSITION OF OUTPUT WEIGHT COLUMNS")
    for nc in (24,28):
        ica=FastICA(n_components=nc,whiten="unit-variance",random_state=0,max_iter=1500,tol=1e-4)
        S=ica.fit_transform(P)
        kur=kurtosis(S,axis=0)
        sk=skew(S,axis=0)
        ix=np.argsort(-kur)
        print("components",nc,"iterations",ica.n_iter_)
        print("excess kurtoses",np.round(kur[ix],3))
        print("absolute skewness",np.round(abs(sk[ix]),3))

    print("\nINDUCTION-CANDIDATE MATRIX EIGENVALUES")
    g0=np.asarray(W["ln1_w.0"],dtype=float)
    b=(np.asarray(W["W_o.0"][:,128:192],dtype=float)@np.asarray(W["W_v.0"][128:192],dtype=float))*g0
    g3=np.asarray(W["ln1_w.3"],dtype=float)
    q=np.asarray(W["W_q.3"][64:128],dtype=float)*g3
    k=np.asarray(W["W_k.3"][64:128],dtype=float)*g3
    a=q.T@k/8
    _,_,vt=svd(b)
    T=vt[:64].T
    c=T.T@a@b@T
    e=la.eigvals(c)
    print("eigenvalues descending magnitude",np.round(e[np.argsort(-abs(e))],4))
    print("symmetric-part eigenvalues descending",np.round(la.eigvalsh((c+c.T)/2)[::-1],4))
```
Permitted experiment harness: python — full request and result
{
  "run": 7,
  "turn": "t007",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom scipy.stats import skew, kurtosis\nfrom sklearn.cluster import KMeans\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4,suppress=True,linewidth=180)\n\ndef svd(a):\n    return la.svd(a,full_matrices=False,lapack_driver=\"gesvd\")\n\nwith threadpool_limits(limits=2):\n    print(\"MLP NEURON NORM QUANTILES: input row / output column\")\n    for l in range(4):\n        f=np.asarray(W[f\"W_fc.{l}\"],dtype=float)\n        p=np.asarray(W[f\"W_proj.{l}\"],dtype=float)\n        print(l,np.percentile(la.norm(f,axis=1),[0,5,25,50,75,95,100]),\n              np.percentile(la.norm(p,axis=0),[0,5,25,50,75,95,100]))\n\n    print(\"\\nLAST MLP OUTPUT-COLUMN GEOMETRY\")\n    P=np.asarray(W[\"W_proj.3\"],dtype=float).T\n    norms=la.norm(P,axis=1)\n    Z=P/norms[:,None]\n    gram=Z@Z.T\n    np.fill_diagonal(gram,-2)\n    print(\"nearest cosine percentiles:\",np.percentile(gram.max(axis=1),[0,10,25,50,75,90,99,100]))\n    for n in (20,24,28,32):\n        km=KMeans(n_clusters=n,n_init=5,random_state=0,max_iter=200).fit(Z)\n        sizes=np.bincount(km.labels_,minlength=n)\n        c_norm=la.norm(km.cluster_centers_,axis=1)\n        order=np.argsort(-sizes)\n        print(\"k\",n,\"inertia\",round(km.inertia_,3))\n        print(\"cluster sizes\",sizes[order])\n        print(\"centroid norms\",np.round(c_norm[order],3))\n\n    print(\"\\nINDEPENDENT-COMPONENT DECOMPOSITION OF OUTPUT WEIGHT COLUMNS\")\n    for nc in (24,28):\n        ica=FastICA(n_components=nc,whiten=\"unit-variance\",random_state=0,max_iter=1500,tol=1e-4)\n        S=ica.fit_transform(P)\n        kur=kurtosis(S,axis=0)\n        sk=skew(S,axis=0)\n        ix=np.argsort(-kur)\n        print(\"components\",nc,\"iterations\",ica.n_iter_)\n        print(\"excess kurtoses\",np.round(kur[ix],3))\n        print(\"absolute skewness\",np.round(abs(sk[ix]),3))\n\n    print(\"\\nINDUCTION-CANDIDATE MATRIX EIGENVALUES\")\n    g0=np.asarray(W[\"ln1_w.0\"],dtype=float)\n    b=(np.asarray(W[\"W_o.0\"][:,128:192],dtype=float)@np.asarray(W[\"W_v.0\"][128:192],dtype=float))*g0\n    g3=np.asarray(W[\"ln1_w.3\"],dtype=float)\n    q=np.asarray(W[\"W_q.3\"][64:128],dtype=float)*g3\n    k=np.asarray(W[\"W_k.3\"][64:128],dtype=float)*g3\n    a=q.T@k/8\n    _,_,vt=svd(b)\n    T=vt[:64].T\n    c=T.T@a@b@T\n    e=la.eigvals(c)\n    print(\"eigenvalues descending magnitude\",np.round(e[np.argsort(-abs(e))],4))\n    print(\"symmetric-part eigenvalues descending\",np.round(la.eigvalsh((c+c.T)/2)[::-1],4))",
  "out": "MLP NEURON NORM QUANTILES: input row / output column\n0 [0.3266 0.3623 0.3895 0.4232 0.9669 2.6887 4.4219] [0.1318 0.1623 0.194  0.2373 1.2717 2.154  4.7954]\n1 [0.3913 1.7931 1.9056 2.004  2.1051 2.2776 3.3711] [0.1986 1.7341 1.8466 1.9219 2.0072 2.1576 2.6756]\n2 [1.7221 1.9046 1.9707 2.0103 2.0551 2.1253 2.4825] [1.5505 1.7804 1.8885 1.9574 2.0251 2.1289 2.4465]\n3 [1.7763 1.8813 1.9381 1.9761 2.014  2.0986 3.1298] [1.4454 1.735  1.9208 2.0615 2.2221 2.491  4.8197]\n\nLAST MLP OUTPUT-COLUMN GEOMETRY\nnearest cosine percentiles: [0.459  0.5382 0.5696 0.605  0.6485 0.7103 0.9073 0.9462]\nk 20 inertia 1139.135\ncluster sizes [107  96  89  88  87  85  84  83  81  78  74  74  73  71  69  68  66  64  52  47]\ncentroid norms [0.7   0.475 0.529 0.49  0.448 0.475 0.5   0.508 0.465 0.497 0.502 0.469 0.499 0.491 0.481 0.491 0.505 0.471 0.481 0.57 ]\nk 24 inertia 1117.005\ncluster sizes [93 79 77 74 73 72 70 67 66 66 66 66 65 58 58 58 57 57 57 56 54 51 48 48]\ncentroid norms [0.6   0.726 0.511 0.435 0.501 0.508 0.458 0.507 0.485 0.528 0.5   0.564 0.485 0.506 0.488 0.5   0.507 0.477 0.486 0.513 0.523 0.517 0.519 0.54 ]\nk 28 inertia 1095.936\ncluster sizes [78 76 73 68 63 63 62 61 59 58 58 55 55 55 53 52 51 50 49 48 48 47 46 44 43 43 40 38]\ncentroid norms [0.725 0.653 0.504 0.506 0.511 0.505 0.533 0.588 0.479 0.508 0.488 0.496 0.5   0.524 0.493 0.548 0.456 0.556 0.499 0.517 0.532 0.522 0.492 0.508 0.5   0.502 0.522 0.602]\nk 32 inertia 1077.692\ncluster sizes [74 61 61 59 57 57 55 52 52 50 48 48 48 48 47 46 46 46 44 44 43 43 43 42 42 42 41 41 41 41 38 36]\ncentroid norms [0.72  0.481 0.499 0.487 0.545 0.698 0.532 0.518 0.514 0.542 0.503 0.54  0.55  0.566 0.532 0.553 0.537 0.502 0.513 0.56  0.543 0.512 0.489 0.49  0.53  0.519 0.557 0.5   0.662\n 0.498 0.517 0.57 ]\n\nINDEPENDENT-COMPONENT DECOMPOSITION OF OUTPUT WEIGHT COLUMNS\ncomponents 24 iterations 660\nexcess kurtoses [ 4.353  1.607  1.307  1.306  1.246  1.156  1.15   1.028  1.002  0.996  0.884  0.875  0.83   0.809  0.798 -0.443 -0.46  -0.493 -0.554 -0.576 -0.583 -0.591 -0.62  -0.673]\nabsolute skewness [0.368 0.464 0.003 0.307 0.177 0.08  0.234 0.22  0.08  0.157 0.179 0.084 0.3   0.13  0.094 0.141 0.069 0.055 0.023 0.086 0.01  0.006 0.053 0.139]\ncomponents 28 iterations 1416\nexcess kurtoses [37.202  4.452  1.572  1.357  1.232  1.232  1.154  1.153  1.131  1.131  1.083  1.049  0.934  0.925  0.876  0.854  0.795  0.789  0.779  0.745 -0.517 -0.566 -0.576 -0.58  -0.587\n -0.605 -0.626 -0.702]\nabsolute skewness [0.607 0.415 0.422 0.155 0.142 0.278 0.443 0.171 0.027 0.256 0.128 0.283 0.124 0.174 0.107 0.012 0.    0.076 0.211 0.181 0.074 0.119 0.085 0.056 0.121 0.082 0.017 0.057]\n\nINDUCTION-CANDIDATE MATRIX EIGENVALUES\neigenvalues descending magnitude [ 0.0505+0.0128j  0.0505-0.0128j  0.0423+0.j      0.0356+0.0108j  0.0356-0.0108j  0.0321+0.j      0.0306+0.0061j  0.0306-0.0061j  0.0269+0.0028j  0.0269-0.0028j  0.0151+0.0061j\n  0.0151-0.0061j  0.0139+0.j      0.0117+0.j      0.0056+0.0101j  0.0056-0.0101j  0.0085+0.j      0.0019+0.005j   0.0019-0.005j   0.0037+0.0019j  0.0037-0.0019j -0.0019+0.0034j\n -0.0019-0.0034j  0.0034+0.j     -0.0022+0.0025j -0.0022-0.0025j  0.0019+0.0022j  0.0019-0.0022j -0.0025+0.0002j -0.0025-0.0002j -0.0021+0.j     -0.0015-0.0006j -0.0015+0.0006j\n  0.0008+0.0012j  0.0008-0.0012j  0.0009+0.j     -0.0003+0.0007j -0.0003-0.0007j  0.0006+0.0002j  0.0006-0.0002j  0.0006+0.j      0.0002+0.0005j  0.0002-0.0005j  0.0003+0.0003j\n  0.0003-0.0003j -0.0002+0.0004j -0.0002-0.0004j -0.0003+0.j      0.    -0.0002j  0.    +0.0002j -0.0002+0.j      0.0001+0.j      0.0001-0.j     -0.0001+0.j     -0.0001-0.j\n  0.0001+0.j     -0.    +0.0001j -0.    -0.0001j  0.0001+0.j      0.0001-0.j      0.    +0.j     -0.    +0.j     -0.    +0.j     -0.    +0.j    ]\nsymmetric-part eigenvalues descending [ 0.0673  0.0527  0.0511  0.0468  0.0452  0.0421  0.0364  0.0329  0.0319  0.0281  0.0273  0.0188  0.0175  0.0171  0.0125  0.0103  0.0077  0.0063  0.0058  0.0044  0.0042  0.0027\n  0.0024  0.0021  0.0014  0.0012  0.001   0.001   0.0008  0.0004  0.0003  0.0002  0.0001  0.0001 -0.0002 -0.0003 -0.0003 -0.0005 -0.0006 -0.0007 -0.0009 -0.0012 -0.0014 -0.0017\n -0.0018 -0.002  -0.0024 -0.0025 -0.0028 -0.0029 -0.0036 -0.0041 -0.0046 -0.005  -0.0052 -0.0059 -0.0073 -0.0084 -0.0086 -0.0098 -0.0103 -0.0126 -0.0142 -0.0173]\n",
  "seconds": 2.352,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "6fa4ced7ba259926fa64dea27d3889903ebe6db0d6e2561f0f997f7bacf2b679",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
MLP NEURON NORM QUANTILES: input row / output column
0 [0.3266 0.3623 0.3895 0.4232 0.9669 2.6887 4.4219] [0.1318 0.1623 0.194  0.2373 1.2717 2.154  4.7954]
1 [0.3913 1.7931 1.9056 2.004  2.1051 2.2776 3.3711] [0.1986 1.7341 1.8466 1.9219 2.0072 2.1576 2.6756]
2 [1.7221 1.9046 1.9707 2.0103 2.0551 2.1253 2.4825] [1.5505 1.7804 1.8885 1.9574 2.0251 2.1289 2.4465]
3 [1.7763 1.8813 1.9381 1.9761 2.014  2.0986 3.1298] [1.4454 1.735  1.9208 2.0615 2.2221 2.491  4.8197]

LAST MLP OUTPUT-COLUMN GEOMETRY
nearest cosine percentiles: [0.459  0.5382 0.5696 0.605  0.6485 0.7103 0.9073 0.9462]
k 20 inertia 1139.135
cluster sizes [107  96  89  88  87  85  84  83  81  78  74  74  73  71  69  68  66  64  52  47]
centroid norms [0.7   0.475 0.529 0.49  0.448 0.475 0.5   0.508 0.465 0.497 0.502 0.469 0.499 0.491 0.481 0.491 0.505 0.471 0.481 0.57 ]
k 24 inertia 1117.005
cluster sizes [93 79 77 74 73 72 70 67 66 66 66 66 65 58 58 58 57 57 57 56 54 51 48 48]
centroid norms [0.6   0.726 0.511 0.435 0.501 0.508 0.458 0.507 0.485 0.528 0.5   0.564 0.485 0.506 0.488 0.5   0.507 0.477 0.486 0.513 0.523 0.517 0.519 0.54 ]
k 28 inertia 1095.936
cluster sizes [78 76 73 68 63 63 62 61 59 58 58 55 55 55 53 52 51 50 49 48 48 47 46 44 43 43 40 38]
centroid norms [0.725 0.653 0.504 0.506 0.511 0.505 0.533 0.588 0.479 0.508 0.488 0.496 0.5   0.524 0.493 0.548 0.456 0.556 0.499 0.517 0.532 0.522 0.492 0.508 0.5   0.502 0.522 0.602]
k 32 inertia 1077.692
cluster sizes [74 61 61 59 57 57 55 52 52 50 48 48 48 48 47 46 46 46 44 44 43 43 43 42 42 42 41 41 41 41 38 36]
centroid norms [0.72  0.481 0.499 0.487 0.545 0.698 0.532 0.518 0.514 0.542 0.503 0.54  0.55  0.566 0.532 0.553 0.537 0.502 0.513 0.56  0.543 0.512 0.489 0.49  0.53  0.519 0.557 0.5   0.662
 0.498 0.517 0.57 ]

INDEPENDENT-COMPONENT DECOMPOSITION OF OUTPUT WEIGHT COLUMNS
components 24 iterations 660
excess kurtoses [ 4.353  1.607  1.307  1.306  1.246  1.156  1.15   1.028  1.002  0.996  0.884  0.875  0.83   0.809  0.798 -0.443 -0.46  -0.493 -0.554 -0.576 -0.583 -0.591 -0.62  -0.673]
absolute skewness [0.368 0.464 0.003 0.307 0.177 0.08  0.234 0.22  0.08  0.157 0.179 0.084 0.3   0.13  0.094 0.141 0.069 0.055 0.023 0.086 0.01  0.006 0.053 0.139]
components 28 iterations 1416
excess kurtoses [37.202  4.452  1.572  1.357  1.232  1.232  1.154  1.153  1.131  1.131  1.083  1.049  0.934  0.925  0.876  0.854  0.795  0.789  0.779  0.745 -0.517 -0.566 -0.576 -0.58  -0.587
 -0.605 -0.626 -0.702]
absolute skewness [0.607 0.415 0.422 0.155 0.142 0.278 0.443 0.171 0.027 0.256 0.128 0.283 0.124 0.174 0.107 0.012 0.    0.076 0.211 0.181 0.074 0.119 0.085 0.056 0.121 0.082 0.017 0.057]

INDUCTION-CANDIDATE MATRIX EIGENVALUES
eigenvalues descending magnitude [ 0.0505+0.0128j  0.0505-0.0128j  0.0423+0.j      0.0356+0.0108j  0.0356-0.0108j  0.0321+0.j      0.0306+0.0061j  0.0306-0.0061j  0.0269+0.0028j  0.0269-0.0028j  0.0151+0.0061j
  0.0151-0.0061j  0.0139+0.j      0.0117+0.j      0.0056+0.0101j  0.0056-0.0101j  0.0085+0.j      0.0019+0.005j   0.0019-0.005j   0.0037+0.0019j  0.0037-0.0019j -0.0019+0.0034j
 -0.0019-0.0034j  0.0034+0.j     -0.0022+0.0025j -0.0022-0.0025j  0.0019+0.0022j  0.0019-0.0022j -0.0025+0.0002j -0.0025-0.0002j -0.0021+0.j     -0.0015-0.0006j -0.0015+0.0006j
  0.0008+0.0012j  0.0008-0.0012j  0.0009+0.j     -0.0003+0.0007j -0.0003-0.0007j  0.0006+0.0002j  0.0006-0.0002j  0.0006+0.j      0.0002+0.0005j  0.0002-0.0005j  0.0003+0.0003j
  0.0003-0.0003j -0.0002+0.0004j -0.0002-0.0004j -0.0003+0.j      0.    -0.0002j  0.    +0.0002j -0.0002+0.j      0.0001+0.j      0.0001-0.j     -0.0001+0.j     -0.0001-0.j
  0.0001+0.j     -0.    +0.0001j -0.    -0.0001j  0.0001+0.j      0.0001-0.j      0.    +0.j     -0.    +0.j     -0.    +0.j     -0.    +0.j    ]
symmetric-part eigenvalues descending [ 0.0673  0.0527  0.0511  0.0468  0.0452  0.0421  0.0364  0.0329  0.0319  0.0281  0.0273  0.0188  0.0175  0.0171  0.0125  0.0103  0.0077  0.0063  0.0058  0.0044  0.0042  0.0027
  0.0024  0.0021  0.0014  0.0012  0.001   0.001   0.0008  0.0004  0.0003  0.0002  0.0001  0.0001 -0.0002 -0.0003 -0.0003 -0.0005 -0.0006 -0.0007 -0.0009 -0.0012 -0.0014 -0.0017
 -0.0018 -0.002  -0.0024 -0.0025 -0.0028 -0.0029 -0.0036 -0.0041 -0.0046 -0.005  -0.0052 -0.0059 -0.0073 -0.0084 -0.0086 -0.0098 -0.0103 -0.0126 -0.0142 -0.0173]
t008 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 4,333 characters
OUTPUT of run 7 of at most 100:
```
MLP NEURON NORM QUANTILES: input row / output column
0 [0.3266 0.3623 0.3895 0.4232 0.9669 2.6887 4.4219] [0.1318 0.1623 0.194  0.2373 1.2717 2.154  4.7954]
1 [0.3913 1.7931 1.9056 2.004  2.1051 2.2776 3.3711] [0.1986 1.7341 1.8466 1.9219 2.0072 2.1576 2.6756]
2 [1.7221 1.9046 1.9707 2.0103 2.0551 2.1253 2.4825] [1.5505 1.7804 1.8885 1.9574 2.0251 2.1289 2.4465]
3 [1.7763 1.8813 1.9381 1.9761 2.014  2.0986 3.1298] [1.4454 1.735  1.9208 2.0615 2.2221 2.491  4.8197]

LAST MLP OUTPUT-COLUMN GEOMETRY
nearest cosine percentiles: [0.459  0.5382 0.5696 0.605  0.6485 0.7103 0.9073 0.9462]
k 20 inertia 1139.135
cluster sizes [107  96  89  88  87  85  84  83  81  78  74  74  73  71  69  68  66  64  52  47]
centroid norms [0.7   0.475 0.529 0.49  0.448 0.475 0.5   0.508 0.465 0.497 0.502 0.469 0.499 0.491 0.481 0.491 0.505 0.471 0.481 0.57 ]
k 24 inertia 1117.005
cluster sizes [93 79 77 74 73 72 70 67 66 66 66 66 65 58 58 58 57 57 57 56 54 51 48 48]
centroid norms [0.6   0.726 0.511 0.435 0.501 0.508 0.458 0.507 0.485 0.528 0.5   0.564 0.485 0.506 0.488 0.5   0.507 0.477 0.486 0.513 0.523 0.517 0.519 0.54 ]
k 28 inertia 1095.936
cluster sizes [78 76 73 68 63 63 62 61 59 58 58 55 55 55 53 52 51 50 49 48 48 47 46 44 43 43 40 38]
centroid norms [0.725 0.653 0.504 0.506 0.511 0.505 0.533 0.588 0.479 0.508 0.488 0.496 0.5   0.524 0.493 0.548 0.456 0.556 0.499 0.517 0.532 0.522 0.492 0.508 0.5   0.502 0.522 0.602]
k 32 inertia 1077.692
cluster sizes [74 61 61 59 57 57 55 52 52 50 48 48 48 48 47 46 46 46 44 44 43 43 43 42 42 42 41 41 41 41 38 36]
centroid norms [0.72  0.481 0.499 0.487 0.545 0.698 0.532 0.518 0.514 0.542 0.503 0.54  0.55  0.566 0.532 0.553 0.537 0.502 0.513 0.56  0.543 0.512 0.489 0.49  0.53  0.519 0.557 0.5   0.662
 0.498 0.517 0.57 ]

INDEPENDENT-COMPONENT DECOMPOSITION OF OUTPUT WEIGHT COLUMNS
components 24 iterations 660
excess kurtoses [ 4.353  1.607  1.307  1.306  1.246  1.156  1.15   1.028  1.002  0.996  0.884  0.875  0.83   0.809  0.798 -0.443 -0.46  -0.493 -0.554 -0.576 -0.583 -0.591 -0.62  -0.673]
absolute skewness [0.368 0.464 0.003 0.307 0.177 0.08  0.234 0.22  0.08  0.157 0.179 0.084 0.3   0.13  0.094 0.141 0.069 0.055 0.023 0.086 0.01  0.006 0.053 0.139]
components 28 iterations 1416
excess kurtoses [37.202  4.452  1.572  1.357  1.232  1.232  1.154  1.153  1.131  1.131  1.083  1.049  0.934  0.925  0.876  0.854  0.795  0.789  0.779  0.745 -0.517 -0.566 -0.576 -0.58  -0.587
 -0.605 -0.626 -0.702]
absolute skewness [0.607 0.415 0.422 0.155 0.142 0.278 0.443 0.171 0.027 0.256 0.128 0.283 0.124 0.174 0.107 0.012 0.    0.076 0.211 0.181 0.074 0.119 0.085 0.056 0.121 0.082 0.017 0.057]

INDUCTION-CANDIDATE MATRIX EIGENVALUES
eigenvalues descending magnitude [ 0.0505+0.0128j  0.0505-0.0128j  0.0423+0.j      0.0356+0.0108j  0.0356-0.0108j  0.0321+0.j      0.0306+0.0061j  0.0306-0.0061j  0.0269+0.0028j  0.0269-0.0028j  0.0151+0.0061j
  0.0151-0.0061j  0.0139+0.j      0.0117+0.j      0.0056+0.0101j  0.0056-0.0101j  0.0085+0.j      0.0019+0.005j   0.0019-0.005j   0.0037+0.0019j  0.0037-0.0019j -0.0019+0.0034j
 -0.0019-0.0034j  0.0034+0.j     -0.0022+0.0025j -0.0022-0.0025j  0.0019+0.0022j  0.0019-0.0022j -0.0025+0.0002j -0.0025-0.0002j -0.0021+0.j     -0.0015-0.0006j -0.0015+0.0006j
  0.0008+0.0012j  0.0008-0.0012j  0.0009+0.j     -0.0003+0.0007j -0.0003-0.0007j  0.0006+0.0002j  0.0006-0.0002j  0.0006+0.j      0.0002+0.0005j  0.0002-0.0005j  0.0003+0.0003j
  0.0003-0.0003j -0.0002+0.0004j -0.0002-0.0004j -0.0003+0.j      0.    -0.0002j  0.    +0.0002j -0.0002+0.j      0.0001+0.j      0.0001-0.j     -0.0001+0.j     -0.0001-0.j
  0.0001+0.j     -0.    +0.0001j -0.    -0.0001j  0.0001+0.j      0.0001-0.j      0.    +0.j     -0.    +0.j     -0.    +0.j     -0.    +0.j    ]
symmetric-part eigenvalues descending [ 0.0673  0.0527  0.0511  0.0468  0.0452  0.0421  0.0364  0.0329  0.0319  0.0281  0.0273  0.0188  0.0175  0.0171  0.0125  0.0103  0.0077  0.0063  0.0058  0.0044  0.0042  0.0027
  0.0024  0.0021  0.0014  0.0012  0.001   0.001   0.0008  0.0004  0.0003  0.0002  0.0001  0.0001 -0.0002 -0.0003 -0.0003 -0.0005 -0.0006 -0.0007 -0.0009 -0.0012 -0.0014 -0.0017
 -0.0018 -0.002  -0.0024 -0.0025 -0.0028 -0.0029 -0.0036 -0.0041 -0.0046 -0.005  -0.0052 -0.0059 -0.0073 -0.0084 -0.0086 -0.0098 -0.0103 -0.0126 -0.0142 -0.0173]

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4,suppress=True,linewidth=180)

def svd(a):
    return la.svd(a,full_matrices=False,lapack_driver="gesvd")
def orth(a):
    u,s,_=svd(a)
    return u[:,s>s[0]*1e-8]

with threadpool_limits(limits=2):
    F=np.asarray(W["W_fc.0"],dtype=float)*W["ln2_w.0"]
    P=np.asarray(W["W_proj.0"],dtype=float)
    fn=la.norm(W["W_fc.0"],axis=1)
    pn=la.norm(P,axis=0)
    active=(fn>0.7)&(pn>0.6)
    print("L0 MLP substantial-weight neurons:",int(active.sum()),"of",len(active))
    print("Input-norm/output-norm correlation:",np.corrcoef(fn,pn)[0,1])
    g=np.asarray(W["ln1_w.0"],dtype=float)
    pos_weights=np.vstack([np.asarray(W["W_q.0"][128:192],dtype=float)*g,
                           np.asarray(W["W_k.0"][128:192],dtype=float)*g])
    _,_,vp=svd(pos_weights)
    Bs=[]
    for j in range(6):
        sl=slice(j*64,j*64+64)
        b=(np.asarray(W["W_o.0"][:,sl],dtype=float)@np.asarray(W["W_v.0"][sl],dtype=float))*g
        Bs.append(b)
    broad=svd(np.hstack([Bs[j] for j in (0,1,4,5)]))[0][:,:4]
    other=svd(Bs[3])[0][:,:8]
    prev=svd(Bs[2])[0]
    V=np.asarray(W["W_v.0"][128:192],dtype=float)*g

    print("\nRESIDUAL ROW SPECTRA AFTER REMOVING WEIGHT-DERIVED POSITION/WRITE DIRECTIONS")
    print("No token identities are assumed; these are orthogonal matrix projections.")
    for rp in (32,64,96):
        for rv in (24,48):
            N=orth(np.hstack([vp[:rp].T,broad,other,prev[:,:rv]]))
            Fc=F[active]-(F[active]@N)@N.T
            Vc=V-(V@N)@N.T
            sf=svd(Fc)[1]
            sv=svd(Vc)[1]
            print(f"posrank={rp}, prevrank={rv}, removedrank={N.shape[1]}")
            print("F spectrum",sf[:45])
            print("V spectrum",sv[:40])
            _,_,tf=svd(Fc)
            _,_,tv=svd(Vc)
            cc=la.svdvals(tf[:28]@tv[:28].T)
            print("principal cosines between top-28 current/previous read spaces",cc)

    print("\nACTIVE MLP ROW SIMILARITIES")
    A=F[active]/la.norm(F[active],axis=1)[:,None]
    G=A@A.T
    np.fill_diagonal(G,-2)
    print("nearest cosine percentiles",np.percentile(G.max(axis=1),[0,10,25,50,75,90,99,100]))
```
Permitted experiment harness: python — full request and result
{
  "run": 8,
  "turn": "t008",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4,suppress=True,linewidth=180)\n\ndef svd(a):\n    return la.svd(a,full_matrices=False,lapack_driver=\"gesvd\")\ndef orth(a):\n    u,s,_=svd(a)\n    return u[:,s>s[0]*1e-8]\n\nwith threadpool_limits(limits=2):\n    F=np.asarray(W[\"W_fc.0\"],dtype=float)*W[\"ln2_w.0\"]\n    P=np.asarray(W[\"W_proj.0\"],dtype=float)\n    fn=la.norm(W[\"W_fc.0\"],axis=1)\n    pn=la.norm(P,axis=0)\n    active=(fn>0.7)&(pn>0.6)\n    print(\"L0 MLP substantial-weight neurons:\",int(active.sum()),\"of\",len(active))\n    print(\"Input-norm/output-norm correlation:\",np.corrcoef(fn,pn)[0,1])\n    g=np.asarray(W[\"ln1_w.0\"],dtype=float)\n    pos_weights=np.vstack([np.asarray(W[\"W_q.0\"][128:192],dtype=float)*g,\n                           np.asarray(W[\"W_k.0\"][128:192],dtype=float)*g])\n    _,_,vp=svd(pos_weights)\n    Bs=[]\n    for j in range(6):\n        sl=slice(j*64,j*64+64)\n        b=(np.asarray(W[\"W_o.0\"][:,sl],dtype=float)@np.asarray(W[\"W_v.0\"][sl],dtype=float))*g\n        Bs.append(b)\n    broad=svd(np.hstack([Bs[j] for j in (0,1,4,5)]))[0][:,:4]\n    other=svd(Bs[3])[0][:,:8]\n    prev=svd(Bs[2])[0]\n    V=np.asarray(W[\"W_v.0\"][128:192],dtype=float)*g\n\n    print(\"\\nRESIDUAL ROW SPECTRA AFTER REMOVING WEIGHT-DERIVED POSITION/WRITE DIRECTIONS\")\n    print(\"No token identities are assumed; these are orthogonal matrix projections.\")\n    for rp in (32,64,96):\n        for rv in (24,48):\n            N=orth(np.hstack([vp[:rp].T,broad,other,prev[:,:rv]]))\n            Fc=F[active]-(F[active]@N)@N.T\n            Vc=V-(V@N)@N.T\n            sf=svd(Fc)[1]\n            sv=svd(Vc)[1]\n            print(f\"posrank={rp}, prevrank={rv}, removedrank={N.shape[1]}\")\n            print(\"F spectrum\",sf[:45])\n            print(\"V spectrum\",sv[:40])\n            _,_,tf=svd(Fc)\n            _,_,tv=svd(Vc)\n            cc=la.svdvals(tf[:28]@tv[:28].T)\n            print(\"principal cosines between top-28 current/previous read spaces\",cc)\n\n    print(\"\\nACTIVE MLP ROW SIMILARITIES\")\n    A=F[active]/la.norm(F[active],axis=1)[:,None]\n    G=A@A.T\n    np.fill_diagonal(G,-2)\n    print(\"nearest cosine percentiles\",np.percentile(G.max(axis=1),[0,10,25,50,75,90,99,100]))",
  "out": "L0 MLP substantial-weight neurons: 413 of 1536\nInput-norm/output-norm correlation: 0.9611175807816661\n\nRESIDUAL ROW SPECTRA AFTER REMOVING WEIGHT-DERIVED POSITION/WRITE DIRECTIONS\nNo token identities are assumed; these are orthogonal matrix projections.\nposrank=32, prevrank=24, removedrank=68\nF spectrum [16.3714  7.9638  6.8664  6.7152  6.4413  6.3209  6.028   5.9496  5.8348  5.8002  5.7713  5.6446  5.6111  5.3835  5.2814  5.1987  5.1542  5.0809  4.9779  4.8831  4.8232  4.7045\n  4.6009  4.5693  4.4541  4.4085  4.262   4.1424  4.0963  3.902   3.5697  3.2372  3.1985  3.0433  2.876   2.8055  2.7239  2.6233  2.5241  2.4904  2.4431  2.3608  2.3567  2.2086\n  2.2035]\nV spectrum [0.3836 0.3595 0.3338 0.3319 0.3126 0.3006 0.2958 0.2921 0.2725 0.2642 0.2523 0.2444 0.2403 0.2318 0.2297 0.2243 0.2218 0.2108 0.1949 0.1884 0.1822 0.169  0.153  0.146  0.1333\n 0.1322 0.124  0.1199 0.1104 0.1041 0.0989 0.0971 0.0936 0.0933 0.0887 0.0857 0.0809 0.0786 0.0765 0.0749]\nprincipal cosines between top-28 current/previous read spaces [0.8771 0.856  0.8377 0.8162 0.7984 0.7697 0.7268 0.7024 0.6794 0.6701 0.6304 0.6181 0.6151 0.5672 0.5538 0.5375 0.5089 0.4883 0.4788 0.4622 0.4262 0.3974 0.3261 0.2615 0.2417\n 0.161  0.0846 0.0233]\nposrank=32, prevrank=48, removedrank=92\nF spectrum [15.4008  7.7148  6.7676  6.617   6.3349  6.2195  5.8863  5.8239  5.7411  5.7079  5.5929  5.54    5.4776  5.261   5.1876  5.0951  5.0629  4.9592  4.8608  4.8059  4.6825  4.6327\n  4.5339  4.5019  4.3896  4.3077  4.134   4.0281  3.9536  3.7022  3.3755  3.1422  3.0929  2.9269  2.7923  2.7097  2.6476  2.5242  2.4285  2.3966  2.3505  2.2621  2.2158  2.1621\n  2.1277]\nV spectrum [0.3683 0.3509 0.3223 0.3176 0.3063 0.2913 0.2897 0.285  0.2645 0.2563 0.2447 0.2363 0.2335 0.2281 0.2252 0.2156 0.2105 0.2027 0.1893 0.1819 0.1728 0.1622 0.1495 0.1339 0.1283\n 0.1227 0.1184 0.1129 0.1042 0.0982 0.0947 0.0942 0.0885 0.0871 0.083  0.0818 0.0785 0.0752 0.0731 0.0722]\nprincipal cosines between top-28 current/previous read spaces [0.8828 0.8664 0.8586 0.844  0.822  0.8028 0.7263 0.7115 0.7021 0.6861 0.6462 0.6335 0.632  0.595  0.5797 0.5516 0.5302 0.5189 0.489  0.4704 0.4328 0.3587 0.331  0.2785 0.2601\n 0.1578 0.1233 0.0035]\nposrank=64, prevrank=24, removedrank=100\nF spectrum [15.871   7.2694  6.6665  6.3799  6.2806  6.0542  5.8796  5.7198  5.6865  5.6124  5.5581  5.4351  5.3719  5.2527  5.0907  5.0147  4.9406  4.8771  4.8232  4.6524  4.5273  4.4885\n  4.4377  4.3689  4.2772  4.2158  4.0526  3.9587  3.9402  3.7967  3.368   3.1353  3.0849  2.9057  2.8065  2.7211  2.637   2.5304  2.4707  2.4268  2.3826  2.2814  2.2634  2.14\n  2.1173]\nV spectrum [0.2999 0.2904 0.279  0.2628 0.2599 0.2574 0.2475 0.2336 0.2238 0.2154 0.2117 0.2012 0.197  0.193  0.1835 0.1771 0.1651 0.1618 0.1547 0.1379 0.1331 0.1213 0.1132 0.1067 0.1002\n 0.0971 0.0879 0.0853 0.084  0.0799 0.0757 0.0734 0.0709 0.0682 0.0651 0.0632 0.0625 0.0609 0.0583 0.0567]\nprincipal cosines between top-28 current/previous read spaces [0.8842 0.8702 0.8551 0.8415 0.8176 0.8059 0.7733 0.747  0.722  0.6946 0.6853 0.6685 0.6224 0.6031 0.5797 0.5595 0.5254 0.5095 0.4807 0.4205 0.3941 0.3695 0.3378 0.2715 0.2158\n 0.1279 0.0546 0.006 ]\nposrank=64, prevrank=48, removedrank=124\nF spectrum [14.9706  7.0356  6.5165  6.2766  6.1428  5.9266  5.6821  5.6168  5.5929  5.4824  5.4387  5.3339  5.2119  5.0944  4.9521  4.92    4.7925  4.7731  4.6524  4.5596  4.4249  4.3942\n  4.3302  4.2489  4.1988  4.0836  3.924   3.8409  3.7556  3.5791  3.1291  3.015   2.9696  2.7675  2.7085  2.6021  2.5389  2.4706  2.3548  2.3205  2.2831  2.1965  2.1151  2.0735\n  2.0075]\nV spectrum [0.2883 0.2832 0.2742 0.2561 0.2536 0.2508 0.24   0.2217 0.216  0.2097 0.2054 0.1951 0.1897 0.1849 0.1755 0.1692 0.1589 0.1571 0.148  0.1281 0.1257 0.1177 0.1072 0.1021 0.0938\n 0.0927 0.0843 0.0814 0.077  0.0748 0.0716 0.0703 0.0686 0.0652 0.0628 0.0601 0.0585 0.0571 0.0548 0.0542]\nprincipal cosines between top-28 current/previous read spaces [0.9016 0.8812 0.8691 0.8579 0.8427 0.8244 0.7943 0.7345 0.7302 0.7189 0.6976 0.6719 0.6616 0.6509 0.6231 0.6023 0.5703 0.5206 0.4964 0.4638 0.4083 0.4042 0.3339 0.27   0.2314\n 0.1246 0.084  0.0333]\nposrank=96, prevrank=24, removedrank=132\nF spectrum [14.8055  6.826   6.3299  6.1388  5.9858  5.7719  5.6631  5.5562  5.3892  5.3184  5.2628  5.1207  5.0086  4.7875  4.7518  4.7057  4.6312  4.4776  4.4652  4.3595  4.2894  4.1896\n  4.1356  3.9811  3.9365  3.7805  3.7046  3.5839  3.4785  3.2848  3.2313  2.9999  2.9346  2.7326  2.6832  2.5544  2.533   2.4139  2.3671  2.3314  2.2256  2.168   2.1232  2.0475\n  1.9686]\nV spectrum [0.2584 0.242  0.2287 0.2256 0.2127 0.1968 0.1962 0.1867 0.1796 0.1631 0.1601 0.157  0.1481 0.14   0.1292 0.1288 0.1225 0.1204 0.1175 0.1063 0.1    0.0935 0.0848 0.0837 0.0767\n 0.07   0.0686 0.0652 0.0641 0.0621 0.0589 0.0567 0.0554 0.0529 0.052  0.05   0.0478 0.0473 0.0464 0.0446]\nprincipal cosines between top-28 current/previous read spaces [0.883  0.8738 0.8613 0.8386 0.8221 0.7967 0.7613 0.7466 0.726  0.7033 0.6973 0.6766 0.6626 0.648  0.6148 0.5619 0.5357 0.5311 0.5154 0.4374 0.3936 0.3817 0.2598 0.2423 0.1638\n 0.1392 0.0528 0.0097]\nposrank=96, prevrank=48, removedrank=156\nF spectrum [14.0375  6.5446  6.1611  6.0005  5.7795  5.6147  5.4951  5.3895  5.2956  5.2007  5.0815  4.9679  4.8584  4.6647  4.6275  4.5614  4.5146  4.3645  4.3208  4.2658  4.1048  4.0263\n  3.8846  3.7689  3.7176  3.6343  3.5805  3.4231  3.2931  3.18    2.9733  2.8699  2.8203  2.5631  2.5283  2.4171  2.3994  2.3314  2.2341  2.2096  2.1435  2.0514  2.0267  1.9361\n  1.8627]\nV spectrum [0.246  0.2378 0.2226 0.2173 0.203  0.1922 0.1859 0.1823 0.1728 0.158  0.1527 0.1481 0.1405 0.1319 0.1256 0.1237 0.1186 0.1135 0.1103 0.0993 0.0917 0.0885 0.0811 0.0788 0.0744\n 0.0672 0.0649 0.0622 0.0606 0.0587 0.056  0.0539 0.0521 0.0488 0.048  0.0472 0.0459 0.0446 0.0432 0.0429]\nprincipal cosines between top-28 current/previous read spaces [0.9039 0.8947 0.8687 0.8536 0.84   0.8027 0.7746 0.7595 0.744  0.7321 0.7153 0.6996 0.6834 0.6692 0.6214 0.6013 0.5761 0.5362 0.5224 0.4663 0.4228 0.4174 0.2799 0.2083 0.1889\n 0.1617 0.076  0.0716]\n\nACTIVE MLP ROW SIMILARITIES\nnearest cosine percentiles [0.3449 0.5168 0.615  0.7556 0.9388 0.9725 0.9821 0.9873]\n",
  "seconds": 1.518,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "a90e05c656436e0a504eb5f40a7aaf31936d488df1aeab69f7734ca125b19543",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
L0 MLP substantial-weight neurons: 413 of 1536
Input-norm/output-norm correlation: 0.9611175807816661

RESIDUAL ROW SPECTRA AFTER REMOVING WEIGHT-DERIVED POSITION/WRITE DIRECTIONS
No token identities are assumed; these are orthogonal matrix projections.
posrank=32, prevrank=24, removedrank=68
F spectrum [16.3714  7.9638  6.8664  6.7152  6.4413  6.3209  6.028   5.9496  5.8348  5.8002  5.7713  5.6446  5.6111  5.3835  5.2814  5.1987  5.1542  5.0809  4.9779  4.8831  4.8232  4.7045
  4.6009  4.5693  4.4541  4.4085  4.262   4.1424  4.0963  3.902   3.5697  3.2372  3.1985  3.0433  2.876   2.8055  2.7239  2.6233  2.5241  2.4904  2.4431  2.3608  2.3567  2.2086
  2.2035]
V spectrum [0.3836 0.3595 0.3338 0.3319 0.3126 0.3006 0.2958 0.2921 0.2725 0.2642 0.2523 0.2444 0.2403 0.2318 0.2297 0.2243 0.2218 0.2108 0.1949 0.1884 0.1822 0.169  0.153  0.146  0.1333
 0.1322 0.124  0.1199 0.1104 0.1041 0.0989 0.0971 0.0936 0.0933 0.0887 0.0857 0.0809 0.0786 0.0765 0.0749]
principal cosines between top-28 current/previous read spaces [0.8771 0.856  0.8377 0.8162 0.7984 0.7697 0.7268 0.7024 0.6794 0.6701 0.6304 0.6181 0.6151 0.5672 0.5538 0.5375 0.5089 0.4883 0.4788 0.4622 0.4262 0.3974 0.3261 0.2615 0.2417
 0.161  0.0846 0.0233]
posrank=32, prevrank=48, removedrank=92
F spectrum [15.4008  7.7148  6.7676  6.617   6.3349  6.2195  5.8863  5.8239  5.7411  5.7079  5.5929  5.54    5.4776  5.261   5.1876  5.0951  5.0629  4.9592  4.8608  4.8059  4.6825  4.6327
  4.5339  4.5019  4.3896  4.3077  4.134   4.0281  3.9536  3.7022  3.3755  3.1422  3.0929  2.9269  2.7923  2.7097  2.6476  2.5242  2.4285  2.3966  2.3505  2.2621  2.2158  2.1621
  2.1277]
V spectrum [0.3683 0.3509 0.3223 0.3176 0.3063 0.2913 0.2897 0.285  0.2645 0.2563 0.2447 0.2363 0.2335 0.2281 0.2252 0.2156 0.2105 0.2027 0.1893 0.1819 0.1728 0.1622 0.1495 0.1339 0.1283
 0.1227 0.1184 0.1129 0.1042 0.0982 0.0947 0.0942 0.0885 0.0871 0.083  0.0818 0.0785 0.0752 0.0731 0.0722]
principal cosines between top-28 current/previous read spaces [0.8828 0.8664 0.8586 0.844  0.822  0.8028 0.7263 0.7115 0.7021 0.6861 0.6462 0.6335 0.632  0.595  0.5797 0.5516 0.5302 0.5189 0.489  0.4704 0.4328 0.3587 0.331  0.2785 0.2601
 0.1578 0.1233 0.0035]
posrank=64, prevrank=24, removedrank=100
F spectrum [15.871   7.2694  6.6665  6.3799  6.2806  6.0542  5.8796  5.7198  5.6865  5.6124  5.5581  5.4351  5.3719  5.2527  5.0907  5.0147  4.9406  4.8771  4.8232  4.6524  4.5273  4.4885
  4.4377  4.3689  4.2772  4.2158  4.0526  3.9587  3.9402  3.7967  3.368   3.1353  3.0849  2.9057  2.8065  2.7211  2.637   2.5304  2.4707  2.4268  2.3826  2.2814  2.2634  2.14
  2.1173]
V spectrum [0.2999 0.2904 0.279  0.2628 0.2599 0.2574 0.2475 0.2336 0.2238 0.2154 0.2117 0.2012 0.197  0.193  0.1835 0.1771 0.1651 0.1618 0.1547 0.1379 0.1331 0.1213 0.1132 0.1067 0.1002
 0.0971 0.0879 0.0853 0.084  0.0799 0.0757 0.0734 0.0709 0.0682 0.0651 0.0632 0.0625 0.0609 0.0583 0.0567]
principal cosines between top-28 current/previous read spaces [0.8842 0.8702 0.8551 0.8415 0.8176 0.8059 0.7733 0.747  0.722  0.6946 0.6853 0.6685 0.6224 0.6031 0.5797 0.5595 0.5254 0.5095 0.4807 0.4205 0.3941 0.3695 0.3378 0.2715 0.2158
 0.1279 0.0546 0.006 ]
posrank=64, prevrank=48, removedrank=124
F spectrum [14.9706  7.0356  6.5165  6.2766  6.1428  5.9266  5.6821  5.6168  5.5929  5.4824  5.4387  5.3339  5.2119  5.0944  4.9521  4.92    4.7925  4.7731  4.6524  4.5596  4.4249  4.3942
  4.3302  4.2489  4.1988  4.0836  3.924   3.8409  3.7556  3.5791  3.1291  3.015   2.9696  2.7675  2.7085  2.6021  2.5389  2.4706  2.3548  2.3205  2.2831  2.1965  2.1151  2.0735
  2.0075]
V spectrum [0.2883 0.2832 0.2742 0.2561 0.2536 0.2508 0.24   0.2217 0.216  0.2097 0.2054 0.1951 0.1897 0.1849 0.1755 0.1692 0.1589 0.1571 0.148  0.1281 0.1257 0.1177 0.1072 0.1021 0.0938
 0.0927 0.0843 0.0814 0.077  0.0748 0.0716 0.0703 0.0686 0.0652 0.0628 0.0601 0.0585 0.0571 0.0548 0.0542]
principal cosines between top-28 current/previous read spaces [0.9016 0.8812 0.8691 0.8579 0.8427 0.8244 0.7943 0.7345 0.7302 0.7189 0.6976 0.6719 0.6616 0.6509 0.6231 0.6023 0.5703 0.5206 0.4964 0.4638 0.4083 0.4042 0.3339 0.27   0.2314
 0.1246 0.084  0.0333]
posrank=96, prevrank=24, removedrank=132
F spectrum [14.8055  6.826   6.3299  6.1388  5.9858  5.7719  5.6631  5.5562  5.3892  5.3184  5.2628  5.1207  5.0086  4.7875  4.7518  4.7057  4.6312  4.4776  4.4652  4.3595  4.2894  4.1896
  4.1356  3.9811  3.9365  3.7805  3.7046  3.5839  3.4785  3.2848  3.2313  2.9999  2.9346  2.7326  2.6832  2.5544  2.533   2.4139  2.3671  2.3314  2.2256  2.168   2.1232  2.0475
  1.9686]
V spectrum [0.2584 0.242  0.2287 0.2256 0.2127 0.1968 0.1962 0.1867 0.1796 0.1631 0.1601 0.157  0.1481 0.14   0.1292 0.1288 0.1225 0.1204 0.1175 0.1063 0.1    0.0935 0.0848 0.0837 0.0767
 0.07   0.0686 0.0652 0.0641 0.0621 0.0589 0.0567 0.0554 0.0529 0.052  0.05   0.0478 0.0473 0.0464 0.0446]
principal cosines between top-28 current/previous read spaces [0.883  0.8738 0.8613 0.8386 0.8221 0.7967 0.7613 0.7466 0.726  0.7033 0.6973 0.6766 0.6626 0.648  0.6148 0.5619 0.5357 0.5311 0.5154 0.4374 0.3936 0.3817 0.2598 0.2423 0.1638
 0.1392 0.0528 0.0097]
posrank=96, prevrank=48, removedrank=156
F spectrum [14.0375  6.5446  6.1611  6.0005  5.7795  5.6147  5.4951  5.3895  5.2956  5.2007  5.0815  4.9679  4.8584  4.6647  4.6275  4.5614  4.5146  4.3645  4.3208  4.2658  4.1048  4.0263
  3.8846  3.7689  3.7176  3.6343  3.5805  3.4231  3.2931  3.18    2.9733  2.8699  2.8203  2.5631  2.5283  2.4171  2.3994  2.3314  2.2341  2.2096  2.1435  2.0514  2.0267  1.9361
  1.8627]
V spectrum [0.246  0.2378 0.2226 0.2173 0.203  0.1922 0.1859 0.1823 0.1728 0.158  0.1527 0.1481 0.1405 0.1319 0.1256 0.1237 0.1186 0.1135 0.1103 0.0993 0.0917 0.0885 0.0811 0.0788 0.0744
 0.0672 0.0649 0.0622 0.0606 0.0587 0.056  0.0539 0.0521 0.0488 0.048  0.0472 0.0459 0.0446 0.0432 0.0429]
principal cosines between top-28 current/previous read spaces [0.9039 0.8947 0.8687 0.8536 0.84   0.8027 0.7746 0.7595 0.744  0.7321 0.7153 0.6996 0.6834 0.6692 0.6214 0.6013 0.5761 0.5362 0.5224 0.4663 0.4228 0.4174 0.2799 0.2083 0.1889
 0.1617 0.076  0.0716]

ACTIVE MLP ROW SIMILARITIES
nearest cosine percentiles [0.3449 0.5168 0.615  0.7556 0.9388 0.9725 0.9821 0.9873]
t009 · harness: python

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 6,230 characters
OUTPUT of run 8 of at most 100:
```
L0 MLP substantial-weight neurons: 413 of 1536
Input-norm/output-norm correlation: 0.9611175807816661

RESIDUAL ROW SPECTRA AFTER REMOVING WEIGHT-DERIVED POSITION/WRITE DIRECTIONS
No token identities are assumed; these are orthogonal matrix projections.
posrank=32, prevrank=24, removedrank=68
F spectrum [16.3714  7.9638  6.8664  6.7152  6.4413  6.3209  6.028   5.9496  5.8348  5.8002  5.7713  5.6446  5.6111  5.3835  5.2814  5.1987  5.1542  5.0809  4.9779  4.8831  4.8232  4.7045
  4.6009  4.5693  4.4541  4.4085  4.262   4.1424  4.0963  3.902   3.5697  3.2372  3.1985  3.0433  2.876   2.8055  2.7239  2.6233  2.5241  2.4904  2.4431  2.3608  2.3567  2.2086
  2.2035]
V spectrum [0.3836 0.3595 0.3338 0.3319 0.3126 0.3006 0.2958 0.2921 0.2725 0.2642 0.2523 0.2444 0.2403 0.2318 0.2297 0.2243 0.2218 0.2108 0.1949 0.1884 0.1822 0.169  0.153  0.146  0.1333
 0.1322 0.124  0.1199 0.1104 0.1041 0.0989 0.0971 0.0936 0.0933 0.0887 0.0857 0.0809 0.0786 0.0765 0.0749]
principal cosines between top-28 current/previous read spaces [0.8771 0.856  0.8377 0.8162 0.7984 0.7697 0.7268 0.7024 0.6794 0.6701 0.6304 0.6181 0.6151 0.5672 0.5538 0.5375 0.5089 0.4883 0.4788 0.4622 0.4262 0.3974 0.3261 0.2615 0.2417
 0.161  0.0846 0.0233]
posrank=32, prevrank=48, removedrank=92
F spectrum [15.4008  7.7148  6.7676  6.617   6.3349  6.2195  5.8863  5.8239  5.7411  5.7079  5.5929  5.54    5.4776  5.261   5.1876  5.0951  5.0629  4.9592  4.8608  4.8059  4.6825  4.6327
  4.5339  4.5019  4.3896  4.3077  4.134   4.0281  3.9536  3.7022  3.3755  3.1422  3.0929  2.9269  2.7923  2.7097  2.6476  2.5242  2.4285  2.3966  2.3505  2.2621  2.2158  2.1621
  2.1277]
V spectrum [0.3683 0.3509 0.3223 0.3176 0.3063 0.2913 0.2897 0.285  0.2645 0.2563 0.2447 0.2363 0.2335 0.2281 0.2252 0.2156 0.2105 0.2027 0.1893 0.1819 0.1728 0.1622 0.1495 0.1339 0.1283
 0.1227 0.1184 0.1129 0.1042 0.0982 0.0947 0.0942 0.0885 0.0871 0.083  0.0818 0.0785 0.0752 0.0731 0.0722]
principal cosines between top-28 current/previous read spaces [0.8828 0.8664 0.8586 0.844  0.822  0.8028 0.7263 0.7115 0.7021 0.6861 0.6462 0.6335 0.632  0.595  0.5797 0.5516 0.5302 0.5189 0.489  0.4704 0.4328 0.3587 0.331  0.2785 0.2601
 0.1578 0.1233 0.0035]
posrank=64, prevrank=24, removedrank=100
F spectrum [15.871   7.2694  6.6665  6.3799  6.2806  6.0542  5.8796  5.7198  5.6865  5.6124  5.5581  5.4351  5.3719  5.2527  5.0907  5.0147  4.9406  4.8771  4.8232  4.6524  4.5273  4.4885
  4.4377  4.3689  4.2772  4.2158  4.0526  3.9587  3.9402  3.7967  3.368   3.1353  3.0849  2.9057  2.8065  2.7211  2.637   2.5304  2.4707  2.4268  2.3826  2.2814  2.2634  2.14
  2.1173]
V spectrum [0.2999 0.2904 0.279  0.2628 0.2599 0.2574 0.2475 0.2336 0.2238 0.2154 0.2117 0.2012 0.197  0.193  0.1835 0.1771 0.1651 0.1618 0.1547 0.1379 0.1331 0.1213 0.1132 0.1067 0.1002
 0.0971 0.0879 0.0853 0.084  0.0799 0.0757 0.0734 0.0709 0.0682 0.0651 0.0632 0.0625 0.0609 0.0583 0.0567]
principal cosines between top-28 current/previous read spaces [0.8842 0.8702 0.8551 0.8415 0.8176 0.8059 0.7733 0.747  0.722  0.6946 0.6853 0.6685 0.6224 0.6031 0.5797 0.5595 0.5254 0.5095 0.4807 0.4205 0.3941 0.3695 0.3378 0.2715 0.2158
 0.1279 0.0546 0.006 ]
posrank=64, prevrank=48, removedrank=124
F spectrum [14.9706  7.0356  6.5165  6.2766  6.1428  5.9266  5.6821  5.6168  5.5929  5.4824  5.4387  5.3339  5.2119  5.0944  4.9521  4.92    4.7925  4.7731  4.6524  4.5596  4.4249  4.3942
  4.3302  4.2489  4.1988  4.0836  3.924   3.8409  3.7556  3.5791  3.1291  3.015   2.9696  2.7675  2.7085  2.6021  2.5389  2.4706  2.3548  2.3205  2.2831  2.1965  2.1151  2.0735
  2.0075]
V spectrum [0.2883 0.2832 0.2742 0.2561 0.2536 0.2508 0.24   0.2217 0.216  0.2097 0.2054 0.1951 0.1897 0.1849 0.1755 0.1692 0.1589 0.1571 0.148  0.1281 0.1257 0.1177 0.1072 0.1021 0.0938
 0.0927 0.0843 0.0814 0.077  0.0748 0.0716 0.0703 0.0686 0.0652 0.0628 0.0601 0.0585 0.0571 0.0548 0.0542]
principal cosines between top-28 current/previous read spaces [0.9016 0.8812 0.8691 0.8579 0.8427 0.8244 0.7943 0.7345 0.7302 0.7189 0.6976 0.6719 0.6616 0.6509 0.6231 0.6023 0.5703 0.5206 0.4964 0.4638 0.4083 0.4042 0.3339 0.27   0.2314
 0.1246 0.084  0.0333]
posrank=96, prevrank=24, removedrank=132
F spectrum [14.8055  6.826   6.3299  6.1388  5.9858  5.7719  5.6631  5.5562  5.3892  5.3184  5.2628  5.1207  5.0086  4.7875  4.7518  4.7057  4.6312  4.4776  4.4652  4.3595  4.2894  4.1896
  4.1356  3.9811  3.9365  3.7805  3.7046  3.5839  3.4785  3.2848  3.2313  2.9999  2.9346  2.7326  2.6832  2.5544  2.533   2.4139  2.3671  2.3314  2.2256  2.168   2.1232  2.0475
  1.9686]
V spectrum [0.2584 0.242  0.2287 0.2256 0.2127 0.1968 0.1962 0.1867 0.1796 0.1631 0.1601 0.157  0.1481 0.14   0.1292 0.1288 0.1225 0.1204 0.1175 0.1063 0.1    0.0935 0.0848 0.0837 0.0767
 0.07   0.0686 0.0652 0.0641 0.0621 0.0589 0.0567 0.0554 0.0529 0.052  0.05   0.0478 0.0473 0.0464 0.0446]
principal cosines between top-28 current/previous read spaces [0.883  0.8738 0.8613 0.8386 0.8221 0.7967 0.7613 0.7466 0.726  0.7033 0.6973 0.6766 0.6626 0.648  0.6148 0.5619 0.5357 0.5311 0.5154 0.4374 0.3936 0.3817 0.2598 0.2423 0.1638
 0.1392 0.0528 0.0097]
posrank=96, prevrank=48, removedrank=156
F spectrum [14.0375  6.5446  6.1611  6.0005  5.7795  5.6147  5.4951  5.3895  5.2956  5.2007  5.0815  4.9679  4.8584  4.6647  4.6275  4.5614  4.5146  4.3645  4.3208  4.2658  4.1048  4.0263
  3.8846  3.7689  3.7176  3.6343  3.5805  3.4231  3.2931  3.18    2.9733  2.8699  2.8203  2.5631  2.5283  2.4171  2.3994  2.3314  2.2341  2.2096  2.1435  2.0514  2.0267  1.9361
  1.8627]
V spectrum [0.246  0.2378 0.2226 0.2173 0.203  0.1922 0.1859 0.1823 0.1728 0.158  0.1527 0.1481 0.1405 0.1319 0.1256 0.1237 0.1186 0.1135 0.1103 0.0993 0.0917 0.0885 0.0811 0.0788 0.0744
 0.0672 0.0649 0.0622 0.0606 0.0587 0.056  0.0539 0.0521 0.0488 0.048  0.0472 0.0459 0.0446 0.0432 0.0429]
principal cosines between top-28 current/previous read spaces [0.9039 0.8947 0.8687 0.8536 0.84   0.8027 0.7746 0.7595 0.744  0.7321 0.7153 0.6996 0.6834 0.6692 0.6214 0.6013 0.5761 0.5362 0.5224 0.4663 0.4228 0.4174 0.2799 0.2083 0.1889
 0.1617 0.076  0.0716]

ACTIVE MLP ROW SIMILARITIES
nearest cosine percentiles [0.3449 0.5168 0.615  0.7556 0.9388 0.9725 0.9821 0.9873]

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

```python
import numpy as np
import scipy.linalg as la
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import connected_components
from threadpoolctl import threadpool_limits
np.set_printoptions(precision=4,suppress=True,linewidth=180)

def svd(a):
    return la.svd(a,full_matrices=False,lapack_driver="gesvd")
def unit(v):
    return v/(la.norm(v)+1e-30)

with threadpool_limits(limits=2):
    g=np.asarray(W["ln1_w.0"],dtype=float)
    q2=np.asarray(W["W_q.0"][128:192],dtype=float)*g
    k2=np.asarray(W["W_k.0"][128:192],dtype=float)*g
    P=svd(np.vstack([q2,k2]))[2][:32].T
    left,right=[],[]
    Bs=[]
    print("L0 DOMINANT QK FACTORS")
    for j in range(6):
        sl=slice(j*64,j*64+64)
        q=np.asarray(W["W_q.0"][sl],dtype=float)*g
        k=np.asarray(W["W_k.0"][sl],dtype=float)*g
        a=q.T@k/8
        u,s,vt=svd(a)
        left.append(u[:,0]); right.append(vt[0])
        b=(np.asarray(W["W_o.0"][:,sl],dtype=float)@np.asarray(W["W_v.0"][sl],dtype=float))*g
        Bs.append(b)
        print(f"H{j}: top-mode energy={s[0]**2/sum(s*s):.4f}, "
              f"query-pos32={la.norm(P.T@u[:,0])**2:.4f}, "
              f"key-pos32={la.norm(P.T@vt[0])**2:.4f}")
    print("absolute cosines of leading query directions")
    print(abs(np.array(left)@np.array(left).T))
    print("absolute cosines of leading key directions")
    print(abs(np.array(right)@np.array(right).T))

    R=svd(np.hstack([Bs[j] for j in (0,1,4,5)]))[0][:,:3]
    print("\nPOSITION-SUBSPACE AND AFFINE WEIGHT COEFFICIENTS")
    print("query-pos32 fraction; key-linear norm / QK norm; key-linear fraction in common 3D writes")
    for l in range(1,4):
        g1=np.asarray(W[f"ln1_w.{l}"],dtype=float)
        b1=np.asarray(W[f"ln1_b.{l}"],dtype=float)
        for j in range(6):
            sl=slice(j*64,j*64+64)
            raw=np.asarray(W[f"W_q.{l}"][sl],dtype=float).T@np.asarray(W[f"W_k.{l}"][sl],dtype=float)/8
            a=g1[:,None]*raw*g1
            c=(b1@raw)*g1
            print(f"L{l}H{j}",
                  np.round([la.norm(P.T@a)**2/la.norm(a)**2,
                            la.norm(c)/la.norm(a),
                            la.norm(c@R)**2/(la.norm(c)**2+1e-30)],4))

    F=np.asarray(W["W_fc.0"],dtype=float)*W["ln2_w.0"]
    proj=np.asarray(W["W_proj.0"],dtype=float)
    active=(la.norm(W["W_fc.0"],axis=1)>0.7)&(la.norm(proj,axis=0)>0.6)
    F=F[active]
    Z=F/la.norm(F,axis=1)[:,None]
    G=Z@Z.T
    print("\nACTIVE L0 MLP WEIGHT-ROW CLUSTERS")
    for threshold in (0.8,0.9,0.95,0.98):
        n,labels=connected_components(csr_matrix(G>threshold),directed=False)
        sizes=np.sort(np.bincount(labels))[::-1]
        print(f"cosine>{threshold}: components={n}, non-singleton neurons={sizes[sizes>1].sum()}, largest sizes={sizes[:30]}")
    U=svd(Bs[2])[0][:,:24]
    fr_prev=la.norm(F@U,axis=1)**2/la.norm(F,axis=1)**2
    fr_pos=la.norm(F@P,axis=1)**2/la.norm(F,axis=1)**2
    print("Previous-write top24 read-energy fraction quantiles",np.percentile(fr_prev,[0,10,25,50,75,90,100]))
    print("Position top32 read-energy fraction quantiles",np.percentile(fr_pos,[0,10,25,50,75,90,100]))
```
Permitted experiment harness: python — full request and result
{
  "run": 9,
  "turn": "t009",
  "code": "import numpy as np\nimport scipy.linalg as la\nfrom scipy.sparse import csr_matrix\nfrom scipy.sparse.csgraph import connected_components\nfrom threadpoolctl import threadpool_limits\nnp.set_printoptions(precision=4,suppress=True,linewidth=180)\n\ndef svd(a):\n    return la.svd(a,full_matrices=False,lapack_driver=\"gesvd\")\ndef unit(v):\n    return v/(la.norm(v)+1e-30)\n\nwith threadpool_limits(limits=2):\n    g=np.asarray(W[\"ln1_w.0\"],dtype=float)\n    q2=np.asarray(W[\"W_q.0\"][128:192],dtype=float)*g\n    k2=np.asarray(W[\"W_k.0\"][128:192],dtype=float)*g\n    P=svd(np.vstack([q2,k2]))[2][:32].T\n    left,right=[],[]\n    Bs=[]\n    print(\"L0 DOMINANT QK FACTORS\")\n    for j in range(6):\n        sl=slice(j*64,j*64+64)\n        q=np.asarray(W[\"W_q.0\"][sl],dtype=float)*g\n        k=np.asarray(W[\"W_k.0\"][sl],dtype=float)*g\n        a=q.T@k/8\n        u,s,vt=svd(a)\n        left.append(u[:,0]); right.append(vt[0])\n        b=(np.asarray(W[\"W_o.0\"][:,sl],dtype=float)@np.asarray(W[\"W_v.0\"][sl],dtype=float))*g\n        Bs.append(b)\n        print(f\"H{j}: top-mode energy={s[0]**2/sum(s*s):.4f}, \"\n              f\"query-pos32={la.norm(P.T@u[:,0])**2:.4f}, \"\n              f\"key-pos32={la.norm(P.T@vt[0])**2:.4f}\")\n    print(\"absolute cosines of leading query directions\")\n    print(abs(np.array(left)@np.array(left).T))\n    print(\"absolute cosines of leading key directions\")\n    print(abs(np.array(right)@np.array(right).T))\n\n    R=svd(np.hstack([Bs[j] for j in (0,1,4,5)]))[0][:,:3]\n    print(\"\\nPOSITION-SUBSPACE AND AFFINE WEIGHT COEFFICIENTS\")\n    print(\"query-pos32 fraction; key-linear norm / QK norm; key-linear fraction in common 3D writes\")\n    for l in range(1,4):\n        g1=np.asarray(W[f\"ln1_w.{l}\"],dtype=float)\n        b1=np.asarray(W[f\"ln1_b.{l}\"],dtype=float)\n        for j in range(6):\n            sl=slice(j*64,j*64+64)\n            raw=np.asarray(W[f\"W_q.{l}\"][sl],dtype=float).T@np.asarray(W[f\"W_k.{l}\"][sl],dtype=float)/8\n            a=g1[:,None]*raw*g1\n            c=(b1@raw)*g1\n            print(f\"L{l}H{j}\",\n                  np.round([la.norm(P.T@a)**2/la.norm(a)**2,\n                            la.norm(c)/la.norm(a),\n                            la.norm(c@R)**2/(la.norm(c)**2+1e-30)],4))\n\n    F=np.asarray(W[\"W_fc.0\"],dtype=float)*W[\"ln2_w.0\"]\n    proj=np.asarray(W[\"W_proj.0\"],dtype=float)\n    active=(la.norm(W[\"W_fc.0\"],axis=1)>0.7)&(la.norm(proj,axis=0)>0.6)\n    F=F[active]\n    Z=F/la.norm(F,axis=1)[:,None]\n    G=Z@Z.T\n    print(\"\\nACTIVE L0 MLP WEIGHT-ROW CLUSTERS\")\n    for threshold in (0.8,0.9,0.95,0.98):\n        n,labels=connected_components(csr_matrix(G>threshold),directed=False)\n        sizes=np.sort(np.bincount(labels))[::-1]\n        print(f\"cosine>{threshold}: components={n}, non-singleton neurons={sizes[sizes>1].sum()}, largest sizes={sizes[:30]}\")\n    U=svd(Bs[2])[0][:,:24]\n    fr_prev=la.norm(F@U,axis=1)**2/la.norm(F,axis=1)**2\n    fr_pos=la.norm(F@P,axis=1)**2/la.norm(F,axis=1)**2\n    print(\"Previous-write top24 read-energy fraction quantiles\",np.percentile(fr_prev,[0,10,25,50,75,90,100]))\n    print(\"Position top32 read-energy fraction quantiles\",np.percentile(fr_pos,[0,10,25,50,75,90,100]))",
  "out": "L0 DOMINANT QK FACTORS\nH0: top-mode energy=0.8877, query-pos32=0.8954, key-pos32=0.2819\nH1: top-mode energy=0.7148, query-pos32=0.7432, key-pos32=0.4198\nH2: top-mode energy=0.1204, query-pos32=0.9868, key-pos32=0.9970\nH3: top-mode energy=0.8269, query-pos32=0.8838, key-pos32=0.9328\nH4: top-mode energy=0.7035, query-pos32=0.5431, key-pos32=0.4386\nH5: top-mode energy=0.9578, query-pos32=0.8676, key-pos32=0.5002\nabsolute cosines of leading query directions\n[[1.     0.77   0.4272 0.7972 0.1273 0.2389]\n [0.77   1.     0.5194 0.7382 0.227  0.0475]\n [0.4272 0.5194 1.     0.4817 0.1129 0.0606]\n [0.7972 0.7382 0.4817 1.     0.0648 0.0329]\n [0.1273 0.227  0.1129 0.0648 1.     0.3261]\n [0.2389 0.0475 0.0606 0.0329 0.3261 1.    ]]\nabsolute cosines of leading key directions\n[[1.     0.264  0.0526 0.0032 0.3793 0.0979]\n [0.264  1.     0.0191 0.1238 0.2577 0.1429]\n [0.0526 0.0191 1.     0.513  0.0234 0.1277]\n [0.0032 0.1238 0.513  1.     0.101  0.0119]\n [0.3793 0.2577 0.0234 0.101  1.     0.6433]\n [0.0979 0.1429 0.1277 0.0119 0.6433 1.    ]]\n\nPOSITION-SUBSPACE AND AFFINE WEIGHT COEFFICIENTS\nquery-pos32 fraction; key-linear norm / QK norm; key-linear fraction in common 3D writes\nL1H0 [0.105  1.5297 0.8698]\nL1H1 [0.1152 1.7366 0.9031]\nL1H2 [0.1234 1.6683 0.9041]\nL1H3 [0.1198 1.7087 0.8899]\nL1H4 [0.1231 1.5788 0.9054]\nL1H5 [0.1265 1.3096 0.8894]\nL2H0 [0.0826 0.6969 0.8566]\nL2H1 [0.0713 0.3601 0.7939]\nL2H2 [0.0855 0.4684 0.8053]\nL2H3 [0.08   0.7009 0.8732]\nL2H4 [0.0852 0.6729 0.8644]\nL2H5 [0.0807 0.6703 0.8574]\nL3H0 [0.0772 0.1156 0.1503]\nL3H1 [0.0673 0.1319 0.0366]\nL3H2 [0.0797 0.1923 0.5941]\nL3H3 [0.0831 0.2253 0.6708]\nL3H4 [0.0801 0.1655 0.52  ]\nL3H5 [0.0777 0.1971 0.0378]\n\nACTIVE L0 MLP WEIGHT-ROW CLUSTERS\ncosine>0.8: components=284, non-singleton neurons=183, largest sizes=[7 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3]\ncosine>0.9: components=331, non-singleton neurons=127, largest sizes=[6 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2]\ncosine>0.95: components=355, non-singleton neurons=95, largest sizes=[4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\ncosine>0.98: components=409, non-singleton neurons=8, largest sizes=[2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\nPrevious-write top24 read-energy fraction quantiles [0.0058 0.0196 0.0675 0.1157 0.1704 0.2296 0.7185]\nPosition top32 read-energy fraction quantiles [0.0021 0.0058 0.0167 0.0289 0.0376 0.0515 0.0909]\n",
  "seconds": 0.969,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "8e732fd8741bc83e54209b11f1b42bb7ac34947d33227313f419a2899d64d6eb",
  "weights_sha256": "130135d2b27829df2734d78fa3af27b00b1cc1bf34c41b01a09b350e6c09a7de"
}
Exact feedback returned by the harness
L0 DOMINANT QK FACTORS
H0: top-mode energy=0.8877, query-pos32=0.8954, key-pos32=0.2819
H1: top-mode energy=0.7148, query-pos32=0.7432, key-pos32=0.4198
H2: top-mode energy=0.1204, query-pos32=0.9868, key-pos32=0.9970
H3: top-mode energy=0.8269, query-pos32=0.8838, key-pos32=0.9328
H4: top-mode energy=0.7035, query-pos32=0.5431, key-pos32=0.4386
H5: top-mode energy=0.9578, query-pos32=0.8676, key-pos32=0.5002
absolute cosines of leading query directions
[[1.     0.77   0.4272 0.7972 0.1273 0.2389]
 [0.77   1.     0.5194 0.7382 0.227  0.0475]
 [0.4272 0.5194 1.     0.4817 0.1129 0.0606]
 [0.7972 0.7382 0.4817 1.     0.0648 0.0329]
 [0.1273 0.227  0.1129 0.0648 1.     0.3261]
 [0.2389 0.0475 0.0606 0.0329 0.3261 1.    ]]
absolute cosines of leading key directions
[[1.     0.264  0.0526 0.0032 0.3793 0.0979]
 [0.264  1.     0.0191 0.1238 0.2577 0.1429]
 [0.0526 0.0191 1.     0.513  0.0234 0.1277]
 [0.0032 0.1238 0.513  1.     0.101  0.0119]
 [0.3793 0.2577 0.0234 0.101  1.     0.6433]
 [0.0979 0.1429 0.1277 0.0119 0.6433 1.    ]]

POSITION-SUBSPACE AND AFFINE WEIGHT COEFFICIENTS
query-pos32 fraction; key-linear norm / QK norm; key-linear fraction in common 3D writes
L1H0 [0.105  1.5297 0.8698]
L1H1 [0.1152 1.7366 0.9031]
L1H2 [0.1234 1.6683 0.9041]
L1H3 [0.1198 1.7087 0.8899]
L1H4 [0.1231 1.5788 0.9054]
L1H5 [0.1265 1.3096 0.8894]
L2H0 [0.0826 0.6969 0.8566]
L2H1 [0.0713 0.3601 0.7939]
L2H2 [0.0855 0.4684 0.8053]
L2H3 [0.08   0.7009 0.8732]
L2H4 [0.0852 0.6729 0.8644]
L2H5 [0.0807 0.6703 0.8574]
L3H0 [0.0772 0.1156 0.1503]
L3H1 [0.0673 0.1319 0.0366]
L3H2 [0.0797 0.1923 0.5941]
L3H3 [0.0831 0.2253 0.6708]
L3H4 [0.0801 0.1655 0.52  ]
L3H5 [0.0777 0.1971 0.0378]

ACTIVE L0 MLP WEIGHT-ROW CLUSTERS
cosine>0.8: components=284, non-singleton neurons=183, largest sizes=[7 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3]
cosine>0.9: components=331, non-singleton neurons=127, largest sizes=[6 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2]
cosine>0.95: components=355, non-singleton neurons=95, largest sizes=[4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
cosine>0.98: components=409, non-singleton neurons=8, largest sizes=[2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
Previous-write top24 read-energy fraction quantiles [0.0058 0.0196 0.0675 0.1157 0.1704 0.2296 0.7185]
Position top32 read-energy fraction quantiles [0.0021 0.0058 0.0167 0.0289 0.0376 0.0515 0.0909]
t010 · no harness call

Recorded prompt · Raw CLI events · Settings and audit

Message sent to Codex — 2,484 characters
OUTPUT of run 9 of at most 100:
```
L0 DOMINANT QK FACTORS
H0: top-mode energy=0.8877, query-pos32=0.8954, key-pos32=0.2819
H1: top-mode energy=0.7148, query-pos32=0.7432, key-pos32=0.4198
H2: top-mode energy=0.1204, query-pos32=0.9868, key-pos32=0.9970
H3: top-mode energy=0.8269, query-pos32=0.8838, key-pos32=0.9328
H4: top-mode energy=0.7035, query-pos32=0.5431, key-pos32=0.4386
H5: top-mode energy=0.9578, query-pos32=0.8676, key-pos32=0.5002
absolute cosines of leading query directions
[[1.     0.77   0.4272 0.7972 0.1273 0.2389]
 [0.77   1.     0.5194 0.7382 0.227  0.0475]
 [0.4272 0.5194 1.     0.4817 0.1129 0.0606]
 [0.7972 0.7382 0.4817 1.     0.0648 0.0329]
 [0.1273 0.227  0.1129 0.0648 1.     0.3261]
 [0.2389 0.0475 0.0606 0.0329 0.3261 1.    ]]
absolute cosines of leading key directions
[[1.     0.264  0.0526 0.0032 0.3793 0.0979]
 [0.264  1.     0.0191 0.1238 0.2577 0.1429]
 [0.0526 0.0191 1.     0.513  0.0234 0.1277]
 [0.0032 0.1238 0.513  1.     0.101  0.0119]
 [0.3793 0.2577 0.0234 0.101  1.     0.6433]
 [0.0979 0.1429 0.1277 0.0119 0.6433 1.    ]]

POSITION-SUBSPACE AND AFFINE WEIGHT COEFFICIENTS
query-pos32 fraction; key-linear norm / QK norm; key-linear fraction in common 3D writes
L1H0 [0.105  1.5297 0.8698]
L1H1 [0.1152 1.7366 0.9031]
L1H2 [0.1234 1.6683 0.9041]
L1H3 [0.1198 1.7087 0.8899]
L1H4 [0.1231 1.5788 0.9054]
L1H5 [0.1265 1.3096 0.8894]
L2H0 [0.0826 0.6969 0.8566]
L2H1 [0.0713 0.3601 0.7939]
L2H2 [0.0855 0.4684 0.8053]
L2H3 [0.08   0.7009 0.8732]
L2H4 [0.0852 0.6729 0.8644]
L2H5 [0.0807 0.6703 0.8574]
L3H0 [0.0772 0.1156 0.1503]
L3H1 [0.0673 0.1319 0.0366]
L3H2 [0.0797 0.1923 0.5941]
L3H3 [0.0831 0.2253 0.6708]
L3H4 [0.0801 0.1655 0.52  ]
L3H5 [0.0777 0.1971 0.0378]

ACTIVE L0 MLP WEIGHT-ROW CLUSTERS
cosine>0.8: components=284, non-singleton neurons=183, largest sizes=[7 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3]
cosine>0.9: components=331, non-singleton neurons=127, largest sizes=[6 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2]
cosine>0.95: components=355, non-singleton neurons=95, largest sizes=[4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
cosine>0.98: components=409, non-singleton neurons=8, largest sizes=[2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
Previous-write top24 read-energy fraction quantiles [0.0058 0.0196 0.0675 0.1157 0.1704 0.2296 0.7185]
Position top32 read-energy fraction quantiles [0.0021 0.0058 0.0167 0.0289 0.0376 0.0515 0.0909]

```
CLI notice/error (verbatim)
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
CLI notice/error (verbatim)
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`.

Codex reply

## Observations

These are **weight-based hypotheses, not measured attention patterns**. Missing embeddings particularly limit corpus identification and attention-distance estimates.

Write \(A=DQ^\top KD/8\), \(B=OVD\), where \(D=\mathrm{diag}(\text{ln1\_w})\).

- **L0H2 is exceptional:** its `W_q.0[128:192]` and `W_k.0[128:192]` norms are **30.818 and 28.300**. Its \(A\)-norm is **4.078**, versus **0.030–0.395** for the other L0 heads. Its substantial complex-conjugate eigenvalues suggest a directional positional operation—plausibly previous-character retrieval.
- **Several early heads share a compact summary representation.** One three-dimensional write subspace captures **97.6%, 97.5%, 92.3%, 97.1%** of the \(B\)-energy of L0H0/H1/H4/H5. Their read directions substantially overlap L0H2’s Q/K subspace. Positional summaries are a plausible interpretation.
- **L3H1 has a distinctive induction-compatible connection to L0H2.** Restricting \(A_{3,1}B_{0,2}\) to the latter’s leading 24 read directions gives normalized trace **0.624** and symmetric-part fraction **0.914**. Other later heads’ corresponding normalized traces range from **−0.125 to 0.150**. This is substantially stronger evidence than merely having large weights.
- These measurements reveal structured context processing, but **do not reveal identifiable English words, Python syntax, or Japanese phonotactics**.

## Heads

Here, “local” is a tentative recency interpretation, not an established distance. The *shift score* is the restricted normalized trace defined above.

L0H0: **broad** — weak \(A\)-norm 0.055; 97.6% of value-write energy occupies the shared summary subspace.  
L0H1: **broad** — smallest \(A\)-norm, 0.030; 97.5% shared-summary write energy.  
L0H2: **previous** — exceptionally strong, directional Q/K structure and comparatively multidimensional value transfer; strongest previous-character candidate.  
L0H3: **local** — dominant query/key directions overlap the candidate positional subspace by 88.4%/93.3%; weaker, simpler positional mechanism.  
L0H4: **broad** — \(A\)-norm 0.106; 92.3% shared-summary write energy.  
L0H5: **broad** — nearly rank-one \(A\), stable rank 1.04; 97.1% shared-summary write energy.

L1H0: **local** — tentative positional retrieval; 64.1% of key-side \(A\)-energy uses the shared summary subspace.  
L1H1: **local** — similar mechanism; key-side fraction 60.0%, with strong shared-subspace affine key preference.  
L1H2: **local** — key-side fraction 63.4%; weak shifted-match evidence, score 0.128, insufficient for “copy.”  
L1H3: **local** — key-side fraction 63.5%; shift score 0.150, still far below L3H1.  
L1H4: **local** — strongest shared-summary key dependence, 69.2%; shift score 0.144.  
L1H5: **local** — key-side fraction 63.1%; shift score only 0.046.

L2H0: **unclear** — richer Q/K structure; shift score −0.075; positional preferences do not establish retrieval distance.  
L2H1: **unclear** — shift score −0.086; relatively weak dependence on the shared positional-summary candidate.  
L2H2: **unclear** — substantial summary dependence, but shift score 0.017; local versus broader retrieval unresolved.  
L2H3: **unclear** — shift score 0.064; mixed content and summary connections without a decisive mechanism.  
L2H4: **unclear** — shift score −0.125; no positive direct induction signature through L0H2.  
L2H5: **unclear** — shift score 0.013; contextual routing is evident, its span is not.

L3H0: **unclear** — positive self-alignment in Q/K, but shift score −0.033; similarity matching need not imply induction.  
L3H1: **copy** — standout shifted-match score 0.624 and symmetric fraction 0.914; strongest induction candidate.  
L3H2: **unclear** — shift score 0.052; remaining positional-summary dependence does not determine attention range.  
L3H3: **unclear** — shift score 0.018; no convincing direct copy signature.  
L3H4: **unclear** — shift score −0.072; mixed content/position-related weight structure.  
L3H5: **broad** — tentative aggregation role: relatively weak \(A\)-norm 0.835, large \(B\)-norm 45.401, and little shared-summary key dependence.

## Reasoning

The combination of a likely previous-character mechanism and a strong downstream shifted-matching circuit favors **preserved character order**. Shuffling would greatly reduce the usefulness of retrieving successors of matching earlier contexts, although it would not eliminate all contextual predictability.

**English is my weak corpus preference**, consistent with local orthographic processing plus contextual copying. These signatures also fit Python and romaji; I did not recover a corpus-specific diagnostic sufficient for a confident distinction. The corpus probabilities below are subjective judgments, not calibrated estimates.

corpus: english  
order: as is  
probabilities: python=0.34 romaji=0.23 english=0.43; as is=0.93 shuffled=0.07
Saved final answer
## Observations

These are **weight-based hypotheses, not measured attention patterns**. Missing embeddings particularly limit corpus identification and attention-distance estimates.

Write \(A=DQ^\top KD/8\), \(B=OVD\), where \(D=\mathrm{diag}(\text{ln1\_w})\).

- **L0H2 is exceptional:** its `W_q.0[128:192]` and `W_k.0[128:192]` norms are **30.818 and 28.300**. Its \(A\)-norm is **4.078**, versus **0.030–0.395** for the other L0 heads. Its substantial complex-conjugate eigenvalues suggest a directional positional operation—plausibly previous-character retrieval.
- **Several early heads share a compact summary representation.** One three-dimensional write subspace captures **97.6%, 97.5%, 92.3%, 97.1%** of the \(B\)-energy of L0H0/H1/H4/H5. Their read directions substantially overlap L0H2’s Q/K subspace. Positional summaries are a plausible interpretation.
- **L3H1 has a distinctive induction-compatible connection to L0H2.** Restricting \(A_{3,1}B_{0,2}\) to the latter’s leading 24 read directions gives normalized trace **0.624** and symmetric-part fraction **0.914**. Other later heads’ corresponding normalized traces range from **−0.125 to 0.150**. This is substantially stronger evidence than merely having large weights.
- These measurements reveal structured context processing, but **do not reveal identifiable English words, Python syntax, or Japanese phonotactics**.

## Heads

Here, “local” is a tentative recency interpretation, not an established distance. The *shift score* is the restricted normalized trace defined above.

L0H0: **broad** — weak \(A\)-norm 0.055; 97.6% of value-write energy occupies the shared summary subspace.  
L0H1: **broad** — smallest \(A\)-norm, 0.030; 97.5% shared-summary write energy.  
L0H2: **previous** — exceptionally strong, directional Q/K structure and comparatively multidimensional value transfer; strongest previous-character candidate.  
L0H3: **local** — dominant query/key directions overlap the candidate positional subspace by 88.4%/93.3%; weaker, simpler positional mechanism.  
L0H4: **broad** — \(A\)-norm 0.106; 92.3% shared-summary write energy.  
L0H5: **broad** — nearly rank-one \(A\), stable rank 1.04; 97.1% shared-summary write energy.

L1H0: **local** — tentative positional retrieval; 64.1% of key-side \(A\)-energy uses the shared summary subspace.  
L1H1: **local** — similar mechanism; key-side fraction 60.0%, with strong shared-subspace affine key preference.  
L1H2: **local** — key-side fraction 63.4%; weak shifted-match evidence, score 0.128, insufficient for “copy.”  
L1H3: **local** — key-side fraction 63.5%; shift score 0.150, still far below L3H1.  
L1H4: **local** — strongest shared-summary key dependence, 69.2%; shift score 0.144.  
L1H5: **local** — key-side fraction 63.1%; shift score only 0.046.

L2H0: **unclear** — richer Q/K structure; shift score −0.075; positional preferences do not establish retrieval distance.  
L2H1: **unclear** — shift score −0.086; relatively weak dependence on the shared positional-summary candidate.  
L2H2: **unclear** — substantial summary dependence, but shift score 0.017; local versus broader retrieval unresolved.  
L2H3: **unclear** — shift score 0.064; mixed content and summary connections without a decisive mechanism.  
L2H4: **unclear** — shift score −0.125; no positive direct induction signature through L0H2.  
L2H5: **unclear** — shift score 0.013; contextual routing is evident, its span is not.

L3H0: **unclear** — positive self-alignment in Q/K, but shift score −0.033; similarity matching need not imply induction.  
L3H1: **copy** — standout shifted-match score 0.624 and symmetric fraction 0.914; strongest induction candidate.  
L3H2: **unclear** — shift score 0.052; remaining positional-summary dependence does not determine attention range.  
L3H3: **unclear** — shift score 0.018; no convincing direct copy signature.  
L3H4: **unclear** — shift score −0.072; mixed content/position-related weight structure.  
L3H5: **broad** — tentative aggregation role: relatively weak \(A\)-norm 0.835, large \(B\)-norm 45.401, and little shared-summary key dependence.

## Reasoning

The combination of a likely previous-character mechanism and a strong downstream shifted-matching circuit favors **preserved character order**. Shuffling would greatly reduce the usefulness of retrieving successors of matching earlier contexts, although it would not eliminate all contextual predictability.

**English is my weak corpus preference**, consistent with local orthographic processing plus contextual copying. These signatures also fit Python and romaji; I did not recover a corpus-specific diagnostic sufficient for a confident distinction. The corpus probabilities below are subjective judgments, not calibrated estimates.

corpus: english  
order: as is  
probabilities: python=0.34 romaji=0.23 english=0.43; as is=0.93 shuffled=0.07