Experiment 3 · romaji r0 · fresh
SOURCE: outputs/replication/reconstructed-exp3/runs/4117069e806b4e30969f960586fb6f52
Recorded prompts and outputs are reproduced below without shortening. CLI reasoning entries are emitted summaries. Raw records preserve metadata, notices and original failed resume attempts.

RECORDED INSTRUCTION FILE
Answer the user's request.


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

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

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

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

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

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

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

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

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

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

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

AGENT_MESSAGE
```python
import numpy as np
from threadpoolctl import threadpool_limits
threadpool_limits(2)
np.set_printoptions(precision=4, suppress=True)

def summary(a):
    a = np.asarray(a, dtype=np.float64)
    return (f"mean={a.mean():.5f} sd={a.std():.5f} "
            f"min={a.min():.5f} max={a.max():.5f} norm={np.linalg.norm(a):.4f}")

print("KEYS:", sorted(W))
for l in range(4):
    print(f"\nLAYER {l}")
    for name in ["ln1_w", "ln1_b", "ln2_w", "ln2_b"]:
        print(name, summary(W[f"{name}.{l}"]))
    for name in ["W_q", "W_k", "W_v", "W_o", "W_fc", "W_proj"]:
        a = W[f"{name}.{l}"].astype(np.float64)
        s = np.linalg.svd(a, compute_uv=False)
        er = (s @ s)**2 / np.sum(s**4)
        print(name, f"F={np.linalg.norm(a):.4f}",
              "singular_top8=", np.round(s[:8], 4),
              f"smedian={np.median(s):.4f} effective_rank={er:.2f}")

print("\nHEADS: QK=(Wq D)^T(Wk D)/8; OV=Wo Wv D; D=diag(ln1_w)")
print("tr/F is signed trace divided by Frobenius norm; sym is symmetric-part norm/F.")
for l in range(4):
    d = W[f"ln1_w.{l}"].astype(np.float64)
    for j in range(6):
        sl = slice(64*j, 64*(j+1))
        q = W[f"W_q.{l}"][sl].astype(np.float64) * d
        k = W[f"W_k.{l}"][sl].astype(np.float64) * d
        v = W[f"W_v.{l}"][sl].astype(np.float64) * d
        o = W[f"W_o.{l}"][:, sl].astype(np.float64)
        a = q.T @ k / 8
        b = o @ v
        sa = np.linalg.svd(a, compute_uv=False)[:64]
        sb = np.linalg.svd(b, compute_uv=False)[:64]
        ea = np.linalg.eigvalsh((a+a.T)/2)
        eb = np.linalg.eigvals(v @ o)
        leadb = eb[np.argsort(np.abs(eb))[-4:][::-1]]
        print(f"L{l}H{j}",
              f"QK_F={np.linalg.norm(a):.3f} tr/F={np.trace(a)/np.linalg.norm(a):.3f}",
              f"sym={np.linalg.norm((a+a.T)/2)/np.linalg.norm(a):.3f}",
              f"QK_ER={(sa@sa)**2/np.sum(sa**4):.2f}",
              "QK_s1-4=", np.round(sa[:4],3),
              "QK_sym_extremes=", np.round(ea[[0,1,-2,-1]],3),
              f"OV_F={np.linalg.norm(b):.3f} tr/F={np.trace(b)/np.linalg.norm(b):.3f}",
              f"OV_ER={(sb@sb)**2/np.sum(sb**4):.2f}",
              "OV_eig=", np.round(leadb,3))
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 1,
  "turn": "t001",
  "code": "import numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=4, suppress=True)\n\ndef summary(a):\n    a = np.asarray(a, dtype=np.float64)\n    return (f\"mean={a.mean():.5f} sd={a.std():.5f} \"\n            f\"min={a.min():.5f} max={a.max():.5f} norm={np.linalg.norm(a):.4f}\")\n\nprint(\"KEYS:\", sorted(W))\nfor l in range(4):\n    print(f\"\\nLAYER {l}\")\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        print(name, summary(W[f\"{name}.{l}\"]))\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        a = W[f\"{name}.{l}\"].astype(np.float64)\n        s = np.linalg.svd(a, compute_uv=False)\n        er = (s @ s)**2 / np.sum(s**4)\n        print(name, f\"F={np.linalg.norm(a):.4f}\",\n              \"singular_top8=\", np.round(s[:8], 4),\n              f\"smedian={np.median(s):.4f} effective_rank={er:.2f}\")\n\nprint(\"\\nHEADS: QK=(Wq D)^T(Wk D)/8; OV=Wo Wv D; D=diag(ln1_w)\")\nprint(\"tr/F is signed trace divided by Frobenius norm; sym is symmetric-part norm/F.\")\nfor l in range(4):\n    d = W[f\"ln1_w.{l}\"].astype(np.float64)\n    for j in range(6):\n        sl = slice(64*j, 64*(j+1))\n        q = W[f\"W_q.{l}\"][sl].astype(np.float64) * d\n        k = W[f\"W_k.{l}\"][sl].astype(np.float64) * d\n        v = W[f\"W_v.{l}\"][sl].astype(np.float64) * d\n        o = W[f\"W_o.{l}\"][:, sl].astype(np.float64)\n        a = q.T @ k / 8\n        b = o @ v\n        sa = np.linalg.svd(a, compute_uv=False)[:64]\n        sb = np.linalg.svd(b, compute_uv=False)[:64]\n        ea = np.linalg.eigvalsh((a+a.T)/2)\n        eb = np.linalg.eigvals(v @ o)\n        leadb = eb[np.argsort(np.abs(eb))[-4:][::-1]]\n        print(f\"L{l}H{j}\",\n              f\"QK_F={np.linalg.norm(a):.3f} tr/F={np.trace(a)/np.linalg.norm(a):.3f}\",\n              f\"sym={np.linalg.norm((a+a.T)/2)/np.linalg.norm(a):.3f}\",\n              f\"QK_ER={(sa@sa)**2/np.sum(sa**4):.2f}\",\n              \"QK_s1-4=\", np.round(sa[:4],3),\n              \"QK_sym_extremes=\", np.round(ea[[0,1,-2,-1]],3),\n              f\"OV_F={np.linalg.norm(b):.3f} tr/F={np.trace(b)/np.linalg.norm(b):.3f}\",\n              f\"OV_ER={(sb@sb)**2/np.sum(sb**4):.2f}\",\n              \"OV_eig=\", np.round(leadb,3))",
  "out": "KEYS: ['W_fc.0', 'W_fc.1', 'W_fc.2', 'W_fc.3', 'W_k.0', 'W_k.1', 'W_k.2', 'W_k.3', 'W_o.0', 'W_o.1', 'W_o.2', 'W_o.3', 'W_proj.0', 'W_proj.1', 'W_proj.2', 'W_proj.3', 'W_q.0', 'W_q.1', 'W_q.2', 'W_q.3', 'W_v.0', 'W_v.1', 'W_v.2', 'W_v.3', 'ln1_b.0', 'ln1_b.1', 'ln1_b.2', 'ln1_b.3', 'ln1_w.0', 'ln1_w.1', 'ln1_w.2', 'ln1_w.3', 'ln2_b.0', 'ln2_b.1', 'ln2_b.2', 'ln2_b.3', 'ln2_w.0', 'ln2_w.1', 'ln2_w.2', 'ln2_w.3']\n\nLAYER 0\nln1_w mean=0.29650 sd=0.18837 min=0.03522 max=0.97846 norm=6.8836\nln1_b mean=0.00039 sd=0.03113 min=-0.09542 max=0.09061 norm=0.6101\nln2_w mean=0.87176 sd=0.33816 min=0.04203 max=1.77496 norm=18.3232\nln2_b mean=0.01340 sd=0.06913 min=-0.62361 max=0.46365 norm=1.3799\nW_q F=17.6693 singular_top8= [4.3676 3.1308 3.0988 3.0421 2.9941 2.9513 2.9258 2.8812] smedian=0.3184 effective_rank=50.11\nW_k F=18.5161 singular_top8= [4.1635 3.9516 3.4672 3.4271 3.3642 3.3348 3.2789 3.2565] smedian=0.3365 effective_rank=46.21\nW_v F=14.9367 singular_top8= [5.2084 3.3641 3.0064 2.9623 2.815  2.6389 2.5269 2.4605] smedian=0.3058 effective_rank=31.93\nW_o F=16.5495 singular_top8= [7.3608 6.0993 3.1666 2.4518 2.3212 2.2022 2.1709 2.1597] smedian=0.1874 effective_rank=15.33\nW_fc F=55.3764 singular_top8= [18.1182 12.7604 11.3373  9.3228  9.2666  9.0207  8.8979  8.8171] smedian=0.9490 effective_rank=36.47\nW_proj F=50.0070 singular_top8= [10.899   7.7021  7.1728  6.6377  6.5415  6.2882  6.2009  6.1328] smedian=1.4759 effective_rank=103.70\n\nLAYER 1\nln1_w mean=0.50583 sd=0.08918 min=0.19701 max=1.08462 norm=10.0651\nln1_b mean=0.01103 sd=0.15458 min=-1.56756 max=1.36513 norm=3.0369\nln2_w mean=1.13147 sd=0.17415 min=0.00187 max=1.51965 norm=22.4333\nln2_b mean=0.00647 sd=0.14352 min=-0.38920 max=0.41700 norm=2.8153\nW_q F=38.9293 singular_top8= [10.4064  8.2394  7.4754  7.0325  6.6223  6.395   6.275   6.1297] smedian=0.4279 effective_rank=47.70\nW_k F=34.8926 singular_top8= [14.4767 11.787   6.3698  6.0538  5.5225  5.4072  5.2682  5.167 ] smedian=0.4020 effective_rank=19.20\nW_v F=28.7445 singular_top8= [4.7281 4.5469 4.4588 4.3949 4.3614 4.3317 4.2915 4.276 ] smedian=0.5104 effective_rank=78.15\nW_o F=28.4790 singular_top8= [4.0451 3.2675 3.1879 3.1786 3.1633 3.1006 3.0706 3.0465] smedian=0.7287 effective_rank=135.33\nW_fc F=80.3794 singular_top8= [19.8292 11.575  11.5215 11.2622 11.059  10.8746 10.6322 10.4972] smedian=1.9520 effective_rank=81.51\nW_proj F=77.3844 singular_top8= [9.0375 8.457  8.1027 7.3326 7.3184 7.0875 6.7718 6.666 ] smedian=3.5967 effective_rank=256.84\n\nLAYER 2\nln1_w mean=0.68087 sd=0.06874 min=0.31750 max=1.09272 norm=13.4102\nln1_b mean=0.01305 sd=0.15645 min=-1.65888 max=1.33182 norm=3.0765\nln2_w mean=1.31041 sd=0.11485 min=0.01202 max=1.50783 norm=25.7770\nln2_b mean=0.01229 sd=0.17372 min=-0.51705 max=0.44886 norm=3.4127\nW_q F=38.9025 singular_top8= [5.9663 5.0189 4.865  4.7735 4.6954 4.5676 4.4963 4.4331] smedian=1.3420 effective_rank=141.54\nW_k F=36.7500 singular_top8= [9.8633 9.5331 4.3864 4.1715 4.1168 3.9556 3.8793 3.7772] smedian=1.2369 effective_rank=69.01\nW_v F=33.1825 singular_top8= [3.9143 3.78   3.727  3.6622 3.5821 3.5534 3.5099 3.4637] smedian=1.2014 effective_rank=162.37\nW_o F=32.4345 singular_top8= [4.3982 4.0371 3.8746 3.739  3.621  3.5889 3.4883 3.4674] smedian=1.1896 effective_rank=160.76\nW_fc F=79.8282 singular_top8= [15.7338  9.2489  8.718   8.6937  8.4705  7.865   7.7393  7.4819] smedian=3.3774 effective_rank=172.27\nW_proj F=78.0482 singular_top8= [10.1974  9.0347  8.8821  8.4108  8.2242  8.0885  7.9876  7.8273] smedian=3.4490 effective_rank=219.46\n\nLAYER 3\nln1_w mean=0.70875 sd=0.04941 min=0.40780 max=0.81546 norm=13.9223\nln1_b mean=0.02963 sd=0.09939 min=-0.76388 max=0.82412 norm=2.0324\nln2_w mean=1.26681 sd=0.08945 min=0.23280 max=1.45324 norm=24.8860\nln2_b mean=0.01280 sd=0.14128 min=-0.54017 max=0.39317 norm=2.7798\nW_q F=33.2388 singular_top8= [4.0862 3.9326 3.8093 3.7155 3.6803 3.6334 3.5712 3.5556] smedian=1.2181 effective_rank=160.41\nW_k F=32.4633 singular_top8= [6.3618 5.8423 3.6963 3.4585 3.3626 3.304  3.2563 3.2439] smedian=1.2120 effective_rank=132.45\nW_v F=39.4218 singular_top8= [5.6601 5.4237 5.1815 5.1585 5.0739 5.0075 4.829  4.6848] smedian=1.3079 effective_rank=133.02\nW_o F=40.1331 singular_top8= [10.4767  7.3569  5.3233  5.2944  5.177   5.1238  5.0488  4.9589] smedian=1.2119 effective_rank=81.34\nW_fc F=78.2595 singular_top8= [15.1141  8.6112  8.486   8.0732  7.7689  7.5291  7.4493  7.3342] smedian=3.5182 effective_rank=194.49\nW_proj F=85.1194 singular_top8= [28.4613 22.2137 21.3397 20.708  19.9429 19.6618 18.9864 18.744 ] smedian=0.8176 effective_rank=20.80\n\nHEADS: QK=(Wq D)^T(Wk D)/8; OV=Wo Wv D; D=diag(ln1_w)\ntr/F is signed trace divided by Frobenius norm; sym is symmetric-part norm/F.\nL0H0 QK_F=0.294 tr/F=-0.357 sym=0.755 QK_ER=1.90 QK_s1-4= [0.248 0.102 0.047 0.043] QK_sym_extremes= [-0.073 -0.035  0.043  0.182] OV_F=2.128 tr/F=-0.067 OV_ER=8.58 OV_eig= [-0.086+0.034j -0.086-0.034j -0.057+0.048j -0.057-0.048j]\nL0H1 QK_F=0.159 tr/F=-0.620 sym=0.762 QK_ER=1.25 QK_s1-4= [0.15  0.042 0.021 0.012] QK_sym_extremes= [-0.106 -0.024  0.019  0.045] OV_F=3.979 tr/F=0.231 OV_ER=1.70 OV_eig= [ 1.386+0.j    -0.245+0.j     0.012+0.045j  0.012-0.045j]\nL0H2 QK_F=0.107 tr/F=-0.814 sym=0.803 QK_ER=1.21 QK_s1-4= [0.102 0.018 0.016 0.012] QK_sym_extremes= [-0.079 -0.012  0.005  0.025] OV_F=3.033 tr/F=0.229 OV_ER=1.62 OV_eig= [ 0.954+0.j    -0.118+0.j     0.05 +0.041j  0.05 -0.041j]\nL0H3 QK_F=0.699 tr/F=1.923 sym=0.683 QK_ER=17.06 QK_s1-4= [0.244 0.223 0.201 0.182] QK_sym_extremes= [-0.078 -0.056  0.17   0.186] OV_F=3.536 tr/F=-0.570 OV_ER=19.72 OV_eig= [-0.001+0.207j -0.001-0.207j -0.085+0.183j -0.085-0.183j]\nL0H4 QK_F=0.089 tr/F=-0.519 sym=0.725 QK_ER=1.52 QK_s1-4= [0.08  0.022 0.019 0.013] QK_sym_extremes= [-0.047 -0.016  0.007  0.033] OV_F=1.684 tr/F=0.102 OV_ER=1.70 OV_eig= [ 0.501+0.j   -0.061+0.j   -0.046+0.j   -0.033+0.02j]\nL0H5 QK_F=0.034 tr/F=-0.254 sym=0.716 QK_ER=1.23 QK_s1-4= [0.032 0.004 0.003 0.003] QK_sym_extremes= [-0.018 -0.002  0.003  0.014] OV_F=18.820 tr/F=0.111 OV_ER=1.64 OV_eig= [ 0.554+1.534j  0.554-1.534j  1.168+0.j    -0.395+0.j   ]\nL1H0 QK_F=1.981 tr/F=-0.883 sym=0.722 QK_ER=5.90 QK_s1-4= [1.185 0.729 0.557 0.534] QK_sym_extremes= [-0.585 -0.386  0.402  0.601] OV_F=11.481 tr/F=-1.346 OV_ER=24.55 OV_eig= [-1.071+0.031j -1.071-0.031j -0.967+0.207j -0.967-0.207j]\nL1H1 QK_F=1.846 tr/F=-0.845 sym=0.713 QK_ER=5.08 QK_s1-4= [1.155 0.715 0.583 0.465] QK_sym_extremes= [-0.575 -0.336  0.381  0.577] OV_F=11.266 tr/F=-1.351 OV_ER=22.13 OV_eig= [-1.056+0.085j -1.056-0.085j -0.818+0.25j  -0.818-0.25j ]\nL1H2 QK_F=1.783 tr/F=-1.059 sym=0.720 QK_ER=4.53 QK_s1-4= [1.176 0.62  0.485 0.436] QK_sym_extremes= [-0.598 -0.362  0.326  0.577] OV_F=11.810 tr/F=-1.467 OV_ER=20.46 OV_eig= [-1.307+0.j    -1.2  +0.096j -1.2  -0.096j -0.986+0.026j]\nL1H3 QK_F=1.791 tr/F=-0.844 sym=0.709 QK_ER=4.34 QK_s1-4= [1.188 0.678 0.532 0.388] QK_sym_extremes= [-0.595 -0.304  0.346  0.592] OV_F=12.401 tr/F=-1.481 OV_ER=22.76 OV_eig= [-1.215+0.244j -1.215-0.244j -1.162+0.j    -0.93 +0.326j]\nL1H4 QK_F=1.630 tr/F=-1.234 sym=0.723 QK_ER=4.86 QK_s1-4= [1.059 0.505 0.477 0.351] QK_sym_extremes= [-0.506 -0.276  0.279  0.557] OV_F=13.401 tr/F=-1.429 OV_ER=20.93 OV_eig= [-1.499+0.j    -1.365+0.05j  -1.365-0.05j  -1.009+0.162j]\nL1H5 QK_F=1.867 tr/F=-0.874 sym=0.713 QK_ER=5.37 QK_s1-4= [1.147 0.719 0.556 0.499] QK_sym_extremes= [-0.607 -0.383  0.377  0.558] OV_F=10.921 tr/F=-1.369 OV_ER=21.74 OV_eig= [-1.112+0.j    -0.919+0.j    -0.875+0.104j -0.875-0.104j]\nL2H0 QK_F=1.635 tr/F=-0.954 sym=0.693 QK_ER=16.43 QK_s1-4= [0.694 0.555 0.35  0.328] QK_sym_extremes= [-0.303 -0.233  0.269  0.342] OV_F=16.382 tr/F=-1.027 OV_ER=24.63 OV_eig= [-1.786+0.j    -1.284+0.369j -1.284-0.369j -1.149+0.072j]\nL2H1 QK_F=2.155 tr/F=-0.100 sym=0.700 QK_ER=31.47 QK_s1-4= [0.578 0.514 0.509 0.466] QK_sym_extremes= [-0.289 -0.279  0.293  0.303] OV_F=17.620 tr/F=-1.995 OV_ER=36.57 OV_eig= [-1.758+0.j    -1.526+0.297j -1.526-0.297j -1.438+0.j   ]\nL2H2 QK_F=1.721 tr/F=-0.754 sym=0.706 QK_ER=31.27 QK_s1-4= [0.518 0.412 0.4   0.374] QK_sym_extremes= [-0.246 -0.231  0.213  0.273] OV_F=18.665 tr/F=-1.604 OV_ER=29.47 OV_eig= [-2.375+0.j    -1.782+0.329j -1.782-0.329j -1.811+0.j   ]\nL2H3 QK_F=1.624 tr/F=-0.367 sym=0.704 QK_ER=30.99 QK_s1-4= [0.503 0.385 0.364 0.334] QK_sym_extremes= [-0.232 -0.212  0.198  0.273] OV_F=19.000 tr/F=-1.590 OV_ER=32.89 OV_eig= [-1.936+0.231j -1.936-0.231j -1.686+0.j    -1.338+0.j   ]\nL2H4 QK_F=2.217 tr/F=-0.443 sym=0.696 QK_ER=22.78 QK_s1-4= [0.815 0.63  0.506 0.471] QK_sym_extremes= [-0.386 -0.266  0.331  0.4  ] OV_F=14.878 tr/F=-2.103 OV_ER=34.68 OV_eig= [-1.601+0.191j -1.601-0.191j -1.412+0.278j -1.412-0.278j]\nL2H5 QK_F=2.265 tr/F=-0.118 sym=0.699 QK_ER=30.28 QK_s1-4= [0.615 0.564 0.54  0.513] QK_sym_extremes= [-0.316 -0.276  0.309  0.326] OV_F=16.861 tr/F=-2.058 OV_ER=37.87 OV_eig= [-1.554+0.245j -1.554-0.245j -1.426+0.268j -1.426-0.268j]\nL3H0 QK_F=2.029 tr/F=1.073 sym=0.709 QK_ER=44.28 QK_s1-4= [0.436 0.414 0.39  0.38 ] QK_sym_extremes= [-0.2   -0.194  0.259  0.268] OV_F=20.713 tr/F=-2.223 OV_ER=30.51 OV_eig= [-2.394+0.j    -1.792+0.672j -1.792-0.672j -1.734+0.272j]\nL3H1 QK_F=1.217 tr/F=0.353 sym=0.706 QK_ER=33.91 QK_s1-4= [0.346 0.31  0.285 0.253] QK_sym_extremes= [-0.168 -0.155  0.176  0.197] OV_F=26.506 tr/F=-1.586 OV_ER=23.74 OV_eig= [-2.621+0.132j -2.621-0.132j -2.213+0.j    -1.906+0.658j]\nL3H2 QK_F=1.438 tr/F=0.242 sym=0.703 QK_ER=40.06 QK_s1-4= [0.385 0.304 0.294 0.277] QK_sym_extremes= [-0.186 -0.161  0.176  0.196] OV_F=23.507 tr/F=-1.593 OV_ER=27.26 OV_eig= [-2.363+0.j    -2.12 +0.564j -2.12 -0.564j -1.938+0.j   ]\nL3H3 QK_F=1.819 tr/F=3.200 sym=0.778 QK_ER=53.56 QK_s1-4= [0.322 0.311 0.307 0.304] QK_sym_extremes= [-0.189 -0.165  0.253  0.257] OV_F=37.530 tr/F=-1.210 OV_ER=22.97 OV_eig= [-3.02 +0.433j -3.02 -0.433j -2.93 +0.j    -2.476+1.04j ]\nL3H4 QK_F=0.625 tr/F=0.001 sym=0.708 QK_ER=19.19 QK_s1-4= [0.236 0.203 0.171 0.151] QK_sym_extremes= [-0.113 -0.11   0.108  0.128] OV_F=49.245 tr/F=-0.794 OV_ER=10.30 OV_eig= [-5.486+0.j   -4.383+0.56j -4.383-0.56j -4.333+0.j  ]\nL3H5 QK_F=1.666 tr/F=0.863 sym=0.710 QK_ER=41.68 QK_s1-4= [0.455 0.321 0.315 0.299] QK_sym_extremes= [-0.212 -0.164  0.209  0.24 ] OV_F=22.565 tr/F=-1.534 OV_ER=29.70 OV_eig= [-1.875+0.178j -1.875-0.178j -1.418+0.799j -1.418-0.799j]\n",
  "seconds": 1.391,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "ed74cf179f9f522302a919e16aac7043dbde81a6469a9a7fb9cd63ba184deb8f",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

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

LAYER 0
ln1_w mean=0.29650 sd=0.18837 min=0.03522 max=0.97846 norm=6.8836
ln1_b mean=0.00039 sd=0.03113 min=-0.09542 max=0.09061 norm=0.6101
ln2_w mean=0.87176 sd=0.33816 min=0.04203 max=1.77496 norm=18.3232
ln2_b mean=0.01340 sd=0.06913 min=-0.62361 max=0.46365 norm=1.3799
W_q F=17.6693 singular_top8= [4.3676 3.1308 3.0988 3.0421 2.9941 2.9513 2.9258 2.8812] smedian=0.3184 effective_rank=50.11
W_k F=18.5161 singular_top8= [4.1635 3.9516 3.4672 3.4271 3.3642 3.3348 3.2789 3.2565] smedian=0.3365 effective_rank=46.21
W_v F=14.9367 singular_top8= [5.2084 3.3641 3.0064 2.9623 2.815  2.6389 2.5269 2.4605] smedian=0.3058 effective_rank=31.93
W_o F=16.5495 singular_top8= [7.3608 6.0993 3.1666 2.4518 2.3212 2.2022 2.1709 2.1597] smedian=0.1874 effective_rank=15.33
W_fc F=55.3764 singular_top8= [18.1182 12.7604 11.3373  9.3228  9.2666  9.0207  8.8979  8.8171] smedian=0.9490 effective_rank=36.47
W_proj F=50.0070 singular_top8= [10.899   7.7021  7.1728  6.6377  6.5415  6.2882  6.2009  6.1328] smedian=1.4759 effective_rank=103.70

LAYER 1
ln1_w mean=0.50583 sd=0.08918 min=0.19701 max=1.08462 norm=10.0651
ln1_b mean=0.01103 sd=0.15458 min=-1.56756 max=1.36513 norm=3.0369
ln2_w mean=1.13147 sd=0.17415 min=0.00187 max=1.51965 norm=22.4333
ln2_b mean=0.00647 sd=0.14352 min=-0.38920 max=0.41700 norm=2.8153
W_q F=38.9293 singular_top8= [10.4064  8.2394  7.4754  7.0325  6.6223  6.395   6.275   6.1297] smedian=0.4279 effective_rank=47.70
W_k F=34.8926 singular_top8= [14.4767 11.787   6.3698  6.0538  5.5225  5.4072  5.2682  5.167 ] smedian=0.4020 effective_rank=19.20
W_v F=28.7445 singular_top8= [4.7281 4.5469 4.4588 4.3949 4.3614 4.3317 4.2915 4.276 ] smedian=0.5104 effective_rank=78.15
W_o F=28.4790 singular_top8= [4.0451 3.2675 3.1879 3.1786 3.1633 3.1006 3.0706 3.0465] smedian=0.7287 effective_rank=135.33
W_fc F=80.3794 singular_top8= [19.8292 11.575  11.5215 11.2622 11.059  10.8746 10.6322 10.4972] smedian=1.9520 effective_rank=81.51
W_proj F=77.3844 singular_top8= [9.0375 8.457  8.1027 7.3326 7.3184 7.0875 6.7718 6.666 ] smedian=3.5967 effective_rank=256.84

LAYER 2
ln1_w mean=0.68087 sd=0.06874 min=0.31750 max=1.09272 norm=13.4102
ln1_b mean=0.01305 sd=0.15645 min=-1.65888 max=1.33182 norm=3.0765
ln2_w mean=1.31041 sd=0.11485 min=0.01202 max=1.50783 norm=25.7770
ln2_b mean=0.01229 sd=0.17372 min=-0.51705 max=0.44886 norm=3.4127
W_q F=38.9025 singular_top8= [5.9663 5.0189 4.865  4.7735 4.6954 4.5676 4.4963 4.4331] smedian=1.3420 effective_rank=141.54
W_k F=36.7500 singular_top8= [9.8633 9.5331 4.3864 4.1715 4.1168 3.9556 3.8793 3.7772] smedian=1.2369 effective_rank=69.01
W_v F=33.1825 singular_top8= [3.9143 3.78   3.727  3.6622 3.5821 3.5534 3.5099 3.4637] smedian=1.2014 effective_rank=162.37
W_o F=32.4345 singular_top8= [4.3982 4.0371 3.8746 3.739  3.621  3.5889 3.4883 3.4674] smedian=1.1896 effective_rank=160.76
W_fc F=79.8282 singular_top8= [15.7338  9.2489  8.718   8.6937  8.4705  7.865   7.7393  7.4819] smedian=3.3774 effective_rank=172.27
W_proj F=78.0482 singular_top8= [10.1974  9.0347  8.8821  8.4108  8.2242  8.0885  7.9876  7.8273] smedian=3.4490 effective_rank=219.46

LAYER 3
ln1_w mean=0.70875 sd=0.04941 min=0.40780 max=0.81546 norm=13.9223
ln1_b mean=0.02963 sd=0.09939 min=-0.76388 max=0.82412 norm=2.0324
ln2_w mean=1.26681 sd=0.08945 min=0.23280 max=1.45324 norm=24.8860
ln2_b mean=0.01280 sd=0.14128 min=-0.54017 max=0.39317 norm=2.7798
W_q F=33.2388 singular_top8= [4.0862 3.9326 3.8093 3.7155 3.6803 3.6334 3.5712 3.5556] smedian=1.2181 effective_rank=160.41
W_k F=32.4633 singular_top8= [6.3618 5.8423 3.6963 3.4585 3.3626 3.304  3.2563 3.2439] smedian=1.2120 effective_rank=132.45
W_v F=39.4218 singular_top8= [5.6601 5.4237 5.1815 5.1585 5.0739 5.0075 4.829  4.6848] smedian=1.3079 effective_rank=133.02
W_o F=40.1331 singular_top8= [10.4767  7.3569  5.3233  5.2944  5.177   5.1238  5.0488  4.9589] smedian=1.2119 effective_rank=81.34
W_fc F=78.2595 singular_top8= [15.1141  8.6112  8.486   8.0732  7.7689  7.5291  7.4493  7.3342] smedian=3.5182 effective_rank=194.49
W_proj F=85.1194 singular_top8= [28.4613 22.2137 21.3397 20.708  19.9429 19.6618 18.9864 18.744 ] smedian=0.8176 effective_rank=20.80

HEADS: QK=(Wq D)^T(Wk D)/8; OV=Wo Wv D; D=diag(ln1_w)
tr/F is signed trace divided by Frobenius norm; sym is symmetric-part norm/F.
L0H0 QK_F=0.294 tr/F=-0.357 sym=0.755 QK_ER=1.90 QK_s1-4= [0.248 0.102 0.047 0.043] QK_sym_extremes= [-0.073 -0.035  0.043  0.182] OV_F=2.128 tr/F=-0.067 OV_ER=8.58 OV_eig= [-0.086+0.034j -0.086-0.034j -0.057+0.048j -0.057-0.048j]
L0H1 QK_F=0.159 tr/F=-0.620 sym=0.762 QK_ER=1.25 QK_s1-4= [0.15  0.042 0.021 0.012] QK_sym_extremes= [-0.106 -0.024  0.019  0.045] OV_F=3.979 tr/F=0.231 OV_ER=1.70 OV_eig= [ 1.386+0.j    -0.245+0.j     0.012+0.045j  0.012-0.045j]
L0H2 QK_F=0.107 tr/F=-0.814 sym=0.803 QK_ER=1.21 QK_s1-4= [0.102 0.018 0.016 0.012] QK_sym_extremes= [-0.079 -0.012  0.005  0.025] OV_F=3.033 tr/F=0.229 OV_ER=1.62 OV_eig= [ 0.954+0.j    -0.118+0.j     0.05 +0.041j  0.05 -0.041j]
L0H3 QK_F=0.699 tr/F=1.923 sym=0.683 QK_ER=17.06 QK_s1-4= [0.244 0.223 0.201 0.182] QK_sym_extremes= [-0.078 -0.056  0.17   0.186] OV_F=3.536 tr/F=-0.570 OV_ER=19.72 OV_eig= [-0.001+0.207j -0.001-0.207j -0.085+0.183j -0.085-0.183j]
L0H4 QK_F=0.089 tr/F=-0.519 sym=0.725 QK_ER=1.52 QK_s1-4= [0.08  0.022 0.019 0.013] QK_sym_extremes= [-0.047 -0.016  0.007  0.033] OV_F=1.684 tr/F=0.102 OV_ER=1.70 OV_eig= [ 0.501+0.j   -0.061+0.j   -0.046+0.j   -0.033+0.02j]
L0H5 QK_F=0.034 tr/F=-0.254 sym=0.716 QK_ER=1.23 QK_s1-4= [0.032 0.004 0.003 0.003] QK_sym_extremes= [-0.018 -0.002  0.003  0.014] OV_F=18.820 tr/F=0.111 OV_ER=1.64 OV_eig= [ 0.554+1.534j  0.554-1.534j  1.168+0.j    -0.395+0.j   ]
L1H0 QK_F=1.981 tr/F=-0.883 sym=0.722 QK_ER=5.90 QK_s1-4= [1.185 0.729 0.557 0.534] QK_sym_extremes= [-0.585 -0.386  0.402  0.601] OV_F=11.481 tr/F=-1.346 OV_ER=24.55 OV_eig= [-1.071+0.031j -1.071-0.031j -0.967+0.207j -0.967-0.207j]
L1H1 QK_F=1.846 tr/F=-0.845 sym=0.713 QK_ER=5.08 QK_s1-4= [1.155 0.715 0.583 0.465] QK_sym_extremes= [-0.575 -0.336  0.381  0.577] OV_F=11.266 tr/F=-1.351 OV_ER=22.13 OV_eig= [-1.056+0.085j -1.056-0.085j -0.818+0.25j  -0.818-0.25j ]
L1H2 QK_F=1.783 tr/F=-1.059 sym=0.720 QK_ER=4.53 QK_s1-4= [1.176 0.62  0.485 0.436] QK_sym_extremes= [-0.598 -0.362  0.326  0.577] OV_F=11.810 tr/F=-1.467 OV_ER=20.46 OV_eig= [-1.307+0.j    -1.2  +0.096j -1.2  -0.096j -0.986+0.026j]
L1H3 QK_F=1.791 tr/F=-0.844 sym=0.709 QK_ER=4.34 QK_s1-4= [1.188 0.678 0.532 0.388] QK_sym_extremes= [-0.595 -0.304  0.346  0.592] OV_F=12.401 tr/F=-1.481 OV_ER=22.76 OV_eig= [-1.215+0.244j -1.215-0.244j -1.162+0.j    -0.93 +0.326j]
L1H4 QK_F=1.630 tr/F=-1.234 sym=0.723 QK_ER=4.86 QK_s1-4= [1.059 0.505 0.477 0.351] QK_sym_extremes= [-0.506 -0.276  0.279  0.557] OV_F=13.401 tr/F=-1.429 OV_ER=20.93 OV_eig= [-1.499+0.j    -1.365+0.05j  -1.365-0.05j  -1.009+0.162j]
L1H5 QK_F=1.867 tr/F=-0.874 sym=0.713 QK_ER=5.37 QK_s1-4= [1.147 0.719 0.556 0.499] QK_sym_extremes= [-0.607 -0.383  0.377  0.558] OV_F=10.921 tr/F=-1.369 OV_ER=21.74 OV_eig= [-1.112+0.j    -0.919+0.j    -0.875+0.104j -0.875-0.104j]
L2H0 QK_F=1.635 tr/F=-0.954 sym=0.693 QK_ER=16.43 QK_s1-4= [0.694 0.555 0.35  0.328] QK_sym_extremes= [-0.303 -0.233  0.269  0.342] OV_F=16.382 tr/F=-1.027 OV_ER=24.63 OV_eig= [-1.786+0.j    -1.284+0.369j -1.284-0.369j -1.149+0.072j]
L2H1 QK_F=2.155 tr/F=-0.100 sym=0.700 QK_ER=31.47 QK_s1-4= [0.578 0.514 0.509 0.466] QK_sym_extremes= [-0.289 -0.279  0.293  0.303] OV_F=17.620 tr/F=-1.995 OV_ER=36.57 OV_eig= [-1.758+0.j    -1.526+0.297j -1.526-0.297j -1.438+0.j   ]
L2H2 QK_F=1.721 tr/F=-0.754 sym=0.706 QK_ER=31.27 QK_s1-4= [0.518 0.412 0.4   0.374] QK_sym_extremes= [-0.246 -0.231  0.213  0.273] OV_F=18.665 tr/F=-1.604 OV_ER=29.47 OV_eig= [-2.375+0.j    -1.782+0.329j -1.782-0.329j -1.811+0.j   ]
L2H3 QK_F=1.624 tr/F=-0.367 sym=0.704 QK_ER=30.99 QK_s1-4= [0.503 0.385 0.364 0.334] QK_sym_extremes= [-0.232 -0.212  0.198  0.273] OV_F=19.000 tr/F=-1.590 OV_ER=32.89 OV_eig= [-1.936+0.231j -1.936-0.231j -1.686+0.j    -1.338+0.j   ]
L2H4 QK_F=2.217 tr/F=-0.443 sym=0.696 QK_ER=22.78 QK_s1-4= [0.815 0.63  0.506 0.471] QK_sym_extremes= [-0.386 -0.266  0.331  0.4  ] OV_F=14.878 tr/F=-2.103 OV_ER=34.68 OV_eig= [-1.601+0.191j -1.601-0.191j -1.412+0.278j -1.412-0.278j]
L2H5 QK_F=2.265 tr/F=-0.118 sym=0.699 QK_ER=30.28 QK_s1-4= [0.615 0.564 0.54  0.513] QK_sym_extremes= [-0.316 -0.276  0.309  0.326] OV_F=16.861 tr/F=-2.058 OV_ER=37.87 OV_eig= [-1.554+0.245j -1.554-0.245j -1.426+0.268j -1.426-0.268j]
L3H0 QK_F=2.029 tr/F=1.073 sym=0.709 QK_ER=44.28 QK_s1-4= [0.436 0.414 0.39  0.38 ] QK_sym_extremes= [-0.2   -0.194  0.259  0.268] OV_F=20.713 tr/F=-2.223 OV_ER=30.51 OV_eig= [-2.394+0.j    -1.792+0.672j -1.792-0.672j -1.734+0.272j]
L3H1 QK_F=1.217 tr/F=0.353 sym=0.706 QK_ER=33.91 QK_s1-4= [0.346 0.31  0.285 0.253] QK_sym_extremes= [-0.168 -0.155  0.176  0.197] OV_F=26.506 tr/F=-1.586 OV_ER=23.74 OV_eig= [-2.621+0.132j -2.621-0.132j -2.213+0.j    -1.906+0.658j]
L3H2 QK_F=1.438 tr/F=0.242 sym=0.703 QK_ER=40.06 QK_s1-4= [0.385 0.304 0.294 0.277] QK_sym_extremes= [-0.186 -0.161  0.176  0.196] OV_F=23.507 tr/F=-1.593 OV_ER=27.26 OV_eig= [-2.363+0.j    -2.12 +0.564j -2.12 -0.564j -1.938+0.j   ]
L3H3 QK_F=1.819 tr/F=3.200 sym=0.778 QK_ER=53.56 QK_s1-4= [0.322 0.311 0.307 0.304] QK_sym_extremes= [-0.189 -0.165  0.253  0.257] OV_F=37.530 tr/F=-1.210 OV_ER=22.97 OV_eig= [-3.02 +0.433j -3.02 -0.433j -2.93 +0.j    -2.476+1.04j ]
L3H4 QK_F=0.625 tr/F=0.001 sym=0.708 QK_ER=19.19 QK_s1-4= [0.236 0.203 0.171 0.151] QK_sym_extremes= [-0.113 -0.11   0.108  0.128] OV_F=49.245 tr/F=-0.794 OV_ER=10.30 OV_eig= [-5.486+0.j   -4.383+0.56j -4.383-0.56j -4.333+0.j  ]
L3H5 QK_F=1.666 tr/F=0.863 sym=0.710 QK_ER=41.68 QK_s1-4= [0.455 0.321 0.315 0.299] QK_sym_extremes= [-0.212 -0.164  0.209  0.24 ] OV_F=22.565 tr/F=-1.534 OV_ER=29.70 OV_eig= [-1.875+0.178j -1.875-0.178j -1.418+0.799j -1.418-0.799j]


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

LAYER 0
ln1_w mean=0.29650 sd=0.18837 min=0.03522 max=0.97846 norm=6.8836
ln1_b mean=0.00039 sd=0.03113 min=-0.09542 max=0.09061 norm=0.6101
ln2_w mean=0.87176 sd=0.33816 min=0.04203 max=1.77496 norm=18.3232
ln2_b mean=0.01340 sd=0.06913 min=-0.62361 max=0.46365 norm=1.3799
W_q F=17.6693 singular_top8= [4.3676 3.1308 3.0988 3.0421 2.9941 2.9513 2.9258 2.8812] smedian=0.3184 effective_rank=50.11
W_k F=18.5161 singular_top8= [4.1635 3.9516 3.4672 3.4271 3.3642 3.3348 3.2789 3.2565] smedian=0.3365 effective_rank=46.21
W_v F=14.9367 singular_top8= [5.2084 3.3641 3.0064 2.9623 2.815  2.6389 2.5269 2.4605] smedian=0.3058 effective_rank=31.93
W_o F=16.5495 singular_top8= [7.3608 6.0993 3.1666 2.4518 2.3212 2.2022 2.1709 2.1597] smedian=0.1874 effective_rank=15.33
W_fc F=55.3764 singular_top8= [18.1182 12.7604 11.3373  9.3228  9.2666  9.0207  8.8979  8.8171] smedian=0.9490 effective_rank=36.47
W_proj F=50.0070 singular_top8= [10.899   7.7021  7.1728  6.6377  6.5415  6.2882  6.2009  6.1328] smedian=1.4759 effective_rank=103.70

LAYER 1
ln1_w mean=0.50583 sd=0.08918 min=0.19701 max=1.08462 norm=10.0651
ln1_b mean=0.01103 sd=0.15458 min=-1.56756 max=1.36513 norm=3.0369
ln2_w mean=1.13147 sd=0.17415 min=0.00187 max=1.51965 norm=22.4333
ln2_b mean=0.00647 sd=0.14352 min=-0.38920 max=0.41700 norm=2.8153
W_q F=38.9293 singular_top8= [10.4064  8.2394  7.4754  7.0325  6.6223  6.395   6.275   6.1297] smedian=0.4279 effective_rank=47.70
W_k F=34.8926 singular_top8= [14.4767 11.787   6.3698  6.0538  5.5225  5.4072  5.2682  5.167 ] smedian=0.4020 effective_rank=19.20
W_v F=28.7445 singular_top8= [4.7281 4.5469 4.4588 4.3949 4.3614 4.3317 4.2915 4.276 ] smedian=0.5104 effective_rank=78.15
W_o F=28.4790 singular_top8= [4.0451 3.2675 3.1879 3.1786 3.1633 3.1006 3.0706 3.0465] smedian=0.7287 effective_rank=135.33
W_fc F=80.3794 singular_top8= [19.8292 11.575  11.5215 11.2622 11.059  10.8746 10.6322 10.4972] smedian=1.9520 effective_rank=81.51
W_proj F=77.3844 singular_top8= [9.0375 8.457  8.1027 7.3326 7.3184 7.0875 6.7718 6.666 ] smedian=3.5967 effective_rank=256.84

LAYER 2
ln1_w mean=0.68087 sd=0.06874 min=0.31750 max=1.09272 norm=13.4102
ln1_b mean=0.01305 sd=0.15645 min=-1.65888 max=1.33182 norm=3.0765
ln2_w mean=1.31041 sd=0.11485 min=0.01202 max=1.50783 norm=25.7770
ln2_b mean=0.01229 sd=0.17372 min=-0.51705 max=0.44886 norm=3.4127
W_q F=38.9025 singular_top8= [5.9663 5.0189 4.865  4.7735 4.6954 4.5676 4.4963 4.4331] smedian=1.3420 effective_rank=141.54
W_k F=36.7500 singular_top8= [9.8633 9.5331 4.3864 4.1715 4.1168 3.9556 3.8793 3.7772] smedian=1.2369 effective_rank=69.01
W_v F=33.1825 singular_top8= [3.9143 3.78   3.727  3.6622 3.5821 3.5534 3.5099 3.4637] smedian=1.2014 effective_rank=162.37
W_o F=32.4345 singular_top8= [4.3982 4.0371 3.8746 3.739  3.621  3.5889 3.4883 3.4674] smedian=1.1896 effective_rank=160.76
W_fc F=79.8282 singular_top8= [15.7338  9.2489  8.718   8.6937  8.4705  7.865   7.7393  7.4819] smedian=3.3774 effective_rank=172.27
W_proj F=78.0482 singular_top8= [10.1974  9.0347  8.8821  8.4108  8.2242  8.0885  7.9876  7.8273] smedian=3.4490 effective_rank=219.46

LAYER 3
ln1_w mean=0.70875 sd=0.04941 min=0.40780 max=0.81546 norm=13.9223
ln1_b mean=0.02963 sd=0.09939 min=-0.76388 max=0.82412 norm=2.0324
ln2_w mean=1.26681 sd=0.08945 min=0.23280 max=1.45324 norm=24.8860
ln2_b mean=0.01280 sd=0.14128 min=-0.54017 max=0.39317 norm=2.7798
W_q F=33.2388 singular_top8= [4.0862 3.9326 3.8093 3.7155 3.6803 3.6334 3.5712 3.5556] smedian=1.2181 effective_rank=160.41
W_k F=32.4633 singular_top8= [6.3618 5.8423 3.6963 3.4585 3.3626 3.304  3.2563 3.2439] smedian=1.2120 effective_rank=132.45
W_v F=39.4218 singular_top8= [5.6601 5.4237 5.1815 5.1585 5.0739 5.0075 4.829  4.6848] smedian=1.3079 effective_rank=133.02
W_o F=40.1331 singular_top8= [10.4767  7.3569  5.3233  5.2944  5.177   5.1238  5.0488  4.9589] smedian=1.2119 effective_rank=81.34
W_fc F=78.2595 singular_top8= [15.1141  8.6112  8.486   8.0732  7.7689  7.5291  7.4493  7.3342] smedian=3.5182 effective_rank=194.49
W_proj F=85.1194 singular_top8= [28.4613 22.2137 21.3397 20.708  19.9429 19.6618 18.9864 18.744 ] smedian=0.8176 effective_rank=20.80

HEADS: QK=(Wq D)^T(Wk D)/8; OV=Wo Wv D; D=diag(ln1_w)
tr/F is signed trace divided by Frobenius norm; sym is symmetric-part norm/F.
L0H0 QK_F=0.294 tr/F=-0.357 sym=0.755 QK_ER=1.90 QK_s1-4= [0.248 0.102 0.047 0.043] QK_sym_extremes= [-0.073 -0.035  0.043  0.182] OV_F=2.128 tr/F=-0.067 OV_ER=8.58 OV_eig= [-0.086+0.034j -0.086-0.034j -0.057+0.048j -0.057-0.048j]
L0H1 QK_F=0.159 tr/F=-0.620 sym=0.762 QK_ER=1.25 QK_s1-4= [0.15  0.042 0.021 0.012] QK_sym_extremes= [-0.106 -0.024  0.019  0.045] OV_F=3.979 tr/F=0.231 OV_ER=1.70 OV_eig= [ 1.386+0.j    -0.245+0.j     0.012+0.045j  0.012-0.045j]
L0H2 QK_F=0.107 tr/F=-0.814 sym=0.803 QK_ER=1.21 QK_s1-4= [0.102 0.018 0.016 0.012] QK_sym_extremes= [-0.079 -0.012  0.005  0.025] OV_F=3.033 tr/F=0.229 OV_ER=1.62 OV_eig= [ 0.954+0.j    -0.118+0.j     0.05 +0.041j  0.05 -0.041j]
L0H3 QK_F=0.699 tr/F=1.923 sym=0.683 QK_ER=17.06 QK_s1-4= [0.244 0.223 0.201 0.182] QK_sym_extremes= [-0.078 -0.056  0.17   0.186] OV_F=3.536 tr/F=-0.570 OV_ER=19.72 OV_eig= [-0.001+0.207j -0.001-0.207j -0.085+0.183j -0.085-0.183j]
L0H4 QK_F=0.089 tr/F=-0.519 sym=0.725 QK_ER=1.52 QK_s1-4= [0.08  0.022 0.019 0.013] QK_sym_extremes= [-0.047 -0.016  0.007  0.033] OV_F=1.684 tr/F=0.102 OV_ER=1.70 OV_eig= [ 0.501+0.j   -0.061+0.j   -0.046+0.j   -0.033+0.02j]
L0H5 QK_F=0.034 tr/F=-0.254 sym=0.716 QK_ER=1.23 QK_s1-4= [0.032 0.004 0.003 0.003] QK_sym_extremes= [-0.018 -0.002  0.003  0.014] OV_F=18.820 tr/F=0.111 OV_ER=1.64 OV_eig= [ 0.554+1.534j  0.554-1.534j  1.168+0.j    -0.395+0.j   ]
L1H0 QK_F=1.981 tr/F=-0.883 sym=0.722 QK_ER=5.90 QK_s1-4= [1.185 0.729 0.557 0.534] QK_sym_extremes= [-0.585 -0.386  0.402  0.601] OV_F=11.481 tr/F=-1.346 OV_ER=24.55 OV_eig= [-1.071+0.031j -1.071-0.031j -0.967+0.207j -0.967-0.207j]
L1H1 QK_F=1.846 tr/F=-0.845 sym=0.713 QK_ER=5.08 QK_s1-4= [1.155 0.715 0.583 0.465] QK_sym_extremes= [-0.575 -0.336  0.381  0.577] OV_F=11.266 tr/F=-1.351 OV_ER=22.13 OV_eig= [-1.056+0.085j -1.056-0.085j -0.818+0.25j  -0.818-0.25j ]
L1H2 QK_F=1.783 tr/F=-1.059 sym=0.720 QK_ER=4.53 QK_s1-4= [1.176 0.62  0.485 0.436] QK_sym_extremes= [-0.598 -0.362  0.326  0.577] OV_F=11.810 tr/F=-1.467 OV_ER=20.46 OV_eig= [-1.307+0.j    -1.2  +0.096j -1.2  -0.096j -0.986+0.026j]
L1H3 QK_F=1.791 tr/F=-0.844 sym=0.709 QK_ER=4.34 QK_s1-4= [1.188 0.678 0.532 0.388] QK_sym_extremes= [-0.595 -0.304  0.346  0.592] OV_F=12.401 tr/F=-1.481 OV_ER=22.76 OV_eig= [-1.215+0.244j -1.215-0.244j -1.162+0.j    -0.93 +0.326j]
L1H4 QK_F=1.630 tr/F=-1.234 sym=0.723 QK_ER=4.86 QK_s1-4= [1.059 0.505 0.477 0.351] QK_sym_extremes= [-0.506 -0.276  0.279  0.557] OV_F=13.401 tr/F=-1.429 OV_ER=20.93 OV_eig= [-1.499+0.j    -1.365+0.05j  -1.365-0.05j  -1.009+0.162j]
L1H5 QK_F=1.867 tr/F=-0.874 sym=0.713 QK_ER=5.37 QK_s1-4= [1.147 0.719 0.556 0.499] QK_sym_extremes= [-0.607 -0.383  0.377  0.558] OV_F=10.921 tr/F=-1.369 OV_ER=21.74 OV_eig= [-1.112+0.j    -0.919+0.j    -0.875+0.104j -0.875-0.104j]
L2H0 QK_F=1.635 tr/F=-0.954 sym=0.693 QK_ER=16.43 QK_s1-4= [0.694 0.555 0.35  0.328] QK_sym_extremes= [-0.303 -0.233  0.269  0.342] OV_F=16.382 tr/F=-1.027 OV_ER=24.63 OV_eig= [-1.786+0.j    -1.284+0.369j -1.284-0.369j -1.149+0.072j]
L2H1 QK_F=2.155 tr/F=-0.100 sym=0.700 QK_ER=31.47 QK_s1-4= [0.578 0.514 0.509 0.466] QK_sym_extremes= [-0.289 -0.279  0.293  0.303] OV_F=17.620 tr/F=-1.995 OV_ER=36.57 OV_eig= [-1.758+0.j    -1.526+0.297j -1.526-0.297j -1.438+0.j   ]
L2H2 QK_F=1.721 tr/F=-0.754 sym=0.706 QK_ER=31.27 QK_s1-4= [0.518 0.412 0.4   0.374] QK_sym_extremes= [-0.246 -0.231  0.213  0.273] OV_F=18.665 tr/F=-1.604 OV_ER=29.47 OV_eig= [-2.375+0.j    -1.782+0.329j -1.782-0.329j -1.811+0.j   ]
L2H3 QK_F=1.624 tr/F=-0.367 sym=0.704 QK_ER=30.99 QK_s1-4= [0.503 0.385 0.364 0.334] QK_sym_extremes= [-0.232 -0.212  0.198  0.273] OV_F=19.000 tr/F=-1.590 OV_ER=32.89 OV_eig= [-1.936+0.231j -1.936-0.231j -1.686+0.j    -1.338+0.j   ]
L2H4 QK_F=2.217 tr/F=-0.443 sym=0.696 QK_ER=22.78 QK_s1-4= [0.815 0.63  0.506 0.471] QK_sym_extremes= [-0.386 -0.266  0.331  0.4  ] OV_F=14.878 tr/F=-2.103 OV_ER=34.68 OV_eig= [-1.601+0.191j -1.601-0.191j -1.412+0.278j -1.412-0.278j]
L2H5 QK_F=2.265 tr/F=-0.118 sym=0.699 QK_ER=30.28 QK_s1-4= [0.615 0.564 0.54  0.513] QK_sym_extremes= [-0.316 -0.276  0.309  0.326] OV_F=16.861 tr/F=-2.058 OV_ER=37.87 OV_eig= [-1.554+0.245j -1.554-0.245j -1.426+0.268j -1.426-0.268j]
L3H0 QK_F=2.029 tr/F=1.073 sym=0.709 QK_ER=44.28 QK_s1-4= [0.436 0.414 0.39  0.38 ] QK_sym_extremes= [-0.2   -0.194  0.259  0.268] OV_F=20.713 tr/F=-2.223 OV_ER=30.51 OV_eig= [-2.394+0.j    -1.792+0.672j -1.792-0.672j -1.734+0.272j]
L3H1 QK_F=1.217 tr/F=0.353 sym=0.706 QK_ER=33.91 QK_s1-4= [0.346 0.31  0.285 0.253] QK_sym_extremes= [-0.168 -0.155  0.176  0.197] OV_F=26.506 tr/F=-1.586 OV_ER=23.74 OV_eig= [-2.621+0.132j -2.621-0.132j -2.213+0.j    -1.906+0.658j]
L3H2 QK_F=1.438 tr/F=0.242 sym=0.703 QK_ER=40.06 QK_s1-4= [0.385 0.304 0.294 0.277] QK_sym_extremes= [-0.186 -0.161  0.176  0.196] OV_F=23.507 tr/F=-1.593 OV_ER=27.26 OV_eig= [-2.363+0.j    -2.12 +0.564j -2.12 -0.564j -1.938+0.j   ]
L3H3 QK_F=1.819 tr/F=3.200 sym=0.778 QK_ER=53.56 QK_s1-4= [0.322 0.311 0.307 0.304] QK_sym_extremes= [-0.189 -0.165  0.253  0.257] OV_F=37.530 tr/F=-1.210 OV_ER=22.97 OV_eig= [-3.02 +0.433j -3.02 -0.433j -2.93 +0.j    -2.476+1.04j ]
L3H4 QK_F=0.625 tr/F=0.001 sym=0.708 QK_ER=19.19 QK_s1-4= [0.236 0.203 0.171 0.151] QK_sym_extremes= [-0.113 -0.11   0.108  0.128] OV_F=49.245 tr/F=-0.794 OV_ER=10.30 OV_eig= [-5.486+0.j   -4.383+0.56j -4.383-0.56j -4.333+0.j  ]
L3H5 QK_F=1.666 tr/F=0.863 sym=0.710 QK_ER=41.68 QK_s1-4= [0.455 0.321 0.315 0.299] QK_sym_extremes= [-0.212 -0.164  0.209  0.24 ] OV_F=22.565 tr/F=-1.534 OV_ER=29.70 OV_eig= [-1.875+0.178j -1.875-0.178j -1.418+0.799j -1.418-0.799j]

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

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

AGENT_MESSAGE
```python
import numpy as np
from threadpoolctl import threadpool_limits
threadpool_limits(2)
np.set_printoptions(precision=3, suppress=True, linewidth=180)
D = 384
A, B = {}, {}
for l in range(4):
    g = W[f"ln1_w.{l}"].astype(float)
    for j in range(6):
        s = slice(64*j,64*j+64)
        q = W[f"W_q.{l}"][s].astype(float)
        k = W[f"W_k.{l}"][s].astype(float)
        v = W[f"W_v.{l}"][s].astype(float)
        o = W[f"W_o.{l}"][:,s].astype(float)
        A[l,j] = (q*g).T @ (k*g) / 8
        B[l,j] = o @ (v*g)

def cosine(x,y):
    return np.sum(x*y)/(np.linalg.norm(x)*np.linalg.norm(y))

print("WITHIN-LAYER QK COSINES, THEN OV COSINES")
for l in range(4):
    print("L",l,"QK")
    print(np.array([[cosine(A[l,i],A[l,j]) for j in range(6)] for i in range(6)]))
    print("OV")
    print(np.array([[cosine(B[l,i],B[l,j]) for j in range(6)] for i in range(6)]))

print("\nL0 WEIGHT-ONLY EIGENSPECTRA AND ALIGNMENTS")
for j in range(6):
    s = slice(64*j,64*j+64)
    q = W["W_q.0"][s].astype(float)
    k = W["W_k.0"][s].astype(float)
    g = W["ln1_w.0"].astype(float)
    for label,q1,k1 in [("raw",q,k),("gain",q*g,k*g)]:
        ev = np.linalg.eigvals(k1 @ q1.T / 8)
        ev = ev[np.argsort(abs(ev))[::-1]]
        sv = np.linalg.svd(q1.T @ k1 / 8,compute_uv=False)
        print(f"H{j} {label}: eig_top12={ev[:12]}, sv_top8={sv[:8]}")
    u,sv,vt=np.linalg.svd(A[0,j],full_matrices=False)
    print("top left/right cos",np.round(np.diag(vt[:5]@u[:,:5]),3))
    print("top left/right coordinate concentration",
          np.round(np.max(u[:,:5]**2,axis=0),3),
          np.round(np.max(vt[:5]**2,axis=1),3))

print("\nL0 QK TOP-DIRECTION COSINES (signed arbitrary; absolute values shown)")
us,vs=[],[]
for j in range(6):
    u,s,vt=np.linalg.svd(A[0,j],full_matrices=False)
    us.append(u[:,0]); vs.append(vt[0])
print("left",np.abs(np.array(us)@np.array(us).T))
print("right",np.abs(np.array(vs)@np.array(vs).T))

print("\nATTENTION AFFINE PARAMETER NORMS (no activations)")
for l in range(4):
    g=W[f"ln1_w.{l}"].astype(float)
    b=W[f"ln1_b.{l}"].astype(float)
    for j in range(6):
        s=slice(64*j,64*j+64)
        q=W[f"W_q.{l}"][s].astype(float)
        k=W[f"W_k.{l}"][s].astype(float)
        qb=q@b; kb=k@b
        key_linear=(k*g).T@qb/8
        query_linear=(q*g).T@kb/8
        print(f"L{l}H{j}: ||qD||={np.linalg.norm(q*g):.3f} ||kD||={np.linalg.norm(k*g):.3f}",
              f"||qb||={np.linalg.norm(qb):.3f} ||kb||={np.linalg.norm(kb):.3f}",
              f"||key_linear||={np.linalg.norm(key_linear):.3f} ||query_linear||={np.linalg.norm(query_linear):.3f}")

print("\nKEY-SIDE COMPOSITION: trace(A_l B_earlier)/||A_l B_earlier||_F")
print("These are matrix-path diagnostics, not observed attention or induction.")
for l in range(1,4):
    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.linalg.norm(c),m,h,np.linalg.norm(c)))
        vals.sort(reverse=True)
        print(f"L{l}H{j}",[(round(v,3),f"L{m}H{h}",round(n,3)) for v,m,h,n in vals[:5]])
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 2,
  "turn": "t002",
  "code": "import numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=3, suppress=True, linewidth=180)\nD = 384\nA, B = {}, {}\nfor l in range(4):\n    g = W[f\"ln1_w.{l}\"].astype(float)\n    for j in range(6):\n        s = slice(64*j,64*j+64)\n        q = W[f\"W_q.{l}\"][s].astype(float)\n        k = W[f\"W_k.{l}\"][s].astype(float)\n        v = W[f\"W_v.{l}\"][s].astype(float)\n        o = W[f\"W_o.{l}\"][:,s].astype(float)\n        A[l,j] = (q*g).T @ (k*g) / 8\n        B[l,j] = o @ (v*g)\n\ndef cosine(x,y):\n    return np.sum(x*y)/(np.linalg.norm(x)*np.linalg.norm(y))\n\nprint(\"WITHIN-LAYER QK COSINES, THEN OV COSINES\")\nfor l in range(4):\n    print(\"L\",l,\"QK\")\n    print(np.array([[cosine(A[l,i],A[l,j]) for j in range(6)] for i in range(6)]))\n    print(\"OV\")\n    print(np.array([[cosine(B[l,i],B[l,j]) for j in range(6)] for i in range(6)]))\n\nprint(\"\\nL0 WEIGHT-ONLY EIGENSPECTRA AND ALIGNMENTS\")\nfor j in range(6):\n    s = slice(64*j,64*j+64)\n    q = W[\"W_q.0\"][s].astype(float)\n    k = W[\"W_k.0\"][s].astype(float)\n    g = W[\"ln1_w.0\"].astype(float)\n    for label,q1,k1 in [(\"raw\",q,k),(\"gain\",q*g,k*g)]:\n        ev = np.linalg.eigvals(k1 @ q1.T / 8)\n        ev = ev[np.argsort(abs(ev))[::-1]]\n        sv = np.linalg.svd(q1.T @ k1 / 8,compute_uv=False)\n        print(f\"H{j} {label}: eig_top12={ev[:12]}, sv_top8={sv[:8]}\")\n    u,sv,vt=np.linalg.svd(A[0,j],full_matrices=False)\n    print(\"top left/right cos\",np.round(np.diag(vt[:5]@u[:,:5]),3))\n    print(\"top left/right coordinate concentration\",\n          np.round(np.max(u[:,:5]**2,axis=0),3),\n          np.round(np.max(vt[:5]**2,axis=1),3))\n\nprint(\"\\nL0 QK TOP-DIRECTION COSINES (signed arbitrary; absolute values shown)\")\nus,vs=[],[]\nfor j in range(6):\n    u,s,vt=np.linalg.svd(A[0,j],full_matrices=False)\n    us.append(u[:,0]); vs.append(vt[0])\nprint(\"left\",np.abs(np.array(us)@np.array(us).T))\nprint(\"right\",np.abs(np.array(vs)@np.array(vs).T))\n\nprint(\"\\nATTENTION AFFINE PARAMETER NORMS (no activations)\")\nfor l in range(4):\n    g=W[f\"ln1_w.{l}\"].astype(float)\n    b=W[f\"ln1_b.{l}\"].astype(float)\n    for j in range(6):\n        s=slice(64*j,64*j+64)\n        q=W[f\"W_q.{l}\"][s].astype(float)\n        k=W[f\"W_k.{l}\"][s].astype(float)\n        qb=q@b; kb=k@b\n        key_linear=(k*g).T@qb/8\n        query_linear=(q*g).T@kb/8\n        print(f\"L{l}H{j}: ||qD||={np.linalg.norm(q*g):.3f} ||kD||={np.linalg.norm(k*g):.3f}\",\n              f\"||qb||={np.linalg.norm(qb):.3f} ||kb||={np.linalg.norm(kb):.3f}\",\n              f\"||key_linear||={np.linalg.norm(key_linear):.3f} ||query_linear||={np.linalg.norm(query_linear):.3f}\")\n\nprint(\"\\nKEY-SIDE COMPOSITION: trace(A_l B_earlier)/||A_l B_earlier||_F\")\nprint(\"These are matrix-path diagnostics, not observed attention or induction.\")\nfor l in range(1,4):\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.linalg.norm(c),m,h,np.linalg.norm(c)))\n        vals.sort(reverse=True)\n        print(f\"L{l}H{j}\",[(round(v,3),f\"L{m}H{h}\",round(n,3)) for v,m,h,n in vals[:5]])",
  "out": "WITHIN-LAYER QK COSINES, THEN OV COSINES\nL 0 QK\n[[ 1.     0.012  0.007  0.137  0.013  0.044]\n [ 0.012  1.     0.69  -0.026  0.57   0.026]\n [ 0.007  0.69   1.    -0.063  0.492 -0.075]\n [ 0.137 -0.026 -0.063  1.    -0.044  0.01 ]\n [ 0.013  0.57   0.492 -0.044  1.     0.033]\n [ 0.044  0.026 -0.075  0.01   0.033  1.   ]]\nOV\n[[ 1.     0.018  0.017 -0.035  0.026  0.018]\n [ 0.018  1.     0.925  0.006  0.868  0.643]\n [ 0.017  0.925  1.     0.009  0.81   0.692]\n [-0.035  0.006  0.009  1.     0.011 -0.001]\n [ 0.026  0.868  0.81   0.011  1.     0.592]\n [ 0.018  0.643  0.692 -0.001  0.592  1.   ]]\nL 1 QK\n[[1.    0.358 0.373 0.346 0.334 0.377]\n [0.358 1.    0.372 0.362 0.302 0.399]\n [0.373 0.372 1.    0.368 0.375 0.352]\n [0.346 0.362 0.368 1.    0.32  0.396]\n [0.334 0.302 0.375 0.32  1.    0.289]\n [0.377 0.399 0.352 0.396 0.289 1.   ]]\nOV\n[[ 1.    -0.02   0.03  -0.012  0.001  0.029]\n [-0.02   1.    -0.009 -0.028 -0.018  0.016]\n [ 0.03  -0.009  1.     0.001  0.014 -0.017]\n [-0.012 -0.028  0.001  1.    -0.001 -0.015]\n [ 0.001 -0.018  0.014 -0.001  1.    -0.014]\n [ 0.029  0.016 -0.017 -0.015 -0.014  1.   ]]\nL 2 QK\n[[1.    0.129 0.161 0.117 0.18  0.135]\n [0.129 1.    0.116 0.086 0.121 0.117]\n [0.161 0.116 1.    0.101 0.145 0.124]\n [0.117 0.086 0.101 1.    0.093 0.093]\n [0.18  0.121 0.145 0.093 1.    0.134]\n [0.135 0.117 0.124 0.093 0.134 1.   ]]\nOV\n[[ 1.    -0.023 -0.039 -0.022 -0.005  0.001]\n [-0.023  1.    -0.001  0.002 -0.012 -0.008]\n [-0.039 -0.001  1.     0.013  0.004  0.022]\n [-0.022  0.002  0.013  1.     0.001  0.   ]\n [-0.005 -0.012  0.004  0.001  1.     0.004]\n [ 0.001 -0.008  0.022  0.     0.004  1.   ]]\nL 3 QK\n[[1.    0.058 0.083 0.06  0.007 0.108]\n [0.058 1.    0.07  0.034 0.042 0.075]\n [0.083 0.07  1.    0.031 0.036 0.094]\n [0.06  0.034 0.031 1.    0.037 0.058]\n [0.007 0.042 0.036 0.037 1.    0.028]\n [0.108 0.075 0.094 0.058 0.028 1.   ]]\nOV\n[[ 1.    -0.012  0.024  0.011 -0.002  0.034]\n [-0.012  1.    -0.021  0.024  0.059  0.001]\n [ 0.024 -0.021  1.     0.003  0.077  0.026]\n [ 0.011  0.024  0.003  1.     0.042 -0.002]\n [-0.002  0.059  0.077  0.042  1.     0.057]\n [ 0.034  0.001  0.026 -0.002  0.057  1.   ]]\n\nL0 WEIGHT-ONLY EIGENSPECTRA AND ALIGNMENTS\nH0 raw: eig_top12=[ 0.113+0.216j  0.113-0.216j -0.166+0.039j -0.166-0.039j -0.141+0.034j -0.141-0.034j -0.11 +0.061j -0.11 -0.061j -0.09 +0.068j -0.09 -0.068j -0.106+0.j    -0.092+0.009j], sv_top8=[0.853 0.613 0.368 0.287 0.279 0.244 0.232 0.21 ]\nH0 gain: eig_top12=[ 0.079+0.j     0.049+0.j    -0.024+0.006j -0.024-0.006j -0.023+0.j     0.018+0.013j  0.018-0.013j -0.02 +0.j    -0.014+0.012j -0.014-0.012j -0.014+0.003j -0.014-0.003j], sv_top8=[0.248 0.102 0.047 0.043 0.037 0.036 0.034 0.031]\ntop left/right cos [ 0.488 -0.117  0.501 -0.2   -0.489]\ntop left/right coordinate concentration [0.094 0.147 0.063 0.067 0.065] [0.103 0.085 0.084 0.088 0.08 ]\nH1 raw: eig_top12=[-0.334+0.j    -0.225+0.j    -0.067+0.012j -0.067-0.012j -0.048+0.j     0.034+0.j    -0.027+0.j    -0.023+0.j    -0.01 +0.004j -0.01 -0.004j -0.009+0.002j -0.009-0.002j], sv_top8=[0.838 0.311 0.219 0.125 0.091 0.086 0.066 0.039]\nH1 gain: eig_top12=[-0.062+0.j    -0.01 +0.j    -0.009+0.j     0.004+0.j    -0.004+0.j    -0.003+0.002j -0.003-0.002j -0.002-0.j    -0.002+0.j    -0.002+0.001j -0.002-0.001j -0.001+0.j   ], sv_top8=[0.15  0.042 0.021 0.012 0.01  0.007 0.007 0.004]\ntop left/right cos [-0.406 -0.111 -0.405 -0.516 -0.422]\ntop left/right coordinate concentration [0.138 0.115 0.048 0.05  0.299] [0.15  0.046 0.023 0.048 0.049]\nH2 raw: eig_top12=[-0.237+0.j    -0.095+0.029j -0.095-0.029j -0.061+0.j    -0.016+0.011j -0.016-0.011j -0.014+0.j    -0.011+0.005j -0.011-0.005j -0.008-0.j    -0.008+0.j     0.   -0.007j], sv_top8=[0.543 0.183 0.157 0.133 0.072 0.066 0.046 0.046]\nH2 gain: eig_top12=[-0.057+0.j    -0.007+0.003j -0.007-0.003j -0.003+0.001j -0.003-0.001j -0.003+0.j    -0.001+0.j    -0.001-0.j    -0.001+0.001j -0.001-0.001j  0.001+0.001j  0.001-0.001j], sv_top8=[0.102 0.018 0.016 0.012 0.008 0.007 0.005 0.004]\ntop left/right cos [-0.539 -0.388 -0.501 -0.573 -0.215]\ntop left/right coordinate concentration [0.155 0.077 0.185 0.049 0.061] [0.12  0.044 0.048 0.051 0.057]\nH3 raw: eig_top12=[0.724+0.691j 0.724-0.691j 0.446+0.842j 0.446-0.842j 0.572+0.722j 0.572-0.722j 0.642+0.614j 0.642-0.614j 0.676+0.463j 0.676-0.463j 0.109+0.762j 0.109-0.762j], sv_top8=[1.114 1.092 1.07  1.064 1.03  0.992 0.968 0.954]\nH3 gain: eig_top12=[0.137+0.133j 0.137-0.133j 0.11 +0.089j 0.11 -0.089j 0.095+0.098j 0.095-0.098j 0.084+0.104j 0.084-0.104j 0.064+0.114j 0.064-0.114j 0.096+0.068j 0.096-0.068j], sv_top8=[0.244 0.223 0.201 0.182 0.179 0.157 0.155 0.149]\ntop left/right cos [0.658 0.673 0.628 0.553 0.593]\ntop left/right coordinate concentration [0.096 0.336 0.126 0.116 0.074] [0.317 0.388 0.196 0.174 0.302]\nH4 raw: eig_top12=[-0.172+0.j    -0.102+0.013j -0.102-0.013j -0.05 +0.j    -0.034+0.j    -0.018+0.006j -0.018-0.006j -0.009+0.009j -0.009-0.009j -0.009+0.005j -0.009-0.005j  0.01 +0.j   ], sv_top8=[0.51  0.227 0.185 0.124 0.101 0.066 0.065 0.049]\nH4 gain: eig_top12=[-0.015+0.j    -0.01 +0.j    -0.008+0.j    -0.003+0.j    -0.003-0.j    -0.002+0.001j -0.002-0.001j -0.   +0.002j -0.   -0.002j -0.001+0.001j -0.001-0.001j -0.002+0.j   ], sv_top8=[0.08  0.022 0.019 0.013 0.011 0.008 0.007 0.006]\ntop left/right cos [-0.166 -0.48  -0.366 -0.27  -0.135]\ntop left/right coordinate concentration [0.185 0.21  0.069 0.062 0.075] [0.079 0.1   0.047 0.059 0.073]\nH5 raw: eig_top12=[-0.028+0.j     0.019+0.j    -0.008+0.j    -0.005+0.005j -0.005-0.005j -0.005+0.002j -0.005-0.002j -0.004+0.004j -0.004-0.004j -0.005+0.002j -0.005-0.002j  0.   +0.005j], sv_top8=[0.18  0.048 0.024 0.02  0.02  0.019 0.019 0.018]\nH5 gain: eig_top12=[-0.005+0.j     0.001+0.j    -0.001+0.j    -0.   +0.001j -0.   -0.001j -0.001+0.j    -0.001+0.j    -0.001-0.j     0.   +0.001j  0.   -0.001j -0.   +0.001j -0.   -0.001j], sv_top8=[0.032 0.004 0.003 0.003 0.002 0.002 0.002 0.002]\ntop left/right cos [-0.145  0.202 -0.338 -0.096 -0.131]\ntop left/right coordinate concentration [0.121 0.049 0.111 0.07  0.074] [0.049 0.045 0.187 0.067 0.074]\n\nL0 QK TOP-DIRECTION COSINES (signed arbitrary; absolute values shown)\nleft [[1.    0.138 0.075 0.626 0.089 0.912]\n [0.138 1.    0.887 0.142 0.914 0.067]\n [0.075 0.887 1.    0.453 0.869 0.18 ]\n [0.626 0.142 0.453 1.    0.185 0.662]\n [0.089 0.914 0.869 0.185 1.    0.06 ]\n [0.912 0.067 0.18  0.662 0.06  1.   ]]\nright [[1.    0.053 0.256 0.533 0.302 0.043]\n [0.053 1.    0.806 0.047 0.686 0.324]\n [0.256 0.806 1.    0.304 0.597 0.528]\n [0.533 0.047 0.304 1.    0.065 0.106]\n [0.302 0.686 0.597 0.065 1.    0.37 ]\n [0.043 0.324 0.528 0.106 0.37  1.   ]]\n\nATTENTION AFFINE PARAMETER NORMS (no activations)\nL0H0: ||qD||=2.753 ||kD||=3.026 ||qb||=0.504 ||kb||=0.725 ||key_linear||=0.043 ||query_linear||=0.106\nL0H1: ||qD||=1.904 ||kD||=1.535 ||qb||=0.228 ||kb||=0.261 ||key_linear||=0.013 ||query_linear||=0.027\nL0H2: ||qD||=1.628 ||kD||=1.389 ||qb||=0.146 ||kb||=0.314 ||key_linear||=0.007 ||query_linear||=0.033\nL0H3: ||qD||=5.150 ||kD||=6.350 ||qb||=0.467 ||kb||=0.543 ||key_linear||=0.043 ||query_linear||=0.052\nL0H4: ||qD||=1.611 ||kD||=1.432 ||qb||=0.270 ||kb||=0.550 ||key_linear||=0.017 ||query_linear||=0.069\nL0H5: ||qD||=1.080 ||kD||=0.938 ||qb||=0.143 ||kb||=0.171 ||key_linear||=0.005 ||query_linear||=0.015\nL1H0: ||qD||=8.551 ||kD||=8.825 ||qb||=6.688 ||kb||=13.795 ||key_linear||=3.975 ||query_linear||=2.983\nL1H1: ||qD||=8.267 ||kD||=8.591 ||qb||=6.079 ||kb||=13.929 ||key_linear||=3.689 ||query_linear||=2.828\nL1H2: ||qD||=8.040 ||kD||=8.321 ||qb||=5.564 ||kb||=13.162 ||key_linear||=3.052 ||query_linear||=2.523\nL1H3: ||qD||=8.128 ||kD||=8.415 ||qb||=5.665 ||kb||=13.742 ||key_linear||=3.391 ||query_linear||=2.648\nL1H4: ||qD||=7.819 ||kD||=7.741 ||qb||=5.347 ||kb||=10.986 ||key_linear||=2.329 ||query_linear||=2.037\nL1H5: ||qD||=8.316 ||kD||=8.794 ||qb||=6.224 ||kb||=14.146 ||key_linear||=3.896 ||query_linear||=2.857\nL2H0: ||qD||=9.875 ||kD||=9.920 ||qb||=4.265 ||kb||=10.635 ||key_linear||=2.021 ||query_linear||=1.811\nL2H1: ||qD||=11.498 ||kD||=11.425 ||qb||=3.787 ||kb||=10.117 ||key_linear||=1.881 ||query_linear||=1.635\nL2H2: ||qD||=10.317 ||kD||=10.337 ||qb||=3.784 ||kb||=9.886 ||key_linear||=1.609 ||query_linear||=1.418\nL2H3: ||qD||=10.057 ||kD||=9.734 ||qb||=3.082 ||kb||=7.454 ||key_linear||=1.060 ||query_linear||=0.841\nL2H4: ||qD||=11.573 ||kD||=11.405 ||qb||=4.319 ||kb||=11.042 ||key_linear||=2.307 ||query_linear||=1.981\nL2H5: ||qD||=11.724 ||kD||=11.635 ||qb||=3.751 ||kb||=10.382 ||key_linear||=2.024 ||query_linear||=1.749\nL3H0: ||qD||=11.379 ||kD||=11.100 ||qb||=1.761 ||kb||=3.665 ||key_linear||=0.501 ||query_linear||=0.447\nL3H1: ||qD||=8.763 ||kD||=8.475 ||qb||=1.460 ||kb||=3.019 ||key_linear||=0.280 ||query_linear||=0.306\nL3H2: ||qD||=9.584 ||kD||=9.387 ||qb||=1.940 ||kb||=3.360 ||key_linear||=0.495 ||query_linear||=0.341\nL3H3: ||qD||=10.731 ||kD||=10.630 ||qb||=1.298 ||kb||=1.116 ||key_linear||=0.221 ||query_linear||=0.181\nL3H4: ||qD||=6.101 ||kD||=5.901 ||qb||=1.230 ||kb||=1.267 ||key_linear||=0.144 ||query_linear||=0.117\nL3H5: ||qD||=10.323 ||kD||=10.032 ||qb||=1.800 ||kb||=3.333 ||key_linear||=0.493 ||query_linear||=0.422\n\nKEY-SIDE COMPOSITION: trace(A_l B_earlier)/||A_l B_earlier||_F\nThese are matrix-path diagnostics, not observed attention or induction.\nL1H0 [(np.float64(0.134), 'L0H2', np.float64(2.261)), (np.float64(0.102), 'L0H3', np.float64(0.399)), (np.float64(0.1), 'L0H5', np.float64(17.379)), (np.float64(0.096), 'L0H4', np.float64(1.016)), (np.float64(0.094), 'L0H1', np.float64(2.843))]\nL1H1 [(np.float64(0.216), 'L0H3', np.float64(0.422)), (np.float64(0.065), 'L0H2', np.float64(2.168)), (np.float64(0.041), 'L0H5', np.float64(16.984)), (np.float64(0.018), 'L0H0', np.float64(0.115)), (np.float64(0.017), 'L0H1', np.float64(2.729))]\nL1H2 [(np.float64(0.167), 'L0H3', np.float64(0.455)), (np.float64(0.106), 'L0H2', np.float64(2.141)), (np.float64(0.1), 'L0H4', np.float64(0.941)), (np.float64(0.09), 'L0H5', np.float64(17.046)), (np.float64(0.078), 'L0H1', np.float64(2.692))]\nL1H3 [(np.float64(0.113), 'L0H2', np.float64(2.176)), (np.float64(0.082), 'L0H5', np.float64(17.098)), (np.float64(0.076), 'L0H3', np.float64(0.429)), (np.float64(0.075), 'L0H4', np.float64(0.971)), (np.float64(0.073), 'L0H1', np.float64(2.741))]\nL1H4 [(np.float64(0.08), 'L0H2', np.float64(1.852)), (np.float64(0.078), 'L0H4', np.float64(0.815)), (np.float64(0.074), 'L0H5', np.float64(15.135)), (np.float64(0.046), 'L0H1', np.float64(2.335)), (np.float64(0.02), 'L0H0', np.float64(0.122))]\nL1H5 [(np.float64(0.198), 'L0H3', np.float64(0.37)), (np.float64(0.102), 'L0H2', np.float64(2.195)), (np.float64(0.062), 'L0H5', np.float64(16.793)), (np.float64(0.061), 'L0H4', np.float64(0.992)), (np.float64(0.052), 'L0H1', np.float64(2.768))]\nL2H0 [(np.float64(0.208), 'L1H3', np.float64(0.907)), (np.float64(0.196), 'L0H2', np.float64(1.477)), (np.float64(0.189), 'L0H5', np.float64(10.681)), (np.float64(0.164), 'L1H1', np.float64(0.787)), (np.float64(0.133), 'L0H1', np.float64(1.866))]\nL2H1 [(np.float64(0.197), 'L0H5', np.float64(8.667)), (np.float64(0.179), 'L0H2', np.float64(1.219)), (np.float64(0.116), 'L0H1', np.float64(1.55)), (np.float64(0.08), 'L0H4', np.float64(0.587)), (np.float64(0.035), 'L0H0', np.float64(0.193))]\nL2H2 [(np.float64(0.184), 'L0H5', np.float64(8.027)), (np.float64(0.182), 'L0H2', np.float64(1.12)), (np.float64(0.123), 'L0H1', np.float64(1.427)), (np.float64(0.099), 'L0H3', np.float64(0.321)), (np.float64(0.075), 'L1H1', np.float64(0.899))]\nL2H3 [(np.float64(0.129), 'L0H2', np.float64(0.859)), (np.float64(0.11), 'L0H5', np.float64(6.748)), (np.float64(0.096), 'L0H1', np.float64(1.089)), (np.float64(0.084), 'L0H4', np.float64(0.387)), (np.float64(0.082), 'L0H3', np.float64(0.328))]\nL2H4 [(np.float64(0.186), 'L0H2', np.float64(1.634)), (np.float64(0.18), 'L0H5', np.float64(11.718)), (np.float64(0.159), 'L0H3', np.float64(0.43)), (np.float64(0.128), 'L0H1', np.float64(2.055)), (np.float64(0.109), 'L0H4', np.float64(0.75))]\nL2H5 [(np.float64(0.21), 'L0H5', np.float64(9.536)), (np.float64(0.187), 'L0H2', np.float64(1.329)), (np.float64(0.112), 'L0H1', np.float64(1.69)), (np.float64(0.081), 'L0H4', np.float64(0.639)), (np.float64(0.018), 'L0H0', np.float64(0.208))]\nL3H0 [(np.float64(0.107), 'L0H2', np.float64(0.735)), (np.float64(0.085), 'L0H5', np.float64(5.722)), (np.float64(0.069), 'L0H1', np.float64(0.946)), (np.float64(0.047), 'L0H4', np.float64(0.351)), (np.float64(-0.049), 'L0H0', np.float64(0.218))]\nL3H1 [(np.float64(0.093), 'L0H2', np.float64(0.533)), (np.float64(0.075), 'L0H5', np.float64(4.189)), (np.float64(0.072), 'L1H2', np.float64(0.667)), (np.float64(0.069), 'L0H1', np.float64(0.681)), (np.float64(0.051), 'L1H1', np.float64(0.643))]\nL3H2 [(np.float64(0.081), 'L0H2', np.float64(0.644)), (np.float64(0.046), 'L0H1', np.float64(0.835)), (np.float64(0.033), 'L0H3', np.float64(0.259)), (np.float64(0.016), 'L0H5', np.float64(5.116)), (np.float64(0.005), 'L1H2', np.float64(0.771))]\nL3H3 [(np.float64(0.922), 'L0H3', np.float64(0.435)), (np.float64(0.806), 'L2H5', np.float64(1.826)), (np.float64(0.562), 'L2H1', np.float64(1.866)), (np.float64(0.514), 'L1H0', np.float64(1.236)), (np.float64(0.472), 'L1H5', np.float64(1.124))]\nL3H4 [(np.float64(0.276), 'L2H2', np.float64(0.593)), (np.float64(0.272), 'L2H4', np.float64(0.415)), (np.float64(0.202), 'L2H5', np.float64(0.486)), (np.float64(0.19), 'L0H4', np.float64(0.057)), (np.float64(0.182), 'L2H1', np.float64(0.512))]\nL3H5 [(np.float64(0.05), 'L0H2', np.float64(0.738)), (np.float64(0.047), 'L0H5', np.float64(5.814)), (np.float64(0.016), 'L0H1', np.float64(0.945)), (np.float64(-0.001), 'L0H4', np.float64(0.337)), (np.float64(-0.008), 'L0H0', np.float64(0.181))]\n",
  "seconds": 0.984,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "482582c5f372ffd5de4ac0d46dd8f82ea9f097d8041f3156bdb16a442bbacee0",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

EXACT HARNESS FEEDBACK
WITHIN-LAYER QK COSINES, THEN OV COSINES
L 0 QK
[[ 1.     0.012  0.007  0.137  0.013  0.044]
 [ 0.012  1.     0.69  -0.026  0.57   0.026]
 [ 0.007  0.69   1.    -0.063  0.492 -0.075]
 [ 0.137 -0.026 -0.063  1.    -0.044  0.01 ]
 [ 0.013  0.57   0.492 -0.044  1.     0.033]
 [ 0.044  0.026 -0.075  0.01   0.033  1.   ]]
OV
[[ 1.     0.018  0.017 -0.035  0.026  0.018]
 [ 0.018  1.     0.925  0.006  0.868  0.643]
 [ 0.017  0.925  1.     0.009  0.81   0.692]
 [-0.035  0.006  0.009  1.     0.011 -0.001]
 [ 0.026  0.868  0.81   0.011  1.     0.592]
 [ 0.018  0.643  0.692 -0.001  0.592  1.   ]]
L 1 QK
[[1.    0.358 0.373 0.346 0.334 0.377]
 [0.358 1.    0.372 0.362 0.302 0.399]
 [0.373 0.372 1.    0.368 0.375 0.352]
 [0.346 0.362 0.368 1.    0.32  0.396]
 [0.334 0.302 0.375 0.32  1.    0.289]
 [0.377 0.399 0.352 0.396 0.289 1.   ]]
OV
[[ 1.    -0.02   0.03  -0.012  0.001  0.029]
 [-0.02   1.    -0.009 -0.028 -0.018  0.016]
 [ 0.03  -0.009  1.     0.001  0.014 -0.017]
 [-0.012 -0.028  0.001  1.    -0.001 -0.015]
 [ 0.001 -0.018  0.014 -0.001  1.    -0.014]
 [ 0.029  0.016 -0.017 -0.015 -0.014  1.   ]]
L 2 QK
[[1.    0.129 0.161 0.117 0.18  0.135]
 [0.129 1.    0.116 0.086 0.121 0.117]
 [0.161 0.116 1.    0.101 0.145 0.124]
 [0.117 0.086 0.101 1.    0.093 0.093]
 [0.18  0.121 0.145 0.093 1.    0.134]
 [0.135 0.117 0.124 0.093 0.134 1.   ]]
OV
[[ 1.    -0.023 -0.039 -0.022 -0.005  0.001]
 [-0.023  1.    -0.001  0.002 -0.012 -0.008]
 [-0.039 -0.001  1.     0.013  0.004  0.022]
 [-0.022  0.002  0.013  1.     0.001  0.   ]
 [-0.005 -0.012  0.004  0.001  1.     0.004]
 [ 0.001 -0.008  0.022  0.     0.004  1.   ]]
L 3 QK
[[1.    0.058 0.083 0.06  0.007 0.108]
 [0.058 1.    0.07  0.034 0.042 0.075]
 [0.083 0.07  1.    0.031 0.036 0.094]
 [0.06  0.034 0.031 1.    0.037 0.058]
 [0.007 0.042 0.036 0.037 1.    0.028]
 [0.108 0.075 0.094 0.058 0.028 1.   ]]
OV
[[ 1.    -0.012  0.024  0.011 -0.002  0.034]
 [-0.012  1.    -0.021  0.024  0.059  0.001]
 [ 0.024 -0.021  1.     0.003  0.077  0.026]
 [ 0.011  0.024  0.003  1.     0.042 -0.002]
 [-0.002  0.059  0.077  0.042  1.     0.057]
 [ 0.034  0.001  0.026 -0.002  0.057  1.   ]]

L0 WEIGHT-ONLY EIGENSPECTRA AND ALIGNMENTS
H0 raw: eig_top12=[ 0.113+0.216j  0.113-0.216j -0.166+0.039j -0.166-0.039j -0.141+0.034j -0.141-0.034j -0.11 +0.061j -0.11 -0.061j -0.09 +0.068j -0.09 -0.068j -0.106+0.j    -0.092+0.009j], sv_top8=[0.853 0.613 0.368 0.287 0.279 0.244 0.232 0.21 ]
H0 gain: eig_top12=[ 0.079+0.j     0.049+0.j    -0.024+0.006j -0.024-0.006j -0.023+0.j     0.018+0.013j  0.018-0.013j -0.02 +0.j    -0.014+0.012j -0.014-0.012j -0.014+0.003j -0.014-0.003j], sv_top8=[0.248 0.102 0.047 0.043 0.037 0.036 0.034 0.031]
top left/right cos [ 0.488 -0.117  0.501 -0.2   -0.489]
top left/right coordinate concentration [0.094 0.147 0.063 0.067 0.065] [0.103 0.085 0.084 0.088 0.08 ]
H1 raw: eig_top12=[-0.334+0.j    -0.225+0.j    -0.067+0.012j -0.067-0.012j -0.048+0.j     0.034+0.j    -0.027+0.j    -0.023+0.j    -0.01 +0.004j -0.01 -0.004j -0.009+0.002j -0.009-0.002j], sv_top8=[0.838 0.311 0.219 0.125 0.091 0.086 0.066 0.039]
H1 gain: eig_top12=[-0.062+0.j    -0.01 +0.j    -0.009+0.j     0.004+0.j    -0.004+0.j    -0.003+0.002j -0.003-0.002j -0.002-0.j    -0.002+0.j    -0.002+0.001j -0.002-0.001j -0.001+0.j   ], sv_top8=[0.15  0.042 0.021 0.012 0.01  0.007 0.007 0.004]
top left/right cos [-0.406 -0.111 -0.405 -0.516 -0.422]
top left/right coordinate concentration [0.138 0.115 0.048 0.05  0.299] [0.15  0.046 0.023 0.048 0.049]
H2 raw: eig_top12=[-0.237+0.j    -0.095+0.029j -0.095-0.029j -0.061+0.j    -0.016+0.011j -0.016-0.011j -0.014+0.j    -0.011+0.005j -0.011-0.005j -0.008-0.j    -0.008+0.j     0.   -0.007j], sv_top8=[0.543 0.183 0.157 0.133 0.072 0.066 0.046 0.046]
H2 gain: eig_top12=[-0.057+0.j    -0.007+0.003j -0.007-0.003j -0.003+0.001j -0.003-0.001j -0.003+0.j    -0.001+0.j    -0.001-0.j    -0.001+0.001j -0.001-0.001j  0.001+0.001j  0.001-0.001j], sv_top8=[0.102 0.018 0.016 0.012 0.008 0.007 0.005 0.004]
top left/right cos [-0.539 -0.388 -0.501 -0.573 -0.215]
top left/right coordinate concentration [0.155 0.077 0.185 0.049 0.061] [0.12  0.044 0.048 0.051 0.057]
H3 raw: eig_top12=[0.724+0.691j 0.724-0.691j 0.446+0.842j 0.446-0.842j 0.572+0.722j 0.572-0.722j 0.642+0.614j 0.642-0.614j 0.676+0.463j 0.676-0.463j 0.109+0.762j 0.109-0.762j], sv_top8=[1.114 1.092 1.07  1.064 1.03  0.992 0.968 0.954]
H3 gain: eig_top12=[0.137+0.133j 0.137-0.133j 0.11 +0.089j 0.11 -0.089j 0.095+0.098j 0.095-0.098j 0.084+0.104j 0.084-0.104j 0.064+0.114j 0.064-0.114j 0.096+0.068j 0.096-0.068j], sv_top8=[0.244 0.223 0.201 0.182 0.179 0.157 0.155 0.149]
top left/right cos [0.658 0.673 0.628 0.553 0.593]
top left/right coordinate concentration [0.096 0.336 0.126 0.116 0.074] [0.317 0.388 0.196 0.174 0.302]
H4 raw: eig_top12=[-0.172+0.j    -0.102+0.013j -0.102-0.013j -0.05 +0.j    -0.034+0.j    -0.018+0.006j -0.018-0.006j -0.009+0.009j -0.009-0.009j -0.009+0.005j -0.009-0.005j  0.01 +0.j   ], sv_top8=[0.51  0.227 0.185 0.124 0.101 0.066 0.065 0.049]
H4 gain: eig_top12=[-0.015+0.j    -0.01 +0.j    -0.008+0.j    -0.003+0.j    -0.003-0.j    -0.002+0.001j -0.002-0.001j -0.   +0.002j -0.   -0.002j -0.001+0.001j -0.001-0.001j -0.002+0.j   ], sv_top8=[0.08  0.022 0.019 0.013 0.011 0.008 0.007 0.006]
top left/right cos [-0.166 -0.48  -0.366 -0.27  -0.135]
top left/right coordinate concentration [0.185 0.21  0.069 0.062 0.075] [0.079 0.1   0.047 0.059 0.073]
H5 raw: eig_top12=[-0.028+0.j     0.019+0.j    -0.008+0.j    -0.005+0.005j -0.005-0.005j -0.005+0.002j -0.005-0.002j -0.004+0.004j -0.004-0.004j -0.005+0.002j -0.005-0.002j  0.   +0.005j], sv_top8=[0.18  0.048 0.024 0.02  0.02  0.019 0.019 0.018]
H5 gain: eig_top12=[-0.005+0.j     0.001+0.j    -0.001+0.j    -0.   +0.001j -0.   -0.001j -0.001+0.j    -0.001+0.j    -0.001-0.j     0.   +0.001j  0.   -0.001j -0.   +0.001j -0.   -0.001j], sv_top8=[0.032 0.004 0.003 0.003 0.002 0.002 0.002 0.002]
top left/right cos [-0.145  0.202 -0.338 -0.096 -0.131]
top left/right coordinate concentration [0.121 0.049 0.111 0.07  0.074] [0.049 0.045 0.187 0.067 0.074]

L0 QK TOP-DIRECTION COSINES (signed arbitrary; absolute values shown)
left [[1.    0.138 0.075 0.626 0.089 0.912]
 [0.138 1.    0.887 0.142 0.914 0.067]
 [0.075 0.887 1.    0.453 0.869 0.18 ]
 [0.626 0.142 0.453 1.    0.185 0.662]
 [0.089 0.914 0.869 0.185 1.    0.06 ]
 [0.912 0.067 0.18  0.662 0.06  1.   ]]
right [[1.    0.053 0.256 0.533 0.302 0.043]
 [0.053 1.    0.806 0.047 0.686 0.324]
 [0.256 0.806 1.    0.304 0.597 0.528]
 [0.533 0.047 0.304 1.    0.065 0.106]
 [0.302 0.686 0.597 0.065 1.    0.37 ]
 [0.043 0.324 0.528 0.106 0.37  1.   ]]

ATTENTION AFFINE PARAMETER NORMS (no activations)
L0H0: ||qD||=2.753 ||kD||=3.026 ||qb||=0.504 ||kb||=0.725 ||key_linear||=0.043 ||query_linear||=0.106
L0H1: ||qD||=1.904 ||kD||=1.535 ||qb||=0.228 ||kb||=0.261 ||key_linear||=0.013 ||query_linear||=0.027
L0H2: ||qD||=1.628 ||kD||=1.389 ||qb||=0.146 ||kb||=0.314 ||key_linear||=0.007 ||query_linear||=0.033
L0H3: ||qD||=5.150 ||kD||=6.350 ||qb||=0.467 ||kb||=0.543 ||key_linear||=0.043 ||query_linear||=0.052
L0H4: ||qD||=1.611 ||kD||=1.432 ||qb||=0.270 ||kb||=0.550 ||key_linear||=0.017 ||query_linear||=0.069
L0H5: ||qD||=1.080 ||kD||=0.938 ||qb||=0.143 ||kb||=0.171 ||key_linear||=0.005 ||query_linear||=0.015
L1H0: ||qD||=8.551 ||kD||=8.825 ||qb||=6.688 ||kb||=13.795 ||key_linear||=3.975 ||query_linear||=2.983
L1H1: ||qD||=8.267 ||kD||=8.591 ||qb||=6.079 ||kb||=13.929 ||key_linear||=3.689 ||query_linear||=2.828
L1H2: ||qD||=8.040 ||kD||=8.321 ||qb||=5.564 ||kb||=13.162 ||key_linear||=3.052 ||query_linear||=2.523
L1H3: ||qD||=8.128 ||kD||=8.415 ||qb||=5.665 ||kb||=13.742 ||key_linear||=3.391 ||query_linear||=2.648
L1H4: ||qD||=7.819 ||kD||=7.741 ||qb||=5.347 ||kb||=10.986 ||key_linear||=2.329 ||query_linear||=2.037
L1H5: ||qD||=8.316 ||kD||=8.794 ||qb||=6.224 ||kb||=14.146 ||key_linear||=3.896 ||query_linear||=2.857
L2H0: ||qD||=9.875 ||kD||=9.920 ||qb||=4.265 ||kb||=10.635 ||key_linear||=2.021 ||query_linear||=1.811
L2H1: ||qD||=11.498 ||kD||=11.425 ||qb||=3.787 ||kb||=10.117 ||key_linear||=1.881 ||query_linear||=1.635
L2H2: ||qD||=10.317 ||kD||=10.337 ||qb||=3.784 ||kb||=9.886 ||key_linear||=1.609 ||query_linear||=1.418
L2H3: ||qD||=10.057 ||kD||=9.734 ||qb||=3.082 ||kb||=7.454 ||key_linear||=1.060 ||query_linear||=0.841
L2H4: ||qD||=11.573 ||kD||=11.405 ||qb||=4.319 ||kb||=11.042 ||key_linear||=2.307 ||query_linear||=1.981
L2H5: ||qD||=11.724 ||kD||=11.635 ||qb||=3.751 ||kb||=10.382 ||key_linear||=2.024 ||query_linear||=1.749
L3H0: ||qD||=11.379 ||kD||=11.100 ||qb||=1.761 ||kb||=3.665 ||key_linear||=0.501 ||query_linear||=0.447
L3H1: ||qD||=8.763 ||kD||=8.475 ||qb||=1.460 ||kb||=3.019 ||key_linear||=0.280 ||query_linear||=0.306
L3H2: ||qD||=9.584 ||kD||=9.387 ||qb||=1.940 ||kb||=3.360 ||key_linear||=0.495 ||query_linear||=0.341
L3H3: ||qD||=10.731 ||kD||=10.630 ||qb||=1.298 ||kb||=1.116 ||key_linear||=0.221 ||query_linear||=0.181
L3H4: ||qD||=6.101 ||kD||=5.901 ||qb||=1.230 ||kb||=1.267 ||key_linear||=0.144 ||query_linear||=0.117
L3H5: ||qD||=10.323 ||kD||=10.032 ||qb||=1.800 ||kb||=3.333 ||key_linear||=0.493 ||query_linear||=0.422

KEY-SIDE COMPOSITION: trace(A_l B_earlier)/||A_l B_earlier||_F
These are matrix-path diagnostics, not observed attention or induction.
L1H0 [(np.float64(0.134), 'L0H2', np.float64(2.261)), (np.float64(0.102), 'L0H3', np.float64(0.399)), (np.float64(0.1), 'L0H5', np.float64(17.379)), (np.float64(0.096), 'L0H4', np.float64(1.016)), (np.float64(0.094), 'L0H1', np.float64(2.843))]
L1H1 [(np.float64(0.216), 'L0H3', np.float64(0.422)), (np.float64(0.065), 'L0H2', np.float64(2.168)), (np.float64(0.041), 'L0H5', np.float64(16.984)), (np.float64(0.018), 'L0H0', np.float64(0.115)), (np.float64(0.017), 'L0H1', np.float64(2.729))]
L1H2 [(np.float64(0.167), 'L0H3', np.float64(0.455)), (np.float64(0.106), 'L0H2', np.float64(2.141)), (np.float64(0.1), 'L0H4', np.float64(0.941)), (np.float64(0.09), 'L0H5', np.float64(17.046)), (np.float64(0.078), 'L0H1', np.float64(2.692))]
L1H3 [(np.float64(0.113), 'L0H2', np.float64(2.176)), (np.float64(0.082), 'L0H5', np.float64(17.098)), (np.float64(0.076), 'L0H3', np.float64(0.429)), (np.float64(0.075), 'L0H4', np.float64(0.971)), (np.float64(0.073), 'L0H1', np.float64(2.741))]
L1H4 [(np.float64(0.08), 'L0H2', np.float64(1.852)), (np.float64(0.078), 'L0H4', np.float64(0.815)), (np.float64(0.074), 'L0H5', np.float64(15.135)), (np.float64(0.046), 'L0H1', np.float64(2.335)), (np.float64(0.02), 'L0H0', np.float64(0.122))]
L1H5 [(np.float64(0.198), 'L0H3', np.float64(0.37)), (np.float64(0.102), 'L0H2', np.float64(2.195)), (np.float64(0.062), 'L0H5', np.float64(16.793)), (np.float64(0.061), 'L0H4', np.float64(0.992)), (np.float64(0.052), 'L0H1', np.float64(2.768))]
L2H0 [(np.float64(0.208), 'L1H3', np.float64(0.907)), (np.float64(0.196), 'L0H2', np.float64(1.477)), (np.float64(0.189), 'L0H5', np.float64(10.681)), (np.float64(0.164), 'L1H1', np.float64(0.787)), (np.float64(0.133), 'L0H1', np.float64(1.866))]
L2H1 [(np.float64(0.197), 'L0H5', np.float64(8.667)), (np.float64(0.179), 'L0H2', np.float64(1.219)), (np.float64(0.116), 'L0H1', np.float64(1.55)), (np.float64(0.08), 'L0H4', np.float64(0.587)), (np.float64(0.035), 'L0H0', np.float64(0.193))]
L2H2 [(np.float64(0.184), 'L0H5', np.float64(8.027)), (np.float64(0.182), 'L0H2', np.float64(1.12)), (np.float64(0.123), 'L0H1', np.float64(1.427)), (np.float64(0.099), 'L0H3', np.float64(0.321)), (np.float64(0.075), 'L1H1', np.float64(0.899))]
L2H3 [(np.float64(0.129), 'L0H2', np.float64(0.859)), (np.float64(0.11), 'L0H5', np.float64(6.748)), (np.float64(0.096), 'L0H1', np.float64(1.089)), (np.float64(0.084), 'L0H4', np.float64(0.387)), (np.float64(0.082), 'L0H3', np.float64(0.328))]
L2H4 [(np.float64(0.186), 'L0H2', np.float64(1.634)), (np.float64(0.18), 'L0H5', np.float64(11.718)), (np.float64(0.159), 'L0H3', np.float64(0.43)), (np.float64(0.128), 'L0H1', np.float64(2.055)), (np.float64(0.109), 'L0H4', np.float64(0.75))]
L2H5 [(np.float64(0.21), 'L0H5', np.float64(9.536)), (np.float64(0.187), 'L0H2', np.float64(1.329)), (np.float64(0.112), 'L0H1', np.float64(1.69)), (np.float64(0.081), 'L0H4', np.float64(0.639)), (np.float64(0.018), 'L0H0', np.float64(0.208))]
L3H0 [(np.float64(0.107), 'L0H2', np.float64(0.735)), (np.float64(0.085), 'L0H5', np.float64(5.722)), (np.float64(0.069), 'L0H1', np.float64(0.946)), (np.float64(0.047), 'L0H4', np.float64(0.351)), (np.float64(-0.049), 'L0H0', np.float64(0.218))]
L3H1 [(np.float64(0.093), 'L0H2', np.float64(0.533)), (np.float64(0.075), 'L0H5', np.float64(4.189)), (np.float64(0.072), 'L1H2', np.float64(0.667)), (np.float64(0.069), 'L0H1', np.float64(0.681)), (np.float64(0.051), 'L1H1', np.float64(0.643))]
L3H2 [(np.float64(0.081), 'L0H2', np.float64(0.644)), (np.float64(0.046), 'L0H1', np.float64(0.835)), (np.float64(0.033), 'L0H3', np.float64(0.259)), (np.float64(0.016), 'L0H5', np.float64(5.116)), (np.float64(0.005), 'L1H2', np.float64(0.771))]
L3H3 [(np.float64(0.922), 'L0H3', np.float64(0.435)), (np.float64(0.806), 'L2H5', np.float64(1.826)), (np.float64(0.562), 'L2H1', np.float64(1.866)), (np.float64(0.514), 'L1H0', np.float64(1.236)), (np.float64(0.472), 'L1H5', np.float64(1.124))]
L3H4 [(np.float64(0.276), 'L2H2', np.float64(0.593)), (np.float64(0.272), 'L2H4', np.float64(0.415)), (np.float64(0.202), 'L2H5', np.float64(0.486)), (np.float64(0.19), 'L0H4', np.float64(0.057)), (np.float64(0.182), 'L2H1', np.float64(0.512))]
L3H5 [(np.float64(0.05), 'L0H2', np.float64(0.738)), (np.float64(0.047), 'L0H5', np.float64(5.814)), (np.float64(0.016), 'L0H1', np.float64(0.945)), (np.float64(-0.001), 'L0H4', np.float64(0.337)), (np.float64(-0.008), 'L0H0', np.float64(0.181))]


========================================================================
TURN t003
MESSAGE SENT TO CODEX
OUTPUT of run 2 of at most 100:
```
WITHIN-LAYER QK COSINES, THEN OV COSINES
L 0 QK
[[ 1.     0.012  0.007  0.137  0.013  0.044]
 [ 0.012  1.     0.69  -0.026  0.57   0.026]
 [ 0.007  0.69   1.    -0.063  0.492 -0.075]
 [ 0.137 -0.026 -0.063  1.    -0.044  0.01 ]
 [ 0.013  0.57   0.492 -0.044  1.     0.033]
 [ 0.044  0.026 -0.075  0.01   0.033  1.   ]]
OV
[[ 1.     0.018  0.017 -0.035  0.026  0.018]
 [ 0.018  1.     0.925  0.006  0.868  0.643]
 [ 0.017  0.925  1.     0.009  0.81   0.692]
 [-0.035  0.006  0.009  1.     0.011 -0.001]
 [ 0.026  0.868  0.81   0.011  1.     0.592]
 [ 0.018  0.643  0.692 -0.001  0.592  1.   ]]
L 1 QK
[[1.    0.358 0.373 0.346 0.334 0.377]
 [0.358 1.    0.372 0.362 0.302 0.399]
 [0.373 0.372 1.    0.368 0.375 0.352]
 [0.346 0.362 0.368 1.    0.32  0.396]
 [0.334 0.302 0.375 0.32  1.    0.289]
 [0.377 0.399 0.352 0.396 0.289 1.   ]]
OV
[[ 1.    -0.02   0.03  -0.012  0.001  0.029]
 [-0.02   1.    -0.009 -0.028 -0.018  0.016]
 [ 0.03  -0.009  1.     0.001  0.014 -0.017]
 [-0.012 -0.028  0.001  1.    -0.001 -0.015]
 [ 0.001 -0.018  0.014 -0.001  1.    -0.014]
 [ 0.029  0.016 -0.017 -0.015 -0.014  1.   ]]
L 2 QK
[[1.    0.129 0.161 0.117 0.18  0.135]
 [0.129 1.    0.116 0.086 0.121 0.117]
 [0.161 0.116 1.    0.101 0.145 0.124]
 [0.117 0.086 0.101 1.    0.093 0.093]
 [0.18  0.121 0.145 0.093 1.    0.134]
 [0.135 0.117 0.124 0.093 0.134 1.   ]]
OV
[[ 1.    -0.023 -0.039 -0.022 -0.005  0.001]
 [-0.023  1.    -0.001  0.002 -0.012 -0.008]
 [-0.039 -0.001  1.     0.013  0.004  0.022]
 [-0.022  0.002  0.013  1.     0.001  0.   ]
 [-0.005 -0.012  0.004  0.001  1.     0.004]
 [ 0.001 -0.008  0.022  0.     0.004  1.   ]]
L 3 QK
[[1.    0.058 0.083 0.06  0.007 0.108]
 [0.058 1.    0.07  0.034 0.042 0.075]
 [0.083 0.07  1.    0.031 0.036 0.094]
 [0.06  0.034 0.031 1.    0.037 0.058]
 [0.007 0.042 0.036 0.037 1.    0.028]
 [0.108 0.075 0.094 0.058 0.028 1.   ]]
OV
[[ 1.    -0.012  0.024  0.011 -0.002  0.034]
 [-0.012  1.    -0.021  0.024  0.059  0.001]
 [ 0.024 -0.021  1.     0.003  0.077  0.026]
 [ 0.011  0.024  0.003  1.     0.042 -0.002]
 [-0.002  0.059  0.077  0.042  1.     0.057]
 [ 0.034  0.001  0.026 -0.002  0.057  1.   ]]

L0 WEIGHT-ONLY EIGENSPECTRA AND ALIGNMENTS
H0 raw: eig_top12=[ 0.113+0.216j  0.113-0.216j -0.166+0.039j -0.166-0.039j -0.141+0.034j -0.141-0.034j -0.11 +0.061j -0.11 -0.061j -0.09 +0.068j -0.09 -0.068j -0.106+0.j    -0.092+0.009j], sv_top8=[0.853 0.613 0.368 0.287 0.279 0.244 0.232 0.21 ]
H0 gain: eig_top12=[ 0.079+0.j     0.049+0.j    -0.024+0.006j -0.024-0.006j -0.023+0.j     0.018+0.013j  0.018-0.013j -0.02 +0.j    -0.014+0.012j -0.014-0.012j -0.014+0.003j -0.014-0.003j], sv_top8=[0.248 0.102 0.047 0.043 0.037 0.036 0.034 0.031]
top left/right cos [ 0.488 -0.117  0.501 -0.2   -0.489]
top left/right coordinate concentration [0.094 0.147 0.063 0.067 0.065] [0.103 0.085 0.084 0.088 0.08 ]
H1 raw: eig_top12=[-0.334+0.j    -0.225+0.j    -0.067+0.012j -0.067-0.012j -0.048+0.j     0.034+0.j    -0.027+0.j    -0.023+0.j    -0.01 +0.004j -0.01 -0.004j -0.009+0.002j -0.009-0.002j], sv_top8=[0.838 0.311 0.219 0.125 0.091 0.086 0.066 0.039]
H1 gain: eig_top12=[-0.062+0.j    -0.01 +0.j    -0.009+0.j     0.004+0.j    -0.004+0.j    -0.003+0.002j -0.003-0.002j -0.002-0.j    -0.002+0.j    -0.002+0.001j -0.002-0.001j -0.001+0.j   ], sv_top8=[0.15  0.042 0.021 0.012 0.01  0.007 0.007 0.004]
top left/right cos [-0.406 -0.111 -0.405 -0.516 -0.422]
top left/right coordinate concentration [0.138 0.115 0.048 0.05  0.299] [0.15  0.046 0.023 0.048 0.049]
H2 raw: eig_top12=[-0.237+0.j    -0.095+0.029j -0.095-0.029j -0.061+0.j    -0.016+0.011j -0.016-0.011j -0.014+0.j    -0.011+0.005j -0.011-0.005j -0.008-0.j    -0.008+0.j     0.   -0.007j], sv_top8=[0.543 0.183 0.157 0.133 0.072 0.066 0.046 0.046]
H2 gain: eig_top12=[-0.057+0.j    -0.007+0.003j -0.007-0.003j -0.003+0.001j -0.003-0.001j -0.003+0.j    -0.001+0.j    -0.001-0.j    -0.001+0.001j -0.001-0.001j  0.001+0.001j  0.001-0.001j], sv_top8=[0.102 0.018 0.016 0.012 0.008 0.007 0.005 0.004]
top left/right cos [-0.539 -0.388 -0.501 -0.573 -0.215]
top left/right coordinate concentration [0.155 0.077 0.185 0.049 0.061] [0.12  0.044 0.048 0.051 0.057]
H3 raw: eig_top12=[0.724+0.691j 0.724-0.691j 0.446+0.842j 0.446-0.842j 0.572+0.722j 0.572-0.722j 0.642+0.614j 0.642-0.614j 0.676+0.463j 0.676-0.463j 0.109+0.762j 0.109-0.762j], sv_top8=[1.114 1.092 1.07  1.064 1.03  0.992 0.968 0.954]
H3 gain: eig_top12=[0.137+0.133j 0.137-0.133j 0.11 +0.089j 0.11 -0.089j 0.095+0.098j 0.095-0.098j 0.084+0.104j 0.084-0.104j 0.064+0.114j 0.064-0.114j 0.096+0.068j 0.096-0.068j], sv_top8=[0.244 0.223 0.201 0.182 0.179 0.157 0.155 0.149]
top left/right cos [0.658 0.673 0.628 0.553 0.593]
top left/right coordinate concentration [0.096 0.336 0.126 0.116 0.074] [0.317 0.388 0.196 0.174 0.302]
H4 raw: eig_top12=[-0.172+0.j    -0.102+0.013j -0.102-0.013j -0.05 +0.j    -0.034+0.j    -0.018+0.006j -0.018-0.006j -0.009+0.009j -0.009-0.009j -0.009+0.005j -0.009-0.005j  0.01 +0.j   ], sv_top8=[0.51  0.227 0.185 0.124 0.101 0.066 0.065 0.049]
H4 gain: eig_top12=[-0.015+0.j    -0.01 +0.j    -0.008+0.j    -0.003+0.j    -0.003-0.j    -0.002+0.001j -0.002-0.001j -0.   +0.002j -0.   -0.002j -0.001+0.001j -0.001-0.001j -0.002+0.j   ], sv_top8=[0.08  0.022 0.019 0.013 0.011 0.008 0.007 0.006]
top left/right cos [-0.166 -0.48  -0.366 -0.27  -0.135]
top left/right coordinate concentration [0.185 0.21  0.069 0.062 0.075] [0.079 0.1   0.047 0.059 0.073]
H5 raw: eig_top12=[-0.028+0.j     0.019+0.j    -0.008+0.j    -0.005+0.005j -0.005-0.005j -0.005+0.002j -0.005-0.002j -0.004+0.004j -0.004-0.004j -0.005+0.002j -0.005-0.002j  0.   +0.005j], sv_top8=[0.18  0.048 0.024 0.02  0.02  0.019 0.019 0.018]
H5 gain: eig_top12=[-0.005+0.j     0.001+0.j    -0.001+0.j    -0.   +0.001j -0.   -0.001j -0.001+0.j    -0.001+0.j    -0.001-0.j     0.   +0.001j  0.   -0.001j -0.   +0.001j -0.   -0.001j], sv_top8=[0.032 0.004 0.003 0.003 0.002 0.002 0.002 0.002]
top left/right cos [-0.145  0.202 -0.338 -0.096 -0.131]
top left/right coordinate concentration [0.121 0.049 0.111 0.07  0.074] [0.049 0.045 0.187 0.067 0.074]

L0 QK TOP-DIRECTION COSINES (signed arbitrary; absolute values shown)
left [[1.    0.138 0.075 0.626 0.089 0.912]
 [0.138 1.    0.887 0.142 0.914 0.067]
 [0.075 0.887 1.    0.453 0.869 0.18 ]
 [0.626 0.142 0.453 1.    0.185 0.662]
 [0.089 0.914 0.869 0.185 1.    0.06 ]
 [0.912 0.067 0.18  0.662 0.06  1.   ]]
right [[1.    0.053 0.256 0.533 0.302 0.043]
 [0.053 1.    0.806 0.047 0.686 0.324]
 [0.256 0.806 1.    0.304 0.597 0.528]
 [0.533 0.047 0.304 1.    0.065 0.106]
 [0.302 0.686 0.597 0.065 1.    0.37 ]
 [0.043 0.324 0.528 0.106 0.37  1.   ]]

ATTENTION AFFINE PARAMETER NORMS (no activations)
L0H0: ||qD||=2.753 ||kD||=3.026 ||qb||=0.504 ||kb||=0.725 ||key_linear||=0.043 ||query_linear||=0.106
L0H1: ||qD||=1.904 ||kD||=1.535 ||qb||=0.228 ||kb||=0.261 ||key_linear||=0.013 ||query_linear||=0.027
L0H2: ||qD||=1.628 ||kD||=1.389 ||qb||=0.146 ||kb||=0.314 ||key_linear||=0.007 ||query_linear||=0.033
L0H3: ||qD||=5.150 ||kD||=6.350 ||qb||=0.467 ||kb||=0.543 ||key_linear||=0.043 ||query_linear||=0.052
L0H4: ||qD||=1.611 ||kD||=1.432 ||qb||=0.270 ||kb||=0.550 ||key_linear||=0.017 ||query_linear||=0.069
L0H5: ||qD||=1.080 ||kD||=0.938 ||qb||=0.143 ||kb||=0.171 ||key_linear||=0.005 ||query_linear||=0.015
L1H0: ||qD||=8.551 ||kD||=8.825 ||qb||=6.688 ||kb||=13.795 ||key_linear||=3.975 ||query_linear||=2.983
L1H1: ||qD||=8.267 ||kD||=8.591 ||qb||=6.079 ||kb||=13.929 ||key_linear||=3.689 ||query_linear||=2.828
L1H2: ||qD||=8.040 ||kD||=8.321 ||qb||=5.564 ||kb||=13.162 ||key_linear||=3.052 ||query_linear||=2.523
L1H3: ||qD||=8.128 ||kD||=8.415 ||qb||=5.665 ||kb||=13.742 ||key_linear||=3.391 ||query_linear||=2.648
L1H4: ||qD||=7.819 ||kD||=7.741 ||qb||=5.347 ||kb||=10.986 ||key_linear||=2.329 ||query_linear||=2.037
L1H5: ||qD||=8.316 ||kD||=8.794 ||qb||=6.224 ||kb||=14.146 ||key_linear||=3.896 ||query_linear||=2.857
L2H0: ||qD||=9.875 ||kD||=9.920 ||qb||=4.265 ||kb||=10.635 ||key_linear||=2.021 ||query_linear||=1.811
L2H1: ||qD||=11.498 ||kD||=11.425 ||qb||=3.787 ||kb||=10.117 ||key_linear||=1.881 ||query_linear||=1.635
L2H2: ||qD||=10.317 ||kD||=10.337 ||qb||=3.784 ||kb||=9.886 ||key_linear||=1.609 ||query_linear||=1.418
L2H3: ||qD||=10.057 ||kD||=9.734 ||qb||=3.082 ||kb||=7.454 ||key_linear||=1.060 ||query_linear||=0.841
L2H4: ||qD||=11.573 ||kD||=11.405 ||qb||=4.319 ||kb||=11.042 ||key_linear||=2.307 ||query_linear||=1.981
L2H5: ||qD||=11.724 ||kD||=11.635 ||qb||=3.751 ||kb||=10.382 ||key_linear||=2.024 ||query_linear||=1.749
L3H0: ||qD||=11.379 ||kD||=11.100 ||qb||=1.761 ||kb||=3.665 ||key_linear||=0.501 ||query_linear||=0.447
L3H1: ||qD||=8.763 ||kD||=8.475 ||qb||=1.460 ||kb||=3.019 ||key_linear||=0.280 ||query_linear||=0.306
L3H2: ||qD||=9.584 ||kD||=9.387 ||qb||=1.940 ||kb||=3.360 ||key_linear||=0.495 ||query_linear||=0.341
L3H3: ||qD||=10.731 ||kD||=10.630 ||qb||=1.298 ||kb||=1.116 ||key_linear||=0.221 ||query_linear||=0.181
L3H4: ||qD||=6.101 ||kD||=5.901 ||qb||=1.230 ||kb||=1.267 ||key_linear||=0.144 ||query_linear||=0.117
L3H5: ||qD||=10.323 ||kD||=10.032 ||qb||=1.800 ||kb||=3.333 ||key_linear||=0.493 ||query_linear||=0.422

KEY-SIDE COMPOSITION: trace(A_l B_earlier)/||A_l B_earlier||_F
These are matrix-path diagnostics, not observed attention or induction.
L1H0 [(np.float64(0.134), 'L0H2', np.float64(2.261)), (np.float64(0.102), 'L0H3', np.float64(0.399)), (np.float64(0.1), 'L0H5', np.float64(17.379)), (np.float64(0.096), 'L0H4', np.float64(1.016)), (np.float64(0.094), 'L0H1', np.float64(2.843))]
L1H1 [(np.float64(0.216), 'L0H3', np.float64(0.422)), (np.float64(0.065), 'L0H2', np.float64(2.168)), (np.float64(0.041), 'L0H5', np.float64(16.984)), (np.float64(0.018), 'L0H0', np.float64(0.115)), (np.float64(0.017), 'L0H1', np.float64(2.729))]
L1H2 [(np.float64(0.167), 'L0H3', np.float64(0.455)), (np.float64(0.106), 'L0H2', np.float64(2.141)), (np.float64(0.1), 'L0H4', np.float64(0.941)), (np.float64(0.09), 'L0H5', np.float64(17.046)), (np.float64(0.078), 'L0H1', np.float64(2.692))]
L1H3 [(np.float64(0.113), 'L0H2', np.float64(2.176)), (np.float64(0.082), 'L0H5', np.float64(17.098)), (np.float64(0.076), 'L0H3', np.float64(0.429)), (np.float64(0.075), 'L0H4', np.float64(0.971)), (np.float64(0.073), 'L0H1', np.float64(2.741))]
L1H4 [(np.float64(0.08), 'L0H2', np.float64(1.852)), (np.float64(0.078), 'L0H4', np.float64(0.815)), (np.float64(0.074), 'L0H5', np.float64(15.135)), (np.float64(0.046), 'L0H1', np.float64(2.335)), (np.float64(0.02), 'L0H0', np.float64(0.122))]
L1H5 [(np.float64(0.198), 'L0H3', np.float64(0.37)), (np.float64(0.102), 'L0H2', np.float64(2.195)), (np.float64(0.062), 'L0H5', np.float64(16.793)), (np.float64(0.061), 'L0H4', np.float64(0.992)), (np.float64(0.052), 'L0H1', np.float64(2.768))]
L2H0 [(np.float64(0.208), 'L1H3', np.float64(0.907)), (np.float64(0.196), 'L0H2', np.float64(1.477)), (np.float64(0.189), 'L0H5', np.float64(10.681)), (np.float64(0.164), 'L1H1', np.float64(0.787)), (np.float64(0.133), 'L0H1', np.float64(1.866))]
L2H1 [(np.float64(0.197), 'L0H5', np.float64(8.667)), (np.float64(0.179), 'L0H2', np.float64(1.219)), (np.float64(0.116), 'L0H1', np.float64(1.55)), (np.float64(0.08), 'L0H4', np.float64(0.587)), (np.float64(0.035), 'L0H0', np.float64(0.193))]
L2H2 [(np.float64(0.184), 'L0H5', np.float64(8.027)), (np.float64(0.182), 'L0H2', np.float64(1.12)), (np.float64(0.123), 'L0H1', np.float64(1.427)), (np.float64(0.099), 'L0H3', np.float64(0.321)), (np.float64(0.075), 'L1H1', np.float64(0.899))]
L2H3 [(np.float64(0.129), 'L0H2', np.float64(0.859)), (np.float64(0.11), 'L0H5', np.float64(6.748)), (np.float64(0.096), 'L0H1', np.float64(1.089)), (np.float64(0.084), 'L0H4', np.float64(0.387)), (np.float64(0.082), 'L0H3', np.float64(0.328))]
L2H4 [(np.float64(0.186), 'L0H2', np.float64(1.634)), (np.float64(0.18), 'L0H5', np.float64(11.718)), (np.float64(0.159), 'L0H3', np.float64(0.43)), (np.float64(0.128), 'L0H1', np.float64(2.055)), (np.float64(0.109), 'L0H4', np.float64(0.75))]
L2H5 [(np.float64(0.21), 'L0H5', np.float64(9.536)), (np.float64(0.187), 'L0H2', np.float64(1.329)), (np.float64(0.112), 'L0H1', np.float64(1.69)), (np.float64(0.081), 'L0H4', np.float64(0.639)), (np.float64(0.018), 'L0H0', np.float64(0.208))]
L3H0 [(np.float64(0.107), 'L0H2', np.float64(0.735)), (np.float64(0.085), 'L0H5', np.float64(5.722)), (np.float64(0.069), 'L0H1', np.float64(0.946)), (np.float64(0.047), 'L0H4', np.float64(0.351)), (np.float64(-0.049), 'L0H0', np.float64(0.218))]
L3H1 [(np.float64(0.093), 'L0H2', np.float64(0.533)), (np.float64(0.075), 'L0H5', np.float64(4.189)), (np.float64(0.072), 'L1H2', np.float64(0.667)), (np.float64(0.069), 'L0H1', np.float64(0.681)), (np.float64(0.051), 'L1H1', np.float64(0.643))]
L3H2 [(np.float64(0.081), 'L0H2', np.float64(0.644)), (np.float64(0.046), 'L0H1', np.float64(0.835)), (np.float64(0.033), 'L0H3', np.float64(0.259)), (np.float64(0.016), 'L0H5', np.float64(5.116)), (np.float64(0.005), 'L1H2', np.float64(0.771))]
L3H3 [(np.float64(0.922), 'L0H3', np.float64(0.435)), (np.float64(0.806), 'L2H5', np.float64(1.826)), (np.float64(0.562), 'L2H1', np.float64(1.866)), (np.float64(0.514), 'L1H0', np.float64(1.236)), (np.float64(0.472), 'L1H5', np.float64(1.124))]
L3H4 [(np.float64(0.276), 'L2H2', np.float64(0.593)), (np.float64(0.272), 'L2H4', np.float64(0.415)), (np.float64(0.202), 'L2H5', np.float64(0.486)), (np.float64(0.19), 'L0H4', np.float64(0.057)), (np.float64(0.182), 'L2H1', np.float64(0.512))]
L3H5 [(np.float64(0.05), 'L0H2', np.float64(0.738)), (np.float64(0.047), 'L0H5', np.float64(5.814)), (np.float64(0.016), 'L0H1', np.float64(0.945)), (np.float64(-0.001), 'L0H4', np.float64(0.337)), (np.float64(-0.008), 'L0H0', np.float64(0.181))]

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

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

AGENT_MESSAGE
```python
import numpy as np
from threadpoolctl import threadpool_limits
threadpool_limits(2)
np.set_printoptions(precision=3, suppress=True, linewidth=180)

print("DETAILED SINGULAR VALUES")
for l,name in [(0,"W_q"),(0,"W_k"),(0,"W_v"),(0,"W_fc"),
               (0,"W_proj"),(1,"W_fc"),(3,"W_proj"),(3,"W_fc")]:
    a=W[f"{name}.{l}"].astype(float)
    sv=np.linalg.svd(a,compute_uv=False)
    print(f"{name}.{l} first 80:",sv[:80])
    print("at indices 96,128,192,256,320,383:",sv[[95,127,191,255,319,382]])

print("\nL0 HEAD Q/K/V SINGULAR VALUES")
for j in range(6):
    s=slice(64*j,64*j+64)
    for name in ["W_q","W_k","W_v"]:
        a=W[f"{name}.0"][s].astype(float)
        sv=np.linalg.svd(a,compute_uv=False)
        print(f"H{j} {name}:",sv[:12], "tail8:",sv[-8:])

print("\nL0 SUBSPACE OVERLAPS")
_,_,Vv=np.linalg.svd(W["W_v.0"].astype(float),full_matrices=False)
_,_,Vf=np.linalg.svd(W["W_fc.0"].astype(float),full_matrices=False)
s=slice(192,256)
qp=W["W_q.0"][s].astype(float)
kp=W["W_k.0"][s].astype(float)
_,_,Vp=np.linalg.svd(np.vstack([qp,kp]),full_matrices=False)
for j in range(6):
    s=slice(64*j,64*j+64)
    for name in ["W_q","W_k","W_v"]:
        a=W[f"{name}.0"][s].astype(float)
        n=np.linalg.norm(a)**2
        print(f"H{j} {name}",
              "energy in V_all top27/top40=",round(np.linalg.norm(a@Vv[:27].T)**2/n,3),round(np.linalg.norm(a@Vv[:40].T)**2/n,3),
              "FC top27/top40=",round(np.linalg.norm(a@Vf[:27].T)**2/n,3),round(np.linalg.norm(a@Vf[:40].T)**2/n,3),
              "H3_QK top40=",round(np.linalg.norm(a@Vp[:40].T)**2/n,3))

print("\nRAW QK ROTATION COMPARISON WITH L0H3")
# Polar factors are obtained only from trained matrices. No inputs or activations.
A0=W["W_q.0"][192:256].astype(float).T@W["W_k.0"][192:256].astype(float)/8
u,s,vt=np.linalg.svd(A0,full_matrices=False)
U=np.linalg.svd(np.hstack([u[:,:40],vt[:40].T]),full_matrices=False)[0][:,:50]
base=U.T@A0@U
ub,sb,vb=np.linalg.svd(base)
rot=ub@vb
powers={n:np.linalg.matrix_power(rot,n) for n in range(-8,9)}
for l in range(4):
    for j in range(6):
        ss=slice(64*j,64*j+64)
        a=W[f"W_q.{l}"][ss].astype(float).T@W[f"W_k.{l}"][ss].astype(float)/8
        ar=U.T@a@U
        frac=np.linalg.norm(ar)**2/np.linalg.norm(a)**2
        vals=[(np.sum(ar*p)/(np.linalg.norm(ar)*np.linalg.norm(p)),n) for n,p in powers.items()]
        vals.sort(reverse=True)
        print(f"L{l}H{j} projected_fraction={frac:.3f} polar_correlations=",
              [(n,round(c,3)) for c,n in vals[:4]])

print("\nLAYER-NORM EXTREME COORDINATES")
for l in range(4):
    for name in ["ln1_w","ln1_b","ln2_w","ln2_b"]:
        a=W[f"{name}.{l}"]
        ii=np.argsort(abs(a-(1 if name.endswith("_w") else 0)))[-8:][::-1]
        print(f"{name}.{l}",[(int(i),round(float(a[i]),4)) for i in ii])
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 3,
  "turn": "t003",
  "code": "import numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=3, suppress=True, linewidth=180)\n\nprint(\"DETAILED SINGULAR VALUES\")\nfor l,name in [(0,\"W_q\"),(0,\"W_k\"),(0,\"W_v\"),(0,\"W_fc\"),\n               (0,\"W_proj\"),(1,\"W_fc\"),(3,\"W_proj\"),(3,\"W_fc\")]:\n    a=W[f\"{name}.{l}\"].astype(float)\n    sv=np.linalg.svd(a,compute_uv=False)\n    print(f\"{name}.{l} first 80:\",sv[:80])\n    print(\"at indices 96,128,192,256,320,383:\",sv[[95,127,191,255,319,382]])\n\nprint(\"\\nL0 HEAD Q/K/V SINGULAR VALUES\")\nfor j in range(6):\n    s=slice(64*j,64*j+64)\n    for name in [\"W_q\",\"W_k\",\"W_v\"]:\n        a=W[f\"{name}.0\"][s].astype(float)\n        sv=np.linalg.svd(a,compute_uv=False)\n        print(f\"H{j} {name}:\",sv[:12], \"tail8:\",sv[-8:])\n\nprint(\"\\nL0 SUBSPACE OVERLAPS\")\n_,_,Vv=np.linalg.svd(W[\"W_v.0\"].astype(float),full_matrices=False)\n_,_,Vf=np.linalg.svd(W[\"W_fc.0\"].astype(float),full_matrices=False)\ns=slice(192,256)\nqp=W[\"W_q.0\"][s].astype(float)\nkp=W[\"W_k.0\"][s].astype(float)\n_,_,Vp=np.linalg.svd(np.vstack([qp,kp]),full_matrices=False)\nfor j in range(6):\n    s=slice(64*j,64*j+64)\n    for name in [\"W_q\",\"W_k\",\"W_v\"]:\n        a=W[f\"{name}.0\"][s].astype(float)\n        n=np.linalg.norm(a)**2\n        print(f\"H{j} {name}\",\n              \"energy in V_all top27/top40=\",round(np.linalg.norm(a@Vv[:27].T)**2/n,3),round(np.linalg.norm(a@Vv[:40].T)**2/n,3),\n              \"FC top27/top40=\",round(np.linalg.norm(a@Vf[:27].T)**2/n,3),round(np.linalg.norm(a@Vf[:40].T)**2/n,3),\n              \"H3_QK top40=\",round(np.linalg.norm(a@Vp[:40].T)**2/n,3))\n\nprint(\"\\nRAW QK ROTATION COMPARISON WITH L0H3\")\n# Polar factors are obtained only from trained matrices. No inputs or activations.\nA0=W[\"W_q.0\"][192:256].astype(float).T@W[\"W_k.0\"][192:256].astype(float)/8\nu,s,vt=np.linalg.svd(A0,full_matrices=False)\nU=np.linalg.svd(np.hstack([u[:,:40],vt[:40].T]),full_matrices=False)[0][:,:50]\nbase=U.T@A0@U\nub,sb,vb=np.linalg.svd(base)\nrot=ub@vb\npowers={n:np.linalg.matrix_power(rot,n) for n in range(-8,9)}\nfor l in range(4):\n    for j in range(6):\n        ss=slice(64*j,64*j+64)\n        a=W[f\"W_q.{l}\"][ss].astype(float).T@W[f\"W_k.{l}\"][ss].astype(float)/8\n        ar=U.T@a@U\n        frac=np.linalg.norm(ar)**2/np.linalg.norm(a)**2\n        vals=[(np.sum(ar*p)/(np.linalg.norm(ar)*np.linalg.norm(p)),n) for n,p in powers.items()]\n        vals.sort(reverse=True)\n        print(f\"L{l}H{j} projected_fraction={frac:.3f} polar_correlations=\",\n              [(n,round(c,3)) for c,n in vals[:4]])\n\nprint(\"\\nLAYER-NORM EXTREME COORDINATES\")\nfor l in range(4):\n    for name in [\"ln1_w\",\"ln1_b\",\"ln2_w\",\"ln2_b\"]:\n        a=W[f\"{name}.{l}\"]\n        ii=np.argsort(abs(a-(1 if name.endswith(\"_w\") else 0)))[-8:][::-1]\n        print(f\"{name}.{l}\",[(int(i),round(float(a[i]),4)) for i in ii])",
  "out": "DETAILED SINGULAR VALUES\nW_q.0 first 80: [4.368 3.131 3.099 3.042 2.994 2.951 2.926 2.881 2.859 2.857 2.804 2.766 2.762 2.692 2.683 2.661 2.637 2.588 2.539 2.509 2.458 2.433 2.403 2.299 2.272 2.173 2.098 2.048 1.954\n 1.887 1.877 1.865 1.738 1.725 1.665 1.605 1.552 1.535 1.476 1.424 1.378 1.341 1.306 1.264 1.24  1.213 1.154 1.122 1.109 1.068 1.053 1.031 1.017 1.007 0.985 0.957 0.944 0.913\n 0.888 0.879 0.868 0.864 0.837 0.822 0.795 0.789 0.778 0.769 0.765 0.754 0.742 0.731 0.72  0.711 0.704 0.697 0.691 0.682 0.679 0.672]\nat indices 96,128,192,256,320,383: [0.572 0.467 0.32  0.205 0.1   0.004]\nW_k.0 first 80: [4.163 3.952 3.467 3.427 3.364 3.335 3.279 3.256 3.214 3.163 3.115 3.029 2.977 2.901 2.881 2.832 2.798 2.76  2.658 2.584 2.528 2.449 2.353 2.301 2.281 2.206 2.121 2.074 1.917\n 1.896 1.813 1.753 1.713 1.676 1.573 1.53  1.47  1.431 1.415 1.355 1.3   1.26  1.187 1.169 1.161 1.116 1.108 1.104 1.072 1.032 1.003 0.988 0.965 0.953 0.931 0.911 0.907 0.885\n 0.873 0.866 0.85  0.841 0.834 0.815 0.792 0.782 0.768 0.749 0.739 0.731 0.726 0.725 0.723 0.716 0.71  0.698 0.69  0.689 0.679 0.666]\nat indices 96,128,192,256,320,383: [0.585 0.484 0.339 0.215 0.108 0.001]\nW_v.0 first 80: [5.208 3.364 3.006 2.962 2.815 2.639 2.527 2.46  2.37  2.301 2.25  2.23  2.151 2.135 2.044 2.039 2.001 1.981 1.91  1.875 1.822 1.773 1.749 1.708 1.634 1.611 1.556 1.487 1.453\n 1.402 1.268 1.213 1.187 1.13  1.109 1.08  1.026 0.992 0.987 0.959 0.933 0.931 0.908 0.892 0.887 0.866 0.864 0.853 0.843 0.827 0.811 0.793 0.783 0.781 0.759 0.748 0.721 0.715\n 0.71  0.701 0.698 0.675 0.663 0.659 0.652 0.647 0.64  0.626 0.626 0.619 0.614 0.607 0.6   0.596 0.593 0.589 0.585 0.584 0.579 0.573]\nat indices 96,128,192,256,320,383: [0.515 0.435 0.306 0.195 0.098 0.001]\nW_fc.0 first 80: [18.118 12.76  11.337  9.323  9.267  9.021  8.898  8.817  8.609  8.4    8.189  8.06   7.819  7.724  7.62   7.543  7.226  7.081  6.979  6.833  6.779  6.682  6.592  6.402  6.39\n  6.306  6.207  6.18   5.974  5.864  5.789  5.741  5.646  5.579  5.512  5.353  5.196  5.193  5.122  5.053  5.029  4.849  4.646  4.573  4.452  4.163  4.133  3.899  3.888  3.557\n  3.455  3.347  3.241  3.008  2.921  2.833  2.796  2.657  2.612  2.555  2.418  2.381  2.33   2.3    2.276  2.213  2.162  2.127  2.062  2.002  1.983  1.968  1.927  1.88   1.851\n  1.832  1.806  1.777  1.766  1.741]\nat indices 96,128,192,256,320,383: [1.496 1.236 0.95  0.766 0.613 0.433]\nW_proj.0 first 80: [10.899  7.702  7.173  6.638  6.541  6.288  6.201  6.133  5.941  5.917  5.758  5.641  5.552  5.476  5.393  5.363  5.259  5.206  5.171  5.153  5.057  5.018  4.96   4.88   4.821\n  4.788  4.77   4.73   4.643  4.579  4.546  4.529  4.505  4.493  4.44   4.415  4.369  4.315  4.293  4.261  4.243  4.185  4.134  4.104  4.064  4.049  4.026  3.988  3.967  3.95\n  3.933  3.901  3.857  3.804  3.778  3.754  3.74   3.713  3.692  3.683  3.652  3.612  3.607  3.575  3.564  3.522  3.509  3.497  3.47   3.456  3.446  3.417  3.4    3.378  3.337\n  3.302  3.291  3.269  3.238  3.23 ]\nat indices 96,128,192,256,320,383: [2.883 2.339 1.479 0.935 0.586 0.321]\nW_fc.1 first 80: [19.829 11.575 11.522 11.262 11.059 10.875 10.632 10.497 10.363 10.3   10.174 10.05   9.875  9.759  9.703  9.671  9.42   9.191  9.065  8.835  8.81   8.705  8.595  8.58   8.435\n  8.206  8.123  8.078  7.899  7.826  7.758  7.676  7.635  7.592  7.463  7.395  7.33   7.244  7.185  7.132  7.062  6.82   6.764  6.645  6.591  6.568  6.514  6.48   6.426  6.346\n  6.271  6.216  6.192  6.148  6.1    6.035  5.947  5.81   5.766  5.762  5.715  5.68   5.646  5.578  5.535  5.439  5.411  5.362  5.325  5.27   5.205  5.152  5.104  5.083  5.02\n  5.     4.974  4.903  4.884  4.838]\nat indices 96,128,192,256,320,383: [4.226 3.295 1.966 1.259 0.912 0.551]\nW_proj.3 first 80: [28.461 22.214 21.34  20.708 19.943 19.662 18.986 18.744 18.295 17.457 17.132 16.561 15.329 14.874 14.328 13.438 12.667 12.455 12.317 11.531 11.047  9.084  8.353  8.24   6.37\n  5.836  4.626  4.583  3.777  3.61   3.252  3.183  3.086  2.941  2.865  2.801  2.639  2.564  2.538  2.466  2.419  2.404  2.346  2.31   2.246  2.227  2.155  2.147  2.089  2.069\n  2.065  2.041  1.975  1.936  1.921  1.917  1.857  1.848  1.83   1.82   1.798  1.779  1.764  1.737  1.729  1.719  1.709  1.692  1.68   1.661  1.645  1.635  1.619  1.594  1.589\n  1.583  1.565  1.55   1.541  1.52 ]\nat indices 96,128,192,256,320,383: [1.36  1.117 0.821 0.626 0.477 0.321]\nW_fc.3 first 80: [15.114  8.611  8.486  8.073  7.769  7.529  7.449  7.334  7.094  6.971  6.891  6.808  6.601  6.537  6.454  6.331  6.294  6.232  6.162  6.115  6.058  6.034  5.926  5.878  5.85\n  5.803  5.786  5.751  5.723  5.713  5.669  5.644  5.591  5.547  5.529  5.512  5.494  5.466  5.454  5.444  5.4    5.348  5.344  5.325  5.294  5.275  5.253  5.237  5.214  5.193\n  5.186  5.17   5.139  5.13   5.098  5.091  5.061  5.059  5.046  5.036  5.014  5.001  4.972  4.962  4.951  4.912  4.906  4.885  4.869  4.859  4.837  4.808  4.787  4.778  4.77\n  4.764  4.75   4.736  4.722  4.7  ]\nat indices 96,128,192,256,320,383: [4.491 4.135 3.521 2.982 2.445 0.818]\n\nL0 HEAD Q/K/V SINGULAR VALUES\nH0 W_q: [2.679 2.108 1.778 1.572 1.462 1.364 1.345 1.302 1.245 1.219 1.192 1.16 ] tail8: [0.335 0.33  0.324 0.315 0.307 0.299 0.284 0.278]\nH0 W_k: [2.798 2.259 1.759 1.613 1.568 1.472 1.427 1.373 1.327 1.296 1.283 1.218] tail8: [0.345 0.342 0.335 0.321 0.314 0.307 0.287 0.276]\nH0 W_v: [1.941 1.825 1.681 1.437 1.343 1.314 1.22  1.165 1.151 1.123 1.078 1.013] tail8: [0.286 0.278 0.27  0.264 0.259 0.254 0.251 0.239]\nH1 W_q: [2.424 1.574 1.242 0.96  0.905 0.802 0.687 0.587 0.566 0.527 0.51  0.509] tail8: [0.239 0.234 0.229 0.224 0.218 0.21  0.207 0.198]\nH1 W_k: [2.798 1.612 1.417 1.068 0.886 0.857 0.784 0.602 0.579 0.569 0.54  0.528] tail8: [0.263 0.262 0.255 0.247 0.244 0.241 0.233 0.225]\nH1 W_v: [1.826 1.676 1.062 0.965 0.815 0.696 0.66  0.645 0.625 0.595 0.582 0.565] tail8: [0.264 0.252 0.25  0.242 0.239 0.227 0.225 0.219]\nH2 W_q: [2.046 1.194 1.147 1.051 0.764 0.701 0.601 0.571 0.535 0.522 0.516 0.482] tail8: [0.237 0.231 0.23  0.218 0.211 0.21  0.204 0.199]\nH2 W_k: [2.164 1.259 1.13  1.038 0.837 0.752 0.7   0.679 0.597 0.579 0.547 0.536] tail8: [0.265 0.259 0.253 0.249 0.246 0.242 0.232 0.218]\nH2 W_v: [1.569 1.363 0.945 0.883 0.778 0.688 0.672 0.64  0.609 0.597 0.558 0.551] tail8: [0.26  0.255 0.252 0.243 0.232 0.228 0.222 0.218]\nH3 W_q: [2.962 2.894 2.873 2.845 2.806 2.792 2.766 2.756 2.703 2.634 2.588 2.561] tail8: [0.573 0.556 0.532 0.519 0.497 0.474 0.443 0.352]\nH3 W_k: [3.362 3.238 3.209 3.181 3.099 3.086 3.024 2.999 2.951 2.923 2.856 2.765] tail8: [0.505 0.492 0.484 0.453 0.433 0.421 0.396 0.366]\nH3 W_v: [2.102 1.948 1.903 1.866 1.843 1.819 1.769 1.755 1.674 1.651 1.629 1.621] tail8: [0.318 0.313 0.313 0.298 0.287 0.279 0.271 0.262]\nH4 W_q: [2.076 1.29  1.127 0.896 0.868 0.699 0.665 0.626 0.597 0.57  0.528 0.524] tail8: [0.241 0.238 0.229 0.226 0.222 0.216 0.209 0.203]\nH4 W_k: [2.007 1.451 1.356 1.117 0.975 0.833 0.809 0.705 0.682 0.674 0.656 0.581] tail8: [0.275 0.274 0.268 0.262 0.257 0.249 0.246 0.23 ]\nH4 W_v: [1.252 1.117 0.818 0.786 0.737 0.658 0.626 0.612 0.602 0.565 0.542 0.537] tail8: [0.255 0.25  0.244 0.234 0.232 0.228 0.226 0.216]\nH5 W_q: [1.229 0.674 0.419 0.398 0.395 0.392 0.382 0.373 0.371 0.366 0.359 0.348] tail8: [0.191 0.188 0.186 0.179 0.176 0.173 0.171 0.166]\nH5 W_k: [1.181 0.612 0.509 0.503 0.482 0.467 0.463 0.449 0.446 0.442 0.428 0.427] tail8: [0.215 0.212 0.209 0.203 0.2   0.196 0.192 0.17 ]\nH5 W_v: [4.667 3.073 2.351 2.151 2.011 1.857 1.567 1.379 1.248 1.229 1.111 1.018] tail8: [0.283 0.281 0.276 0.275 0.269 0.265 0.264 0.256]\n\nL0 SUBSPACE OVERLAPS\nH0 W_q energy in V_all top27/top40= 0.323 0.423 FC top27/top40= 0.159 0.225 H3_QK top40= 0.462\nH0 W_k energy in V_all top27/top40= 0.352 0.455 FC top27/top40= 0.171 0.242 H3_QK top40= 0.51\nH0 W_v energy in V_all top27/top40= 0.661 0.766 FC top27/top40= 0.23 0.436 H3_QK top40= 0.151\nH1 W_q energy in V_all top27/top40= 0.332 0.398 FC top27/top40= 0.125 0.155 H3_QK top40= 0.276\nH1 W_k energy in V_all top27/top40= 0.308 0.428 FC top27/top40= 0.211 0.263 H3_QK top40= 0.284\nH1 W_v energy in V_all top27/top40= 0.511 0.604 FC top27/top40= 0.15 0.204 H3_QK top40= 0.353\nH2 W_q energy in V_all top27/top40= 0.258 0.329 FC top27/top40= 0.101 0.131 H3_QK top40= 0.254\nH2 W_k energy in V_all top27/top40= 0.245 0.361 FC top27/top40= 0.167 0.226 H3_QK top40= 0.274\nH2 W_v energy in V_all top27/top40= 0.43 0.549 FC top27/top40= 0.124 0.178 H3_QK top40= 0.341\nH3 W_q energy in V_all top27/top40= 0.094 0.265 FC top27/top40= 0.069 0.103 H3_QK top40= 0.901\nH3 W_k energy in V_all top27/top40= 0.137 0.318 FC top27/top40= 0.066 0.094 H3_QK top40= 0.922\nH3 W_v energy in V_all top27/top40= 0.773 0.853 FC top27/top40= 0.406 0.663 H3_QK top40= 0.17\nH4 W_q energy in V_all top27/top40= 0.243 0.304 FC top27/top40= 0.113 0.147 H3_QK top40= 0.279\nH4 W_k energy in V_all top27/top40= 0.22 0.329 FC top27/top40= 0.15 0.204 H3_QK top40= 0.351\nH4 W_v energy in V_all top27/top40= 0.298 0.457 FC top27/top40= 0.109 0.149 H3_QK top40= 0.279\nH5 W_q energy in V_all top27/top40= 0.232 0.284 FC top27/top40= 0.094 0.124 H3_QK top40= 0.211\nH5 W_k energy in V_all top27/top40= 0.191 0.26 FC top27/top40= 0.138 0.202 H3_QK top40= 0.188\nH5 W_v energy in V_all top27/top40= 0.852 0.899 FC top27/top40= 0.251 0.336 H3_QK top40= 0.517\n\nRAW QK ROTATION COMPARISON WITH L0H3\nL0H0 projected_fraction=0.403 polar_correlations= [(4, np.float64(0.116)), (5, np.float64(0.098)), (3, np.float64(0.078)), (-4, np.float64(0.058))]\nL0H1 projected_fraction=0.215 polar_correlations= [(3, np.float64(0.067)), (4, np.float64(0.047)), (-2, np.float64(0.03)), (-1, np.float64(0.017))]\nL0H2 projected_fraction=0.253 polar_correlations= [(3, np.float64(0.049)), (-2, np.float64(0.041)), (4, np.float64(0.022)), (-3, np.float64(0.018))]\nL0H3 projected_fraction=0.963 polar_correlations= [(1, np.float64(0.784)), (2, np.float64(0.273)), (0, np.float64(0.273)), (-7, np.float64(0.15))]\nL0H4 projected_fraction=0.335 polar_correlations= [(3, np.float64(0.05)), (2, np.float64(-0.015)), (-7, np.float64(-0.019)), (-2, np.float64(-0.025))]\nL0H5 projected_fraction=0.300 polar_correlations= [(2, np.float64(0.073)), (3, np.float64(0.043)), (-6, np.float64(0.042)), (-7, np.float64(0.029))]\nL1H0 projected_fraction=0.024 polar_correlations= [(1, np.float64(0.08)), (3, np.float64(0.053)), (2, np.float64(0.045)), (-8, np.float64(0.033))]\nL1H1 projected_fraction=0.026 polar_correlations= [(1, np.float64(0.073)), (2, np.float64(0.06)), (3, np.float64(0.052)), (4, np.float64(0.036))]\nL1H2 projected_fraction=0.023 polar_correlations= [(3, np.float64(0.057)), (1, np.float64(0.044)), (-6, np.float64(0.038)), (-8, np.float64(0.027))]\nL1H3 projected_fraction=0.027 polar_correlations= [(3, np.float64(0.073)), (2, np.float64(0.072)), (-6, np.float64(0.053)), (1, np.float64(0.052))]\nL1H4 projected_fraction=0.022 polar_correlations= [(3, np.float64(0.042)), (1, np.float64(0.015)), (2, np.float64(0.009)), (-3, np.float64(0.007))]\nL1H5 projected_fraction=0.037 polar_correlations= [(1, np.float64(0.081)), (2, np.float64(0.062)), (3, np.float64(0.055)), (-6, np.float64(0.042))]\nL2H0 projected_fraction=0.023 polar_correlations= [(3, np.float64(0.056)), (1, np.float64(0.055)), (2, np.float64(0.048)), (-6, np.float64(0.025))]\nL2H1 projected_fraction=0.016 polar_correlations= [(2, np.float64(0.068)), (1, np.float64(0.033)), (-5, np.float64(0.032)), (-7, np.float64(0.028))]\nL2H2 projected_fraction=0.017 polar_correlations= [(3, np.float64(0.038)), (-5, np.float64(0.037)), (-6, np.float64(0.036)), (2, np.float64(0.031))]\nL2H3 projected_fraction=0.015 polar_correlations= [(1, np.float64(0.057)), (4, np.float64(0.036)), (3, np.float64(0.036)), (-7, np.float64(0.017))]\nL2H4 projected_fraction=0.017 polar_correlations= [(2, np.float64(0.07)), (-6, np.float64(0.054)), (3, np.float64(0.05)), (-7, np.float64(0.045))]\nL2H5 projected_fraction=0.017 polar_correlations= [(2, np.float64(0.07)), (3, np.float64(0.047)), (-6, np.float64(0.025)), (8, np.float64(0.021))]\nL3H0 projected_fraction=0.015 polar_correlations= [(0, np.float64(0.132)), (1, np.float64(0.072)), (8, np.float64(0.048)), (-1, np.float64(0.043))]\nL3H1 projected_fraction=0.017 polar_correlations= [(0, np.float64(0.048)), (-7, np.float64(0.035)), (1, np.float64(0.03)), (-5, np.float64(0.028))]\nL3H2 projected_fraction=0.017 polar_correlations= [(1, np.float64(0.042)), (-6, np.float64(0.014)), (4, np.float64(0.011)), (-5, np.float64(0.01))]\nL3H3 projected_fraction=0.016 polar_correlations= [(0, np.float64(0.376)), (1, np.float64(0.067)), (8, np.float64(0.067)), (-1, np.float64(0.052))]\nL3H4 projected_fraction=0.020 polar_correlations= [(0, np.float64(0.048)), (7, np.float64(0.044)), (-6, np.float64(0.042)), (1, np.float64(0.024))]\nL3H5 projected_fraction=0.016 polar_correlations= [(0, np.float64(0.098)), (-1, np.float64(0.048)), (1, np.float64(0.034)), (8, np.float64(0.03))]\n\nLAYER-NORM EXTREME COORDINATES\nln1_w.0 [(381, 0.0352), (42, 0.0606), (95, 0.0607), (17, 0.0622), (147, 0.0673), (237, 0.0678), (376, 0.0726), (334, 0.0736)]\nln1_b.0 [(312, -0.0954), (146, 0.0906), (12, 0.0856), (104, -0.0841), (289, 0.0826), (156, -0.078), (303, -0.0776), (328, 0.0762)]\nln2_w.0 [(372, 0.042), (354, 0.0926), (21, 0.1141), (163, 0.1286), (99, 0.187), (165, 0.2065), (47, 0.2106), (150, 1.775)]\nln2_b.0 [(99, -0.6236), (58, 0.4636), (153, 0.2132), (39, -0.202), (18, 0.1816), (29, -0.172), (383, -0.1652), (131, 0.1602)]\nln1_w.1 [(52, 0.197), (184, 0.2495), (227, 0.2585), (165, 0.2647), (192, 0.286), (163, 0.2875), (109, 0.2953), (39, 0.2985)]\nln1_b.1 [(39, -1.5676), (58, 1.3651), (99, -1.3355), (184, -0.5617), (383, -0.4622), (21, -0.4299), (153, 0.2969), (266, 0.2773)]\nln2_w.1 [(21, 0.0019), (99, 0.0568), (163, 0.2201), (58, 0.2217), (39, 0.3414), (165, 0.3562), (52, 0.4584), (242, 1.5196)]\nln2_b.1 [(58, 0.417), (375, 0.392), (76, -0.3892), (39, -0.3688), (319, 0.3552), (23, -0.3401), (358, 0.3149), (123, 0.3147)]\nln1_w.2 [(383, 0.3175), (184, 0.3373), (39, 0.4223), (336, 0.467), (58, 0.4775), (63, 0.4805), (266, 0.4913), (294, 0.4958)]\nln1_b.2 [(99, -1.6589), (58, 1.3318), (39, -1.1991), (383, -0.507), (184, -0.5046), (21, -0.4534), (63, -0.3245), (266, 0.2991)]\nln2_w.2 [(21, 0.012), (322, 1.5078), (53, 1.494), (97, 1.4847), (161, 1.4752), (374, 1.4683), (214, 1.4646), (222, 1.4615)]\nln2_b.2 [(236, -0.517), (73, -0.4803), (322, 0.4489), (214, -0.4232), (97, 0.4014), (382, -0.3917), (184, -0.3832), (364, 0.3749)]\nln1_w.3 [(383, 0.4078), (58, 0.4661), (39, 0.4723), (45, 0.5111), (266, 0.5241), (5, 0.541), (184, 0.5624), (200, 0.5872)]\nln1_b.3 [(58, 0.8241), (99, -0.7639), (5, 0.436), (383, -0.3974), (266, 0.3425), (21, -0.3152), (184, -0.2889), (45, 0.2888)]\nln2_w.3 [(21, 0.2328), (240, 1.4532), (368, 1.4452), (86, 1.4332), (301, 1.4273), (364, 1.4147), (339, 1.4136), (22, 1.4)]\nln2_b.3 [(21, -0.5402), (275, 0.3932), (215, -0.352), (382, -0.3297), (364, 0.3187), (168, 0.3137), (86, -0.3042), (22, -0.3012)]\n",
  "seconds": 0.664,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "dcda8a7996154e78c1b000a58bc1c1215c5d308f8188f4a7523727074a6e8e3c",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

EXACT HARNESS FEEDBACK
DETAILED SINGULAR VALUES
W_q.0 first 80: [4.368 3.131 3.099 3.042 2.994 2.951 2.926 2.881 2.859 2.857 2.804 2.766 2.762 2.692 2.683 2.661 2.637 2.588 2.539 2.509 2.458 2.433 2.403 2.299 2.272 2.173 2.098 2.048 1.954
 1.887 1.877 1.865 1.738 1.725 1.665 1.605 1.552 1.535 1.476 1.424 1.378 1.341 1.306 1.264 1.24  1.213 1.154 1.122 1.109 1.068 1.053 1.031 1.017 1.007 0.985 0.957 0.944 0.913
 0.888 0.879 0.868 0.864 0.837 0.822 0.795 0.789 0.778 0.769 0.765 0.754 0.742 0.731 0.72  0.711 0.704 0.697 0.691 0.682 0.679 0.672]
at indices 96,128,192,256,320,383: [0.572 0.467 0.32  0.205 0.1   0.004]
W_k.0 first 80: [4.163 3.952 3.467 3.427 3.364 3.335 3.279 3.256 3.214 3.163 3.115 3.029 2.977 2.901 2.881 2.832 2.798 2.76  2.658 2.584 2.528 2.449 2.353 2.301 2.281 2.206 2.121 2.074 1.917
 1.896 1.813 1.753 1.713 1.676 1.573 1.53  1.47  1.431 1.415 1.355 1.3   1.26  1.187 1.169 1.161 1.116 1.108 1.104 1.072 1.032 1.003 0.988 0.965 0.953 0.931 0.911 0.907 0.885
 0.873 0.866 0.85  0.841 0.834 0.815 0.792 0.782 0.768 0.749 0.739 0.731 0.726 0.725 0.723 0.716 0.71  0.698 0.69  0.689 0.679 0.666]
at indices 96,128,192,256,320,383: [0.585 0.484 0.339 0.215 0.108 0.001]
W_v.0 first 80: [5.208 3.364 3.006 2.962 2.815 2.639 2.527 2.46  2.37  2.301 2.25  2.23  2.151 2.135 2.044 2.039 2.001 1.981 1.91  1.875 1.822 1.773 1.749 1.708 1.634 1.611 1.556 1.487 1.453
 1.402 1.268 1.213 1.187 1.13  1.109 1.08  1.026 0.992 0.987 0.959 0.933 0.931 0.908 0.892 0.887 0.866 0.864 0.853 0.843 0.827 0.811 0.793 0.783 0.781 0.759 0.748 0.721 0.715
 0.71  0.701 0.698 0.675 0.663 0.659 0.652 0.647 0.64  0.626 0.626 0.619 0.614 0.607 0.6   0.596 0.593 0.589 0.585 0.584 0.579 0.573]
at indices 96,128,192,256,320,383: [0.515 0.435 0.306 0.195 0.098 0.001]
W_fc.0 first 80: [18.118 12.76  11.337  9.323  9.267  9.021  8.898  8.817  8.609  8.4    8.189  8.06   7.819  7.724  7.62   7.543  7.226  7.081  6.979  6.833  6.779  6.682  6.592  6.402  6.39
  6.306  6.207  6.18   5.974  5.864  5.789  5.741  5.646  5.579  5.512  5.353  5.196  5.193  5.122  5.053  5.029  4.849  4.646  4.573  4.452  4.163  4.133  3.899  3.888  3.557
  3.455  3.347  3.241  3.008  2.921  2.833  2.796  2.657  2.612  2.555  2.418  2.381  2.33   2.3    2.276  2.213  2.162  2.127  2.062  2.002  1.983  1.968  1.927  1.88   1.851
  1.832  1.806  1.777  1.766  1.741]
at indices 96,128,192,256,320,383: [1.496 1.236 0.95  0.766 0.613 0.433]
W_proj.0 first 80: [10.899  7.702  7.173  6.638  6.541  6.288  6.201  6.133  5.941  5.917  5.758  5.641  5.552  5.476  5.393  5.363  5.259  5.206  5.171  5.153  5.057  5.018  4.96   4.88   4.821
  4.788  4.77   4.73   4.643  4.579  4.546  4.529  4.505  4.493  4.44   4.415  4.369  4.315  4.293  4.261  4.243  4.185  4.134  4.104  4.064  4.049  4.026  3.988  3.967  3.95
  3.933  3.901  3.857  3.804  3.778  3.754  3.74   3.713  3.692  3.683  3.652  3.612  3.607  3.575  3.564  3.522  3.509  3.497  3.47   3.456  3.446  3.417  3.4    3.378  3.337
  3.302  3.291  3.269  3.238  3.23 ]
at indices 96,128,192,256,320,383: [2.883 2.339 1.479 0.935 0.586 0.321]
W_fc.1 first 80: [19.829 11.575 11.522 11.262 11.059 10.875 10.632 10.497 10.363 10.3   10.174 10.05   9.875  9.759  9.703  9.671  9.42   9.191  9.065  8.835  8.81   8.705  8.595  8.58   8.435
  8.206  8.123  8.078  7.899  7.826  7.758  7.676  7.635  7.592  7.463  7.395  7.33   7.244  7.185  7.132  7.062  6.82   6.764  6.645  6.591  6.568  6.514  6.48   6.426  6.346
  6.271  6.216  6.192  6.148  6.1    6.035  5.947  5.81   5.766  5.762  5.715  5.68   5.646  5.578  5.535  5.439  5.411  5.362  5.325  5.27   5.205  5.152  5.104  5.083  5.02
  5.     4.974  4.903  4.884  4.838]
at indices 96,128,192,256,320,383: [4.226 3.295 1.966 1.259 0.912 0.551]
W_proj.3 first 80: [28.461 22.214 21.34  20.708 19.943 19.662 18.986 18.744 18.295 17.457 17.132 16.561 15.329 14.874 14.328 13.438 12.667 12.455 12.317 11.531 11.047  9.084  8.353  8.24   6.37
  5.836  4.626  4.583  3.777  3.61   3.252  3.183  3.086  2.941  2.865  2.801  2.639  2.564  2.538  2.466  2.419  2.404  2.346  2.31   2.246  2.227  2.155  2.147  2.089  2.069
  2.065  2.041  1.975  1.936  1.921  1.917  1.857  1.848  1.83   1.82   1.798  1.779  1.764  1.737  1.729  1.719  1.709  1.692  1.68   1.661  1.645  1.635  1.619  1.594  1.589
  1.583  1.565  1.55   1.541  1.52 ]
at indices 96,128,192,256,320,383: [1.36  1.117 0.821 0.626 0.477 0.321]
W_fc.3 first 80: [15.114  8.611  8.486  8.073  7.769  7.529  7.449  7.334  7.094  6.971  6.891  6.808  6.601  6.537  6.454  6.331  6.294  6.232  6.162  6.115  6.058  6.034  5.926  5.878  5.85
  5.803  5.786  5.751  5.723  5.713  5.669  5.644  5.591  5.547  5.529  5.512  5.494  5.466  5.454  5.444  5.4    5.348  5.344  5.325  5.294  5.275  5.253  5.237  5.214  5.193
  5.186  5.17   5.139  5.13   5.098  5.091  5.061  5.059  5.046  5.036  5.014  5.001  4.972  4.962  4.951  4.912  4.906  4.885  4.869  4.859  4.837  4.808  4.787  4.778  4.77
  4.764  4.75   4.736  4.722  4.7  ]
at indices 96,128,192,256,320,383: [4.491 4.135 3.521 2.982 2.445 0.818]

L0 HEAD Q/K/V SINGULAR VALUES
H0 W_q: [2.679 2.108 1.778 1.572 1.462 1.364 1.345 1.302 1.245 1.219 1.192 1.16 ] tail8: [0.335 0.33  0.324 0.315 0.307 0.299 0.284 0.278]
H0 W_k: [2.798 2.259 1.759 1.613 1.568 1.472 1.427 1.373 1.327 1.296 1.283 1.218] tail8: [0.345 0.342 0.335 0.321 0.314 0.307 0.287 0.276]
H0 W_v: [1.941 1.825 1.681 1.437 1.343 1.314 1.22  1.165 1.151 1.123 1.078 1.013] tail8: [0.286 0.278 0.27  0.264 0.259 0.254 0.251 0.239]
H1 W_q: [2.424 1.574 1.242 0.96  0.905 0.802 0.687 0.587 0.566 0.527 0.51  0.509] tail8: [0.239 0.234 0.229 0.224 0.218 0.21  0.207 0.198]
H1 W_k: [2.798 1.612 1.417 1.068 0.886 0.857 0.784 0.602 0.579 0.569 0.54  0.528] tail8: [0.263 0.262 0.255 0.247 0.244 0.241 0.233 0.225]
H1 W_v: [1.826 1.676 1.062 0.965 0.815 0.696 0.66  0.645 0.625 0.595 0.582 0.565] tail8: [0.264 0.252 0.25  0.242 0.239 0.227 0.225 0.219]
H2 W_q: [2.046 1.194 1.147 1.051 0.764 0.701 0.601 0.571 0.535 0.522 0.516 0.482] tail8: [0.237 0.231 0.23  0.218 0.211 0.21  0.204 0.199]
H2 W_k: [2.164 1.259 1.13  1.038 0.837 0.752 0.7   0.679 0.597 0.579 0.547 0.536] tail8: [0.265 0.259 0.253 0.249 0.246 0.242 0.232 0.218]
H2 W_v: [1.569 1.363 0.945 0.883 0.778 0.688 0.672 0.64  0.609 0.597 0.558 0.551] tail8: [0.26  0.255 0.252 0.243 0.232 0.228 0.222 0.218]
H3 W_q: [2.962 2.894 2.873 2.845 2.806 2.792 2.766 2.756 2.703 2.634 2.588 2.561] tail8: [0.573 0.556 0.532 0.519 0.497 0.474 0.443 0.352]
H3 W_k: [3.362 3.238 3.209 3.181 3.099 3.086 3.024 2.999 2.951 2.923 2.856 2.765] tail8: [0.505 0.492 0.484 0.453 0.433 0.421 0.396 0.366]
H3 W_v: [2.102 1.948 1.903 1.866 1.843 1.819 1.769 1.755 1.674 1.651 1.629 1.621] tail8: [0.318 0.313 0.313 0.298 0.287 0.279 0.271 0.262]
H4 W_q: [2.076 1.29  1.127 0.896 0.868 0.699 0.665 0.626 0.597 0.57  0.528 0.524] tail8: [0.241 0.238 0.229 0.226 0.222 0.216 0.209 0.203]
H4 W_k: [2.007 1.451 1.356 1.117 0.975 0.833 0.809 0.705 0.682 0.674 0.656 0.581] tail8: [0.275 0.274 0.268 0.262 0.257 0.249 0.246 0.23 ]
H4 W_v: [1.252 1.117 0.818 0.786 0.737 0.658 0.626 0.612 0.602 0.565 0.542 0.537] tail8: [0.255 0.25  0.244 0.234 0.232 0.228 0.226 0.216]
H5 W_q: [1.229 0.674 0.419 0.398 0.395 0.392 0.382 0.373 0.371 0.366 0.359 0.348] tail8: [0.191 0.188 0.186 0.179 0.176 0.173 0.171 0.166]
H5 W_k: [1.181 0.612 0.509 0.503 0.482 0.467 0.463 0.449 0.446 0.442 0.428 0.427] tail8: [0.215 0.212 0.209 0.203 0.2   0.196 0.192 0.17 ]
H5 W_v: [4.667 3.073 2.351 2.151 2.011 1.857 1.567 1.379 1.248 1.229 1.111 1.018] tail8: [0.283 0.281 0.276 0.275 0.269 0.265 0.264 0.256]

L0 SUBSPACE OVERLAPS
H0 W_q energy in V_all top27/top40= 0.323 0.423 FC top27/top40= 0.159 0.225 H3_QK top40= 0.462
H0 W_k energy in V_all top27/top40= 0.352 0.455 FC top27/top40= 0.171 0.242 H3_QK top40= 0.51
H0 W_v energy in V_all top27/top40= 0.661 0.766 FC top27/top40= 0.23 0.436 H3_QK top40= 0.151
H1 W_q energy in V_all top27/top40= 0.332 0.398 FC top27/top40= 0.125 0.155 H3_QK top40= 0.276
H1 W_k energy in V_all top27/top40= 0.308 0.428 FC top27/top40= 0.211 0.263 H3_QK top40= 0.284
H1 W_v energy in V_all top27/top40= 0.511 0.604 FC top27/top40= 0.15 0.204 H3_QK top40= 0.353
H2 W_q energy in V_all top27/top40= 0.258 0.329 FC top27/top40= 0.101 0.131 H3_QK top40= 0.254
H2 W_k energy in V_all top27/top40= 0.245 0.361 FC top27/top40= 0.167 0.226 H3_QK top40= 0.274
H2 W_v energy in V_all top27/top40= 0.43 0.549 FC top27/top40= 0.124 0.178 H3_QK top40= 0.341
H3 W_q energy in V_all top27/top40= 0.094 0.265 FC top27/top40= 0.069 0.103 H3_QK top40= 0.901
H3 W_k energy in V_all top27/top40= 0.137 0.318 FC top27/top40= 0.066 0.094 H3_QK top40= 0.922
H3 W_v energy in V_all top27/top40= 0.773 0.853 FC top27/top40= 0.406 0.663 H3_QK top40= 0.17
H4 W_q energy in V_all top27/top40= 0.243 0.304 FC top27/top40= 0.113 0.147 H3_QK top40= 0.279
H4 W_k energy in V_all top27/top40= 0.22 0.329 FC top27/top40= 0.15 0.204 H3_QK top40= 0.351
H4 W_v energy in V_all top27/top40= 0.298 0.457 FC top27/top40= 0.109 0.149 H3_QK top40= 0.279
H5 W_q energy in V_all top27/top40= 0.232 0.284 FC top27/top40= 0.094 0.124 H3_QK top40= 0.211
H5 W_k energy in V_all top27/top40= 0.191 0.26 FC top27/top40= 0.138 0.202 H3_QK top40= 0.188
H5 W_v energy in V_all top27/top40= 0.852 0.899 FC top27/top40= 0.251 0.336 H3_QK top40= 0.517

RAW QK ROTATION COMPARISON WITH L0H3
L0H0 projected_fraction=0.403 polar_correlations= [(4, np.float64(0.116)), (5, np.float64(0.098)), (3, np.float64(0.078)), (-4, np.float64(0.058))]
L0H1 projected_fraction=0.215 polar_correlations= [(3, np.float64(0.067)), (4, np.float64(0.047)), (-2, np.float64(0.03)), (-1, np.float64(0.017))]
L0H2 projected_fraction=0.253 polar_correlations= [(3, np.float64(0.049)), (-2, np.float64(0.041)), (4, np.float64(0.022)), (-3, np.float64(0.018))]
L0H3 projected_fraction=0.963 polar_correlations= [(1, np.float64(0.784)), (2, np.float64(0.273)), (0, np.float64(0.273)), (-7, np.float64(0.15))]
L0H4 projected_fraction=0.335 polar_correlations= [(3, np.float64(0.05)), (2, np.float64(-0.015)), (-7, np.float64(-0.019)), (-2, np.float64(-0.025))]
L0H5 projected_fraction=0.300 polar_correlations= [(2, np.float64(0.073)), (3, np.float64(0.043)), (-6, np.float64(0.042)), (-7, np.float64(0.029))]
L1H0 projected_fraction=0.024 polar_correlations= [(1, np.float64(0.08)), (3, np.float64(0.053)), (2, np.float64(0.045)), (-8, np.float64(0.033))]
L1H1 projected_fraction=0.026 polar_correlations= [(1, np.float64(0.073)), (2, np.float64(0.06)), (3, np.float64(0.052)), (4, np.float64(0.036))]
L1H2 projected_fraction=0.023 polar_correlations= [(3, np.float64(0.057)), (1, np.float64(0.044)), (-6, np.float64(0.038)), (-8, np.float64(0.027))]
L1H3 projected_fraction=0.027 polar_correlations= [(3, np.float64(0.073)), (2, np.float64(0.072)), (-6, np.float64(0.053)), (1, np.float64(0.052))]
L1H4 projected_fraction=0.022 polar_correlations= [(3, np.float64(0.042)), (1, np.float64(0.015)), (2, np.float64(0.009)), (-3, np.float64(0.007))]
L1H5 projected_fraction=0.037 polar_correlations= [(1, np.float64(0.081)), (2, np.float64(0.062)), (3, np.float64(0.055)), (-6, np.float64(0.042))]
L2H0 projected_fraction=0.023 polar_correlations= [(3, np.float64(0.056)), (1, np.float64(0.055)), (2, np.float64(0.048)), (-6, np.float64(0.025))]
L2H1 projected_fraction=0.016 polar_correlations= [(2, np.float64(0.068)), (1, np.float64(0.033)), (-5, np.float64(0.032)), (-7, np.float64(0.028))]
L2H2 projected_fraction=0.017 polar_correlations= [(3, np.float64(0.038)), (-5, np.float64(0.037)), (-6, np.float64(0.036)), (2, np.float64(0.031))]
L2H3 projected_fraction=0.015 polar_correlations= [(1, np.float64(0.057)), (4, np.float64(0.036)), (3, np.float64(0.036)), (-7, np.float64(0.017))]
L2H4 projected_fraction=0.017 polar_correlations= [(2, np.float64(0.07)), (-6, np.float64(0.054)), (3, np.float64(0.05)), (-7, np.float64(0.045))]
L2H5 projected_fraction=0.017 polar_correlations= [(2, np.float64(0.07)), (3, np.float64(0.047)), (-6, np.float64(0.025)), (8, np.float64(0.021))]
L3H0 projected_fraction=0.015 polar_correlations= [(0, np.float64(0.132)), (1, np.float64(0.072)), (8, np.float64(0.048)), (-1, np.float64(0.043))]
L3H1 projected_fraction=0.017 polar_correlations= [(0, np.float64(0.048)), (-7, np.float64(0.035)), (1, np.float64(0.03)), (-5, np.float64(0.028))]
L3H2 projected_fraction=0.017 polar_correlations= [(1, np.float64(0.042)), (-6, np.float64(0.014)), (4, np.float64(0.011)), (-5, np.float64(0.01))]
L3H3 projected_fraction=0.016 polar_correlations= [(0, np.float64(0.376)), (1, np.float64(0.067)), (8, np.float64(0.067)), (-1, np.float64(0.052))]
L3H4 projected_fraction=0.020 polar_correlations= [(0, np.float64(0.048)), (7, np.float64(0.044)), (-6, np.float64(0.042)), (1, np.float64(0.024))]
L3H5 projected_fraction=0.016 polar_correlations= [(0, np.float64(0.098)), (-1, np.float64(0.048)), (1, np.float64(0.034)), (8, np.float64(0.03))]

LAYER-NORM EXTREME COORDINATES
ln1_w.0 [(381, 0.0352), (42, 0.0606), (95, 0.0607), (17, 0.0622), (147, 0.0673), (237, 0.0678), (376, 0.0726), (334, 0.0736)]
ln1_b.0 [(312, -0.0954), (146, 0.0906), (12, 0.0856), (104, -0.0841), (289, 0.0826), (156, -0.078), (303, -0.0776), (328, 0.0762)]
ln2_w.0 [(372, 0.042), (354, 0.0926), (21, 0.1141), (163, 0.1286), (99, 0.187), (165, 0.2065), (47, 0.2106), (150, 1.775)]
ln2_b.0 [(99, -0.6236), (58, 0.4636), (153, 0.2132), (39, -0.202), (18, 0.1816), (29, -0.172), (383, -0.1652), (131, 0.1602)]
ln1_w.1 [(52, 0.197), (184, 0.2495), (227, 0.2585), (165, 0.2647), (192, 0.286), (163, 0.2875), (109, 0.2953), (39, 0.2985)]
ln1_b.1 [(39, -1.5676), (58, 1.3651), (99, -1.3355), (184, -0.5617), (383, -0.4622), (21, -0.4299), (153, 0.2969), (266, 0.2773)]
ln2_w.1 [(21, 0.0019), (99, 0.0568), (163, 0.2201), (58, 0.2217), (39, 0.3414), (165, 0.3562), (52, 0.4584), (242, 1.5196)]
ln2_b.1 [(58, 0.417), (375, 0.392), (76, -0.3892), (39, -0.3688), (319, 0.3552), (23, -0.3401), (358, 0.3149), (123, 0.3147)]
ln1_w.2 [(383, 0.3175), (184, 0.3373), (39, 0.4223), (336, 0.467), (58, 0.4775), (63, 0.4805), (266, 0.4913), (294, 0.4958)]
ln1_b.2 [(99, -1.6589), (58, 1.3318), (39, -1.1991), (383, -0.507), (184, -0.5046), (21, -0.4534), (63, -0.3245), (266, 0.2991)]
ln2_w.2 [(21, 0.012), (322, 1.5078), (53, 1.494), (97, 1.4847), (161, 1.4752), (374, 1.4683), (214, 1.4646), (222, 1.4615)]
ln2_b.2 [(236, -0.517), (73, -0.4803), (322, 0.4489), (214, -0.4232), (97, 0.4014), (382, -0.3917), (184, -0.3832), (364, 0.3749)]
ln1_w.3 [(383, 0.4078), (58, 0.4661), (39, 0.4723), (45, 0.5111), (266, 0.5241), (5, 0.541), (184, 0.5624), (200, 0.5872)]
ln1_b.3 [(58, 0.8241), (99, -0.7639), (5, 0.436), (383, -0.3974), (266, 0.3425), (21, -0.3152), (184, -0.2889), (45, 0.2888)]
ln2_w.3 [(21, 0.2328), (240, 1.4532), (368, 1.4452), (86, 1.4332), (301, 1.4273), (364, 1.4147), (339, 1.4136), (22, 1.4)]
ln2_b.3 [(21, -0.5402), (275, 0.3932), (215, -0.352), (382, -0.3297), (364, 0.3187), (168, 0.3137), (86, -0.3042), (22, -0.3012)]


========================================================================
TURN t004
MESSAGE SENT TO CODEX
OUTPUT of run 3 of at most 100:
```
DETAILED SINGULAR VALUES
W_q.0 first 80: [4.368 3.131 3.099 3.042 2.994 2.951 2.926 2.881 2.859 2.857 2.804 2.766 2.762 2.692 2.683 2.661 2.637 2.588 2.539 2.509 2.458 2.433 2.403 2.299 2.272 2.173 2.098 2.048 1.954
 1.887 1.877 1.865 1.738 1.725 1.665 1.605 1.552 1.535 1.476 1.424 1.378 1.341 1.306 1.264 1.24  1.213 1.154 1.122 1.109 1.068 1.053 1.031 1.017 1.007 0.985 0.957 0.944 0.913
 0.888 0.879 0.868 0.864 0.837 0.822 0.795 0.789 0.778 0.769 0.765 0.754 0.742 0.731 0.72  0.711 0.704 0.697 0.691 0.682 0.679 0.672]
at indices 96,128,192,256,320,383: [0.572 0.467 0.32  0.205 0.1   0.004]
W_k.0 first 80: [4.163 3.952 3.467 3.427 3.364 3.335 3.279 3.256 3.214 3.163 3.115 3.029 2.977 2.901 2.881 2.832 2.798 2.76  2.658 2.584 2.528 2.449 2.353 2.301 2.281 2.206 2.121 2.074 1.917
 1.896 1.813 1.753 1.713 1.676 1.573 1.53  1.47  1.431 1.415 1.355 1.3   1.26  1.187 1.169 1.161 1.116 1.108 1.104 1.072 1.032 1.003 0.988 0.965 0.953 0.931 0.911 0.907 0.885
 0.873 0.866 0.85  0.841 0.834 0.815 0.792 0.782 0.768 0.749 0.739 0.731 0.726 0.725 0.723 0.716 0.71  0.698 0.69  0.689 0.679 0.666]
at indices 96,128,192,256,320,383: [0.585 0.484 0.339 0.215 0.108 0.001]
W_v.0 first 80: [5.208 3.364 3.006 2.962 2.815 2.639 2.527 2.46  2.37  2.301 2.25  2.23  2.151 2.135 2.044 2.039 2.001 1.981 1.91  1.875 1.822 1.773 1.749 1.708 1.634 1.611 1.556 1.487 1.453
 1.402 1.268 1.213 1.187 1.13  1.109 1.08  1.026 0.992 0.987 0.959 0.933 0.931 0.908 0.892 0.887 0.866 0.864 0.853 0.843 0.827 0.811 0.793 0.783 0.781 0.759 0.748 0.721 0.715
 0.71  0.701 0.698 0.675 0.663 0.659 0.652 0.647 0.64  0.626 0.626 0.619 0.614 0.607 0.6   0.596 0.593 0.589 0.585 0.584 0.579 0.573]
at indices 96,128,192,256,320,383: [0.515 0.435 0.306 0.195 0.098 0.001]
W_fc.0 first 80: [18.118 12.76  11.337  9.323  9.267  9.021  8.898  8.817  8.609  8.4    8.189  8.06   7.819  7.724  7.62   7.543  7.226  7.081  6.979  6.833  6.779  6.682  6.592  6.402  6.39
  6.306  6.207  6.18   5.974  5.864  5.789  5.741  5.646  5.579  5.512  5.353  5.196  5.193  5.122  5.053  5.029  4.849  4.646  4.573  4.452  4.163  4.133  3.899  3.888  3.557
  3.455  3.347  3.241  3.008  2.921  2.833  2.796  2.657  2.612  2.555  2.418  2.381  2.33   2.3    2.276  2.213  2.162  2.127  2.062  2.002  1.983  1.968  1.927  1.88   1.851
  1.832  1.806  1.777  1.766  1.741]
at indices 96,128,192,256,320,383: [1.496 1.236 0.95  0.766 0.613 0.433]
W_proj.0 first 80: [10.899  7.702  7.173  6.638  6.541  6.288  6.201  6.133  5.941  5.917  5.758  5.641  5.552  5.476  5.393  5.363  5.259  5.206  5.171  5.153  5.057  5.018  4.96   4.88   4.821
  4.788  4.77   4.73   4.643  4.579  4.546  4.529  4.505  4.493  4.44   4.415  4.369  4.315  4.293  4.261  4.243  4.185  4.134  4.104  4.064  4.049  4.026  3.988  3.967  3.95
  3.933  3.901  3.857  3.804  3.778  3.754  3.74   3.713  3.692  3.683  3.652  3.612  3.607  3.575  3.564  3.522  3.509  3.497  3.47   3.456  3.446  3.417  3.4    3.378  3.337
  3.302  3.291  3.269  3.238  3.23 ]
at indices 96,128,192,256,320,383: [2.883 2.339 1.479 0.935 0.586 0.321]
W_fc.1 first 80: [19.829 11.575 11.522 11.262 11.059 10.875 10.632 10.497 10.363 10.3   10.174 10.05   9.875  9.759  9.703  9.671  9.42   9.191  9.065  8.835  8.81   8.705  8.595  8.58   8.435
  8.206  8.123  8.078  7.899  7.826  7.758  7.676  7.635  7.592  7.463  7.395  7.33   7.244  7.185  7.132  7.062  6.82   6.764  6.645  6.591  6.568  6.514  6.48   6.426  6.346
  6.271  6.216  6.192  6.148  6.1    6.035  5.947  5.81   5.766  5.762  5.715  5.68   5.646  5.578  5.535  5.439  5.411  5.362  5.325  5.27   5.205  5.152  5.104  5.083  5.02
  5.     4.974  4.903  4.884  4.838]
at indices 96,128,192,256,320,383: [4.226 3.295 1.966 1.259 0.912 0.551]
W_proj.3 first 80: [28.461 22.214 21.34  20.708 19.943 19.662 18.986 18.744 18.295 17.457 17.132 16.561 15.329 14.874 14.328 13.438 12.667 12.455 12.317 11.531 11.047  9.084  8.353  8.24   6.37
  5.836  4.626  4.583  3.777  3.61   3.252  3.183  3.086  2.941  2.865  2.801  2.639  2.564  2.538  2.466  2.419  2.404  2.346  2.31   2.246  2.227  2.155  2.147  2.089  2.069
  2.065  2.041  1.975  1.936  1.921  1.917  1.857  1.848  1.83   1.82   1.798  1.779  1.764  1.737  1.729  1.719  1.709  1.692  1.68   1.661  1.645  1.635  1.619  1.594  1.589
  1.583  1.565  1.55   1.541  1.52 ]
at indices 96,128,192,256,320,383: [1.36  1.117 0.821 0.626 0.477 0.321]
W_fc.3 first 80: [15.114  8.611  8.486  8.073  7.769  7.529  7.449  7.334  7.094  6.971  6.891  6.808  6.601  6.537  6.454  6.331  6.294  6.232  6.162  6.115  6.058  6.034  5.926  5.878  5.85
  5.803  5.786  5.751  5.723  5.713  5.669  5.644  5.591  5.547  5.529  5.512  5.494  5.466  5.454  5.444  5.4    5.348  5.344  5.325  5.294  5.275  5.253  5.237  5.214  5.193
  5.186  5.17   5.139  5.13   5.098  5.091  5.061  5.059  5.046  5.036  5.014  5.001  4.972  4.962  4.951  4.912  4.906  4.885  4.869  4.859  4.837  4.808  4.787  4.778  4.77
  4.764  4.75   4.736  4.722  4.7  ]
at indices 96,128,192,256,320,383: [4.491 4.135 3.521 2.982 2.445 0.818]

L0 HEAD Q/K/V SINGULAR VALUES
H0 W_q: [2.679 2.108 1.778 1.572 1.462 1.364 1.345 1.302 1.245 1.219 1.192 1.16 ] tail8: [0.335 0.33  0.324 0.315 0.307 0.299 0.284 0.278]
H0 W_k: [2.798 2.259 1.759 1.613 1.568 1.472 1.427 1.373 1.327 1.296 1.283 1.218] tail8: [0.345 0.342 0.335 0.321 0.314 0.307 0.287 0.276]
H0 W_v: [1.941 1.825 1.681 1.437 1.343 1.314 1.22  1.165 1.151 1.123 1.078 1.013] tail8: [0.286 0.278 0.27  0.264 0.259 0.254 0.251 0.239]
H1 W_q: [2.424 1.574 1.242 0.96  0.905 0.802 0.687 0.587 0.566 0.527 0.51  0.509] tail8: [0.239 0.234 0.229 0.224 0.218 0.21  0.207 0.198]
H1 W_k: [2.798 1.612 1.417 1.068 0.886 0.857 0.784 0.602 0.579 0.569 0.54  0.528] tail8: [0.263 0.262 0.255 0.247 0.244 0.241 0.233 0.225]
H1 W_v: [1.826 1.676 1.062 0.965 0.815 0.696 0.66  0.645 0.625 0.595 0.582 0.565] tail8: [0.264 0.252 0.25  0.242 0.239 0.227 0.225 0.219]
H2 W_q: [2.046 1.194 1.147 1.051 0.764 0.701 0.601 0.571 0.535 0.522 0.516 0.482] tail8: [0.237 0.231 0.23  0.218 0.211 0.21  0.204 0.199]
H2 W_k: [2.164 1.259 1.13  1.038 0.837 0.752 0.7   0.679 0.597 0.579 0.547 0.536] tail8: [0.265 0.259 0.253 0.249 0.246 0.242 0.232 0.218]
H2 W_v: [1.569 1.363 0.945 0.883 0.778 0.688 0.672 0.64  0.609 0.597 0.558 0.551] tail8: [0.26  0.255 0.252 0.243 0.232 0.228 0.222 0.218]
H3 W_q: [2.962 2.894 2.873 2.845 2.806 2.792 2.766 2.756 2.703 2.634 2.588 2.561] tail8: [0.573 0.556 0.532 0.519 0.497 0.474 0.443 0.352]
H3 W_k: [3.362 3.238 3.209 3.181 3.099 3.086 3.024 2.999 2.951 2.923 2.856 2.765] tail8: [0.505 0.492 0.484 0.453 0.433 0.421 0.396 0.366]
H3 W_v: [2.102 1.948 1.903 1.866 1.843 1.819 1.769 1.755 1.674 1.651 1.629 1.621] tail8: [0.318 0.313 0.313 0.298 0.287 0.279 0.271 0.262]
H4 W_q: [2.076 1.29  1.127 0.896 0.868 0.699 0.665 0.626 0.597 0.57  0.528 0.524] tail8: [0.241 0.238 0.229 0.226 0.222 0.216 0.209 0.203]
H4 W_k: [2.007 1.451 1.356 1.117 0.975 0.833 0.809 0.705 0.682 0.674 0.656 0.581] tail8: [0.275 0.274 0.268 0.262 0.257 0.249 0.246 0.23 ]
H4 W_v: [1.252 1.117 0.818 0.786 0.737 0.658 0.626 0.612 0.602 0.565 0.542 0.537] tail8: [0.255 0.25  0.244 0.234 0.232 0.228 0.226 0.216]
H5 W_q: [1.229 0.674 0.419 0.398 0.395 0.392 0.382 0.373 0.371 0.366 0.359 0.348] tail8: [0.191 0.188 0.186 0.179 0.176 0.173 0.171 0.166]
H5 W_k: [1.181 0.612 0.509 0.503 0.482 0.467 0.463 0.449 0.446 0.442 0.428 0.427] tail8: [0.215 0.212 0.209 0.203 0.2   0.196 0.192 0.17 ]
H5 W_v: [4.667 3.073 2.351 2.151 2.011 1.857 1.567 1.379 1.248 1.229 1.111 1.018] tail8: [0.283 0.281 0.276 0.275 0.269 0.265 0.264 0.256]

L0 SUBSPACE OVERLAPS
H0 W_q energy in V_all top27/top40= 0.323 0.423 FC top27/top40= 0.159 0.225 H3_QK top40= 0.462
H0 W_k energy in V_all top27/top40= 0.352 0.455 FC top27/top40= 0.171 0.242 H3_QK top40= 0.51
H0 W_v energy in V_all top27/top40= 0.661 0.766 FC top27/top40= 0.23 0.436 H3_QK top40= 0.151
H1 W_q energy in V_all top27/top40= 0.332 0.398 FC top27/top40= 0.125 0.155 H3_QK top40= 0.276
H1 W_k energy in V_all top27/top40= 0.308 0.428 FC top27/top40= 0.211 0.263 H3_QK top40= 0.284
H1 W_v energy in V_all top27/top40= 0.511 0.604 FC top27/top40= 0.15 0.204 H3_QK top40= 0.353
H2 W_q energy in V_all top27/top40= 0.258 0.329 FC top27/top40= 0.101 0.131 H3_QK top40= 0.254
H2 W_k energy in V_all top27/top40= 0.245 0.361 FC top27/top40= 0.167 0.226 H3_QK top40= 0.274
H2 W_v energy in V_all top27/top40= 0.43 0.549 FC top27/top40= 0.124 0.178 H3_QK top40= 0.341
H3 W_q energy in V_all top27/top40= 0.094 0.265 FC top27/top40= 0.069 0.103 H3_QK top40= 0.901
H3 W_k energy in V_all top27/top40= 0.137 0.318 FC top27/top40= 0.066 0.094 H3_QK top40= 0.922
H3 W_v energy in V_all top27/top40= 0.773 0.853 FC top27/top40= 0.406 0.663 H3_QK top40= 0.17
H4 W_q energy in V_all top27/top40= 0.243 0.304 FC top27/top40= 0.113 0.147 H3_QK top40= 0.279
H4 W_k energy in V_all top27/top40= 0.22 0.329 FC top27/top40= 0.15 0.204 H3_QK top40= 0.351
H4 W_v energy in V_all top27/top40= 0.298 0.457 FC top27/top40= 0.109 0.149 H3_QK top40= 0.279
H5 W_q energy in V_all top27/top40= 0.232 0.284 FC top27/top40= 0.094 0.124 H3_QK top40= 0.211
H5 W_k energy in V_all top27/top40= 0.191 0.26 FC top27/top40= 0.138 0.202 H3_QK top40= 0.188
H5 W_v energy in V_all top27/top40= 0.852 0.899 FC top27/top40= 0.251 0.336 H3_QK top40= 0.517

RAW QK ROTATION COMPARISON WITH L0H3
L0H0 projected_fraction=0.403 polar_correlations= [(4, np.float64(0.116)), (5, np.float64(0.098)), (3, np.float64(0.078)), (-4, np.float64(0.058))]
L0H1 projected_fraction=0.215 polar_correlations= [(3, np.float64(0.067)), (4, np.float64(0.047)), (-2, np.float64(0.03)), (-1, np.float64(0.017))]
L0H2 projected_fraction=0.253 polar_correlations= [(3, np.float64(0.049)), (-2, np.float64(0.041)), (4, np.float64(0.022)), (-3, np.float64(0.018))]
L0H3 projected_fraction=0.963 polar_correlations= [(1, np.float64(0.784)), (2, np.float64(0.273)), (0, np.float64(0.273)), (-7, np.float64(0.15))]
L0H4 projected_fraction=0.335 polar_correlations= [(3, np.float64(0.05)), (2, np.float64(-0.015)), (-7, np.float64(-0.019)), (-2, np.float64(-0.025))]
L0H5 projected_fraction=0.300 polar_correlations= [(2, np.float64(0.073)), (3, np.float64(0.043)), (-6, np.float64(0.042)), (-7, np.float64(0.029))]
L1H0 projected_fraction=0.024 polar_correlations= [(1, np.float64(0.08)), (3, np.float64(0.053)), (2, np.float64(0.045)), (-8, np.float64(0.033))]
L1H1 projected_fraction=0.026 polar_correlations= [(1, np.float64(0.073)), (2, np.float64(0.06)), (3, np.float64(0.052)), (4, np.float64(0.036))]
L1H2 projected_fraction=0.023 polar_correlations= [(3, np.float64(0.057)), (1, np.float64(0.044)), (-6, np.float64(0.038)), (-8, np.float64(0.027))]
L1H3 projected_fraction=0.027 polar_correlations= [(3, np.float64(0.073)), (2, np.float64(0.072)), (-6, np.float64(0.053)), (1, np.float64(0.052))]
L1H4 projected_fraction=0.022 polar_correlations= [(3, np.float64(0.042)), (1, np.float64(0.015)), (2, np.float64(0.009)), (-3, np.float64(0.007))]
L1H5 projected_fraction=0.037 polar_correlations= [(1, np.float64(0.081)), (2, np.float64(0.062)), (3, np.float64(0.055)), (-6, np.float64(0.042))]
L2H0 projected_fraction=0.023 polar_correlations= [(3, np.float64(0.056)), (1, np.float64(0.055)), (2, np.float64(0.048)), (-6, np.float64(0.025))]
L2H1 projected_fraction=0.016 polar_correlations= [(2, np.float64(0.068)), (1, np.float64(0.033)), (-5, np.float64(0.032)), (-7, np.float64(0.028))]
L2H2 projected_fraction=0.017 polar_correlations= [(3, np.float64(0.038)), (-5, np.float64(0.037)), (-6, np.float64(0.036)), (2, np.float64(0.031))]
L2H3 projected_fraction=0.015 polar_correlations= [(1, np.float64(0.057)), (4, np.float64(0.036)), (3, np.float64(0.036)), (-7, np.float64(0.017))]
L2H4 projected_fraction=0.017 polar_correlations= [(2, np.float64(0.07)), (-6, np.float64(0.054)), (3, np.float64(0.05)), (-7, np.float64(0.045))]
L2H5 projected_fraction=0.017 polar_correlations= [(2, np.float64(0.07)), (3, np.float64(0.047)), (-6, np.float64(0.025)), (8, np.float64(0.021))]
L3H0 projected_fraction=0.015 polar_correlations= [(0, np.float64(0.132)), (1, np.float64(0.072)), (8, np.float64(0.048)), (-1, np.float64(0.043))]
L3H1 projected_fraction=0.017 polar_correlations= [(0, np.float64(0.048)), (-7, np.float64(0.035)), (1, np.float64(0.03)), (-5, np.float64(0.028))]
L3H2 projected_fraction=0.017 polar_correlations= [(1, np.float64(0.042)), (-6, np.float64(0.014)), (4, np.float64(0.011)), (-5, np.float64(0.01))]
L3H3 projected_fraction=0.016 polar_correlations= [(0, np.float64(0.376)), (1, np.float64(0.067)), (8, np.float64(0.067)), (-1, np.float64(0.052))]
L3H4 projected_fraction=0.020 polar_correlations= [(0, np.float64(0.048)), (7, np.float64(0.044)), (-6, np.float64(0.042)), (1, np.float64(0.024))]
L3H5 projected_fraction=0.016 polar_correlations= [(0, np.float64(0.098)), (-1, np.float64(0.048)), (1, np.float64(0.034)), (8, np.float64(0.03))]

LAYER-NORM EXTREME COORDINATES
ln1_w.0 [(381, 0.0352), (42, 0.0606), (95, 0.0607), (17, 0.0622), (147, 0.0673), (237, 0.0678), (376, 0.0726), (334, 0.0736)]
ln1_b.0 [(312, -0.0954), (146, 0.0906), (12, 0.0856), (104, -0.0841), (289, 0.0826), (156, -0.078), (303, -0.0776), (328, 0.0762)]
ln2_w.0 [(372, 0.042), (354, 0.0926), (21, 0.1141), (163, 0.1286), (99, 0.187), (165, 0.2065), (47, 0.2106), (150, 1.775)]
ln2_b.0 [(99, -0.6236), (58, 0.4636), (153, 0.2132), (39, -0.202), (18, 0.1816), (29, -0.172), (383, -0.1652), (131, 0.1602)]
ln1_w.1 [(52, 0.197), (184, 0.2495), (227, 0.2585), (165, 0.2647), (192, 0.286), (163, 0.2875), (109, 0.2953), (39, 0.2985)]
ln1_b.1 [(39, -1.5676), (58, 1.3651), (99, -1.3355), (184, -0.5617), (383, -0.4622), (21, -0.4299), (153, 0.2969), (266, 0.2773)]
ln2_w.1 [(21, 0.0019), (99, 0.0568), (163, 0.2201), (58, 0.2217), (39, 0.3414), (165, 0.3562), (52, 0.4584), (242, 1.5196)]
ln2_b.1 [(58, 0.417), (375, 0.392), (76, -0.3892), (39, -0.3688), (319, 0.3552), (23, -0.3401), (358, 0.3149), (123, 0.3147)]
ln1_w.2 [(383, 0.3175), (184, 0.3373), (39, 0.4223), (336, 0.467), (58, 0.4775), (63, 0.4805), (266, 0.4913), (294, 0.4958)]
ln1_b.2 [(99, -1.6589), (58, 1.3318), (39, -1.1991), (383, -0.507), (184, -0.5046), (21, -0.4534), (63, -0.3245), (266, 0.2991)]
ln2_w.2 [(21, 0.012), (322, 1.5078), (53, 1.494), (97, 1.4847), (161, 1.4752), (374, 1.4683), (214, 1.4646), (222, 1.4615)]
ln2_b.2 [(236, -0.517), (73, -0.4803), (322, 0.4489), (214, -0.4232), (97, 0.4014), (382, -0.3917), (184, -0.3832), (364, 0.3749)]
ln1_w.3 [(383, 0.4078), (58, 0.4661), (39, 0.4723), (45, 0.5111), (266, 0.5241), (5, 0.541), (184, 0.5624), (200, 0.5872)]
ln1_b.3 [(58, 0.8241), (99, -0.7639), (5, 0.436), (383, -0.3974), (266, 0.3425), (21, -0.3152), (184, -0.2889), (45, 0.2888)]
ln2_w.3 [(21, 0.2328), (240, 1.4532), (368, 1.4452), (86, 1.4332), (301, 1.4273), (364, 1.4147), (339, 1.4136), (22, 1.4)]
ln2_b.3 [(21, -0.5402), (275, 0.3932), (215, -0.352), (382, -0.3297), (364, 0.3187), (168, 0.3137), (86, -0.3042), (22, -0.3012)]

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

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

AGENT_MESSAGE
```python
import numpy as np
from threadpoolctl import threadpool_limits
threadpool_limits(2)
np.set_printoptions(precision=3, suppress=True, linewidth=180)

print("L0H3 VALUE/OUTPUT SPECTRA: possible alphabet-size evidence, not character labels")
for name,a in [
    ("V",W["W_v.0"][192:256]),
    ("O",W["W_o.0"][:,192:256]),
    ("V times ln1 gain",W["W_v.0"][192:256]*W["ln1_w.0"]),
    ("OV",(W["W_o.0"][:,192:256].astype(float)@W["W_v.0"][192:256].astype(float))*W["ln1_w.0"])
]:
    print(name,np.linalg.svd(a.astype(float),compute_uv=False)[:64])

print("\nEARLY OV DOMINANT DIRECTIONS")
Bs={}
As={}
for j in range(6):
    sl=slice(j*64,(j+1)*64)
    g=W["ln1_w.0"].astype(float)
    Bs[j]=(W["W_o.0"][:,sl].astype(float)@W["W_v.0"][sl].astype(float))*g
    As[j]=(W["W_q.0"][sl].astype(float)*g).T@(W["W_k.0"][sl].astype(float)*g)/8
    u,s,vt=np.linalg.svd(Bs[j],full_matrices=False)
    for r in range(2):
        ii=np.argsort(abs(u[:,r]))[-8:][::-1]
        jj=np.argsort(abs(vt[r]))[-8:][::-1]
        print(f"H{j} component {r} s={s[r]:.3f}",
              "output",[(int(i),round(float(u[i,r]),3)) for i in ii],
              "input",[(int(i),round(float(vt[r,i]),3)) for i in jj])

print("\nLATER AFFINE KEY DIRECTIONS VS L0 OV OUTPUT SUBSPACES")
Ub={}
for j in range(6):
    Ub[j]=np.linalg.svd(Bs[j],full_matrices=False)[0]
for l in range(1,4):
    g=W[f"ln1_w.{l}"].astype(float)
    b=W[f"ln1_b.{l}"].astype(float)
    for j in range(6):
        sl=slice(j*64,(j+1)*64)
        q=W[f"W_q.{l}"][sl].astype(float)
        k=W[f"W_k.{l}"][sl].astype(float)
        z=(k*g).T@(q@b)/8
        ovfrac=[np.linalg.norm(Ub[h][:,:2].T@z)**2/(z@z) for h in range(6)]
        ii=np.argsort(abs(z))[-5:][::-1]
        print(f"L{l}H{j}: fractions",np.array(ovfrac),
              "largest coordinates",[(int(i),round(float(z[i]),3)) for i in ii])

print("\nMLP0 ROW GEOMETRY")
fc=W["W_fc.0"].astype(float)*W["ln2_w.0"]
_,sv,vt=np.linalg.svd(W["W_v.0"][192:256].astype(float)*W["ln1_w.0"],full_matrices=False)
T=vt[:27].T
cur=fc@T
prev=fc@Bs[3]@T
for name,x in [("current weight projection",cur),("previous-head path projection",prev)]:
    s=np.linalg.svd(x,compute_uv=False)
    print(name,"singular values",s)
    norm=np.linalg.norm(x,axis=1)
    print("row norm percentiles",np.percentile(norm,[0,10,25,50,75,90,99,100]))
cross=cur.T@prev
s=np.linalg.svd(cross,compute_uv=False)
print("current/previous weight cross spectrum",s)
print("cross symmetric eigenvalues",np.linalg.eigvalsh((cross+cross.T)/2))
print("cross trace/F",np.trace(cross)/np.linalg.norm(cross))
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 4,
  "turn": "t004",
  "code": "import numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=3, suppress=True, linewidth=180)\n\nprint(\"L0H3 VALUE/OUTPUT SPECTRA: possible alphabet-size evidence, not character labels\")\nfor name,a in [\n    (\"V\",W[\"W_v.0\"][192:256]),\n    (\"O\",W[\"W_o.0\"][:,192:256]),\n    (\"V times ln1 gain\",W[\"W_v.0\"][192:256]*W[\"ln1_w.0\"]),\n    (\"OV\",(W[\"W_o.0\"][:,192:256].astype(float)@W[\"W_v.0\"][192:256].astype(float))*W[\"ln1_w.0\"])\n]:\n    print(name,np.linalg.svd(a.astype(float),compute_uv=False)[:64])\n\nprint(\"\\nEARLY OV DOMINANT DIRECTIONS\")\nBs={}\nAs={}\nfor j in range(6):\n    sl=slice(j*64,(j+1)*64)\n    g=W[\"ln1_w.0\"].astype(float)\n    Bs[j]=(W[\"W_o.0\"][:,sl].astype(float)@W[\"W_v.0\"][sl].astype(float))*g\n    As[j]=(W[\"W_q.0\"][sl].astype(float)*g).T@(W[\"W_k.0\"][sl].astype(float)*g)/8\n    u,s,vt=np.linalg.svd(Bs[j],full_matrices=False)\n    for r in range(2):\n        ii=np.argsort(abs(u[:,r]))[-8:][::-1]\n        jj=np.argsort(abs(vt[r]))[-8:][::-1]\n        print(f\"H{j} component {r} s={s[r]:.3f}\",\n              \"output\",[(int(i),round(float(u[i,r]),3)) for i in ii],\n              \"input\",[(int(i),round(float(vt[r,i]),3)) for i in jj])\n\nprint(\"\\nLATER AFFINE KEY DIRECTIONS VS L0 OV OUTPUT SUBSPACES\")\nUb={}\nfor j in range(6):\n    Ub[j]=np.linalg.svd(Bs[j],full_matrices=False)[0]\nfor l in range(1,4):\n    g=W[f\"ln1_w.{l}\"].astype(float)\n    b=W[f\"ln1_b.{l}\"].astype(float)\n    for j in range(6):\n        sl=slice(j*64,(j+1)*64)\n        q=W[f\"W_q.{l}\"][sl].astype(float)\n        k=W[f\"W_k.{l}\"][sl].astype(float)\n        z=(k*g).T@(q@b)/8\n        ovfrac=[np.linalg.norm(Ub[h][:,:2].T@z)**2/(z@z) for h in range(6)]\n        ii=np.argsort(abs(z))[-5:][::-1]\n        print(f\"L{l}H{j}: fractions\",np.array(ovfrac),\n              \"largest coordinates\",[(int(i),round(float(z[i]),3)) for i in ii])\n\nprint(\"\\nMLP0 ROW GEOMETRY\")\nfc=W[\"W_fc.0\"].astype(float)*W[\"ln2_w.0\"]\n_,sv,vt=np.linalg.svd(W[\"W_v.0\"][192:256].astype(float)*W[\"ln1_w.0\"],full_matrices=False)\nT=vt[:27].T\ncur=fc@T\nprev=fc@Bs[3]@T\nfor name,x in [(\"current weight projection\",cur),(\"previous-head path projection\",prev)]:\n    s=np.linalg.svd(x,compute_uv=False)\n    print(name,\"singular values\",s)\n    norm=np.linalg.norm(x,axis=1)\n    print(\"row norm percentiles\",np.percentile(norm,[0,10,25,50,75,90,99,100]))\ncross=cur.T@prev\ns=np.linalg.svd(cross,compute_uv=False)\nprint(\"current/previous weight cross spectrum\",s)\nprint(\"cross symmetric eigenvalues\",np.linalg.eigvalsh((cross+cross.T)/2))\nprint(\"cross trace/F\",np.trace(cross)/np.linalg.norm(cross))",
  "out": "L0H3 VALUE/OUTPUT SPECTRA: possible alphabet-size evidence, not character labels\nV [2.102 1.948 1.903 1.866 1.843 1.819 1.769 1.755 1.674 1.651 1.629 1.621 1.589 1.553 1.52  1.465 1.417 1.303 1.16  1.101 0.945 0.93  0.848 0.818 0.79  0.779 0.745 0.739 0.73\n 0.701 0.688 0.658 0.642 0.622 0.618 0.605 0.575 0.565 0.543 0.522 0.515 0.491 0.481 0.466 0.446 0.43  0.41  0.4   0.378 0.366 0.359 0.349 0.345 0.342 0.336 0.329 0.318 0.313\n 0.313 0.298 0.287 0.279 0.271 0.262]\nO [2.133 2.11  2.076 1.975 1.963 1.937 1.913 1.891 1.878 1.845 1.823 1.813 1.78  1.737 1.713 1.678 1.632 1.457 1.395 1.326 1.286 1.272 1.214 1.201 1.19  1.131 1.101 1.078 1.065\n 1.011 0.994 0.956 0.935 0.916 0.864 0.817 0.756 0.74  0.713 0.688 0.634 0.562 0.535 0.469 0.423 0.373 0.357 0.349 0.321 0.297 0.28  0.267 0.259 0.249 0.235 0.228 0.217 0.216\n 0.198 0.187 0.184 0.172 0.145 0.127]\nV times ln1 gain [0.57  0.513 0.504 0.472 0.47  0.451 0.44  0.434 0.425 0.412 0.39  0.383 0.38  0.367 0.36  0.344 0.339 0.337 0.324 0.298 0.286 0.276 0.273 0.266 0.255 0.249 0.245 0.241 0.237\n 0.232 0.225 0.223 0.22  0.21  0.206 0.204 0.197 0.194 0.183 0.18  0.173 0.169 0.16  0.151 0.15  0.138 0.134 0.132 0.124 0.121 0.116 0.11  0.108 0.107 0.103 0.101 0.097 0.093\n 0.09  0.087 0.085 0.081 0.078 0.072]\nOV [1.127 0.955 0.95  0.89  0.864 0.854 0.844 0.78  0.759 0.723 0.709 0.674 0.67  0.652 0.634 0.617 0.565 0.528 0.438 0.382 0.35  0.317 0.297 0.295 0.282 0.276 0.265 0.246 0.24\n 0.233 0.225 0.213 0.209 0.197 0.189 0.172 0.159 0.149 0.137 0.133 0.111 0.098 0.083 0.07  0.061 0.052 0.047 0.046 0.039 0.034 0.032 0.031 0.027 0.026 0.025 0.023 0.022 0.021\n 0.021 0.018 0.018 0.015 0.013 0.011]\n\nEARLY OV DOMINANT DIRECTIONS\nH0 component 0 s=0.958 output [(336, 0.194), (96, 0.158), (291, -0.15), (327, 0.149), (203, -0.14), (202, -0.128), (330, 0.12), (68, -0.117)] input [(373, 0.174), (225, -0.164), (134, -0.158), (158, -0.155), (7, 0.152), (51, 0.148), (96, -0.142), (173, -0.132)]\nH0 component 1 s=0.898 output [(39, -0.262), (6, -0.192), (282, 0.186), (156, 0.148), (163, 0.136), (152, 0.133), (342, 0.128), (219, -0.127)] input [(226, -0.165), (301, 0.154), (21, -0.146), (358, -0.145), (220, -0.13), (325, -0.129), (348, -0.127), (61, 0.126)]\nH1 component 0 s=3.380 output [(39, 0.692), (58, -0.51), (99, 0.358), (21, -0.212), (184, 0.201), (153, -0.054), (336, -0.038), (63, 0.037)] input [(184, 0.347), (180, 0.315), (99, 0.271), (39, 0.267), (56, -0.254), (317, -0.239), (69, 0.235), (256, -0.214)]\nH1 component 1 s=2.023 output [(21, 0.961), (39, 0.158), (58, -0.129), (184, 0.072), (306, -0.032), (173, -0.029), (225, -0.028), (232, -0.026)] input [(211, 0.341), (223, -0.262), (228, -0.244), (65, 0.232), (173, -0.229), (124, -0.213), (60, -0.212), (26, 0.189)]\nH2 component 0 s=2.643 output [(39, -0.659), (58, 0.494), (21, 0.373), (99, -0.355), (184, -0.162), (153, 0.045), (383, -0.035), (165, -0.029)] input [(184, -0.365), (180, -0.304), (99, -0.298), (317, 0.248), (56, 0.234), (39, -0.232), (69, -0.208), (301, -0.195)]\nH2 component 1 s=1.363 output [(21, 0.91), (39, 0.221), (99, 0.207), (58, -0.201), (184, 0.053), (252, -0.03), (351, -0.03), (153, -0.029)] input [(211, 0.3), (173, -0.273), (223, -0.256), (162, 0.227), (256, -0.225), (65, 0.222), (89, -0.205), (228, -0.196)]\nH3 component 0 s=1.127 output [(92, 0.178), (86, -0.169), (188, 0.163), (18, -0.16), (131, -0.158), (303, -0.156), (215, -0.148), (173, -0.139)] input [(223, 0.227), (26, 0.198), (200, 0.172), (48, 0.168), (359, -0.144), (124, -0.141), (178, 0.137), (354, -0.133)]\nH3 component 1 s=0.955 output [(65, -0.191), (125, -0.18), (320, 0.174), (202, 0.168), (86, 0.158), (287, 0.152), (20, -0.142), (350, -0.136)] input [(200, 0.228), (124, 0.181), (53, -0.178), (329, 0.169), (74, -0.167), (105, -0.159), (154, -0.15), (363, 0.15)]\nH4 component 0 s=1.454 output [(39, -0.696), (58, 0.511), (99, -0.32), (184, -0.227), (21, -0.182), (153, 0.057), (336, 0.042), (350, 0.039)] input [(184, -0.414), (180, -0.29), (256, 0.279), (56, 0.243), (100, 0.236), (69, -0.226), (317, 0.207), (162, -0.199)]\nH4 component 1 s=0.724 output [(21, 0.941), (99, -0.241), (39, -0.097), (58, 0.092), (184, 0.042), (148, -0.033), (52, -0.032), (381, 0.031)] input [(211, 0.354), (228, -0.271), (65, 0.22), (124, -0.217), (26, 0.216), (319, 0.214), (223, -0.201), (59, 0.188)]\nH5 component 0 s=16.282 output [(21, -0.864), (99, -0.358), (58, 0.237), (39, -0.232), (184, 0.024), (153, 0.019), (303, 0.019), (6, 0.017)] input [(162, -0.329), (211, -0.307), (223, 0.277), (65, -0.236), (354, 0.201), (173, 0.2), (132, -0.196), (300, -0.192)]\nH5 component 1 s=8.786 output [(39, -0.608), (58, 0.486), (21, 0.469), (99, -0.404), (180, -0.035), (165, -0.026), (153, 0.021), (86, -0.015)] input [(99, -0.371), (180, -0.317), (184, -0.311), (162, -0.225), (100, 0.207), (240, -0.205), (317, 0.205), (39, -0.203)]\n\nLATER AFFINE KEY DIRECTIONS VS L0 OV OUTPUT SUBSPACES\nL1H0: fractions [0.001 0.82  0.833 0.    0.783 0.847] largest coordinates [(21, 3.627), (39, -0.474), (58, 0.358), (329, -0.211), (304, -0.198)]\nL1H1: fractions [0.    0.822 0.836 0.006 0.767 0.848] largest coordinates [(21, 3.352), (58, 0.506), (39, -0.427), (57, 0.256), (187, 0.193)]\nL1H2: fractions [0.    0.817 0.831 0.    0.766 0.849] largest coordinates [(21, 2.769), (58, 0.4), (39, -0.389), (215, 0.212), (369, -0.195)]\nL1H3: fractions [0.    0.827 0.833 0.009 0.793 0.848] largest coordinates [(21, 3.079), (58, 0.459), (39, -0.423), (170, -0.29), (242, 0.209)]\nL1H4: fractions [0.001 0.788 0.783 0.005 0.764 0.792] largest coordinates [(21, 2.008), (58, 0.505), (39, -0.333), (150, 0.179), (144, -0.164)]\nL1H5: fractions [0.    0.837 0.839 0.001 0.796 0.849] largest coordinates [(21, 3.563), (39, -0.485), (58, 0.399), (351, -0.267), (210, -0.249)]\nL2H0: fractions [0.    0.811 0.818 0.003 0.777 0.828] largest coordinates [(21, 1.815), (39, -0.266), (58, 0.248), (294, -0.155), (351, 0.141)]\nL2H1: fractions [0.    0.815 0.799 0.    0.78  0.809] largest coordinates [(21, 1.682), (99, -0.208), (58, 0.201), (39, -0.199), (255, -0.119)]\nL2H2: fractions [0.001 0.789 0.768 0.003 0.778 0.772] largest coordinates [(21, 1.411), (99, -0.238), (58, 0.156), (39, -0.151), (306, 0.099)]\nL2H3: fractions [0.003 0.62  0.624 0.002 0.589 0.611] largest coordinates [(21, 0.803), (58, 0.158), (39, -0.133), (99, -0.117), (363, 0.096)]\nL2H4: fractions [0.001 0.783 0.805 0.    0.745 0.826] largest coordinates [(21, 2.069), (58, 0.32), (39, -0.29), (361, -0.13), (2, 0.126)]\nL2H5: fractions [0.001 0.83  0.836 0.002 0.786 0.847] largest coordinates [(21, 1.859), (58, 0.204), (39, -0.17), (40, 0.102), (214, 0.1)]\nL3H0: fractions [0.004 0.708 0.692 0.014 0.687 0.685] largest coordinates [(21, 0.408), (39, -0.069), (99, -0.067), (307, 0.041), (58, 0.04)]\nL3H1: fractions [0.041 0.508 0.493 0.007 0.482 0.474] largest coordinates [(21, 0.181), (39, -0.058), (184, -0.048), (99, -0.046), (37, 0.03)]\nL3H2: fractions [0.002 0.649 0.623 0.008 0.639 0.615] largest coordinates [(21, 0.382), (99, -0.095), (39, -0.065), (25, -0.044), (136, -0.037)]\nL3H3: fractions [0.013 0.009 0.01  0.005 0.009 0.008] largest coordinates [(193, -0.036), (157, 0.031), (198, -0.029), (339, -0.029), (129, -0.029)]\nL3H4: fractions [0.112 0.037 0.043 0.001 0.032 0.037] largest coordinates [(163, -0.031), (21, 0.028), (120, 0.023), (355, -0.022), (269, -0.021)]\nL3H5: fractions [0.001 0.737 0.734 0.006 0.691 0.721] largest coordinates [(21, 0.416), (39, -0.064), (184, -0.059), (22, 0.046), (43, 0.043)]\n\nMLP0 ROW GEOMETRY\ncurrent weight projection singular values [11.423  8.653  7.882  7.526  7.341  7.074  6.711  6.614  6.451  5.991  5.713  5.275  5.123  4.988  4.568  4.471  4.337  4.154  4.05   3.687  2.842  2.48   1.813  1.73   1.422\n  1.334  1.069]\nrow norm percentiles [0.058 0.102 0.122 0.185 0.905 1.334 1.93  2.293]\nprevious-head path projection singular values [9.585 6.439 6.141 5.846 5.572 5.342 5.241 5.049 4.705 4.621 4.247 3.991 3.704 3.634 3.478 2.674 2.396 1.701 1.277 0.899 0.859 0.484 0.373 0.35  0.297 0.27  0.252]\nrow norm percentiles [0.035 0.052 0.063 0.093 0.649 1.022 1.463 2.334]\ncurrent/previous weight cross spectrum [62.36  22.22  17.069 16.491 14.485 12.829 11.603  9.646  8.392  6.824  6.069  5.712  4.763  4.273  3.802  3.06   2.612  2.219  2.058  1.591  0.764  0.526  0.267  0.212  0.148\n  0.055  0.002]\ncross symmetric eigenvalues [-12.298 -11.004  -8.306  -7.578  -6.48   -4.526  -2.956  -2.131  -1.505  -0.943  -0.71   -0.46   -0.065   0.379   0.46    1.018   1.305   1.424   2.432   3.238   4.683   5.521\n   6.414   9.651  10.927  15.3    52.67 ]\ncross trace/F 0.7401727646232719\n",
  "seconds": 0.558,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "8c670fcbeb7612b7ad65c4361fc26ee33da86c609cb284ded2811ad087b41307",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

EXACT HARNESS FEEDBACK
L0H3 VALUE/OUTPUT SPECTRA: possible alphabet-size evidence, not character labels
V [2.102 1.948 1.903 1.866 1.843 1.819 1.769 1.755 1.674 1.651 1.629 1.621 1.589 1.553 1.52  1.465 1.417 1.303 1.16  1.101 0.945 0.93  0.848 0.818 0.79  0.779 0.745 0.739 0.73
 0.701 0.688 0.658 0.642 0.622 0.618 0.605 0.575 0.565 0.543 0.522 0.515 0.491 0.481 0.466 0.446 0.43  0.41  0.4   0.378 0.366 0.359 0.349 0.345 0.342 0.336 0.329 0.318 0.313
 0.313 0.298 0.287 0.279 0.271 0.262]
O [2.133 2.11  2.076 1.975 1.963 1.937 1.913 1.891 1.878 1.845 1.823 1.813 1.78  1.737 1.713 1.678 1.632 1.457 1.395 1.326 1.286 1.272 1.214 1.201 1.19  1.131 1.101 1.078 1.065
 1.011 0.994 0.956 0.935 0.916 0.864 0.817 0.756 0.74  0.713 0.688 0.634 0.562 0.535 0.469 0.423 0.373 0.357 0.349 0.321 0.297 0.28  0.267 0.259 0.249 0.235 0.228 0.217 0.216
 0.198 0.187 0.184 0.172 0.145 0.127]
V times ln1 gain [0.57  0.513 0.504 0.472 0.47  0.451 0.44  0.434 0.425 0.412 0.39  0.383 0.38  0.367 0.36  0.344 0.339 0.337 0.324 0.298 0.286 0.276 0.273 0.266 0.255 0.249 0.245 0.241 0.237
 0.232 0.225 0.223 0.22  0.21  0.206 0.204 0.197 0.194 0.183 0.18  0.173 0.169 0.16  0.151 0.15  0.138 0.134 0.132 0.124 0.121 0.116 0.11  0.108 0.107 0.103 0.101 0.097 0.093
 0.09  0.087 0.085 0.081 0.078 0.072]
OV [1.127 0.955 0.95  0.89  0.864 0.854 0.844 0.78  0.759 0.723 0.709 0.674 0.67  0.652 0.634 0.617 0.565 0.528 0.438 0.382 0.35  0.317 0.297 0.295 0.282 0.276 0.265 0.246 0.24
 0.233 0.225 0.213 0.209 0.197 0.189 0.172 0.159 0.149 0.137 0.133 0.111 0.098 0.083 0.07  0.061 0.052 0.047 0.046 0.039 0.034 0.032 0.031 0.027 0.026 0.025 0.023 0.022 0.021
 0.021 0.018 0.018 0.015 0.013 0.011]

EARLY OV DOMINANT DIRECTIONS
H0 component 0 s=0.958 output [(336, 0.194), (96, 0.158), (291, -0.15), (327, 0.149), (203, -0.14), (202, -0.128), (330, 0.12), (68, -0.117)] input [(373, 0.174), (225, -0.164), (134, -0.158), (158, -0.155), (7, 0.152), (51, 0.148), (96, -0.142), (173, -0.132)]
H0 component 1 s=0.898 output [(39, -0.262), (6, -0.192), (282, 0.186), (156, 0.148), (163, 0.136), (152, 0.133), (342, 0.128), (219, -0.127)] input [(226, -0.165), (301, 0.154), (21, -0.146), (358, -0.145), (220, -0.13), (325, -0.129), (348, -0.127), (61, 0.126)]
H1 component 0 s=3.380 output [(39, 0.692), (58, -0.51), (99, 0.358), (21, -0.212), (184, 0.201), (153, -0.054), (336, -0.038), (63, 0.037)] input [(184, 0.347), (180, 0.315), (99, 0.271), (39, 0.267), (56, -0.254), (317, -0.239), (69, 0.235), (256, -0.214)]
H1 component 1 s=2.023 output [(21, 0.961), (39, 0.158), (58, -0.129), (184, 0.072), (306, -0.032), (173, -0.029), (225, -0.028), (232, -0.026)] input [(211, 0.341), (223, -0.262), (228, -0.244), (65, 0.232), (173, -0.229), (124, -0.213), (60, -0.212), (26, 0.189)]
H2 component 0 s=2.643 output [(39, -0.659), (58, 0.494), (21, 0.373), (99, -0.355), (184, -0.162), (153, 0.045), (383, -0.035), (165, -0.029)] input [(184, -0.365), (180, -0.304), (99, -0.298), (317, 0.248), (56, 0.234), (39, -0.232), (69, -0.208), (301, -0.195)]
H2 component 1 s=1.363 output [(21, 0.91), (39, 0.221), (99, 0.207), (58, -0.201), (184, 0.053), (252, -0.03), (351, -0.03), (153, -0.029)] input [(211, 0.3), (173, -0.273), (223, -0.256), (162, 0.227), (256, -0.225), (65, 0.222), (89, -0.205), (228, -0.196)]
H3 component 0 s=1.127 output [(92, 0.178), (86, -0.169), (188, 0.163), (18, -0.16), (131, -0.158), (303, -0.156), (215, -0.148), (173, -0.139)] input [(223, 0.227), (26, 0.198), (200, 0.172), (48, 0.168), (359, -0.144), (124, -0.141), (178, 0.137), (354, -0.133)]
H3 component 1 s=0.955 output [(65, -0.191), (125, -0.18), (320, 0.174), (202, 0.168), (86, 0.158), (287, 0.152), (20, -0.142), (350, -0.136)] input [(200, 0.228), (124, 0.181), (53, -0.178), (329, 0.169), (74, -0.167), (105, -0.159), (154, -0.15), (363, 0.15)]
H4 component 0 s=1.454 output [(39, -0.696), (58, 0.511), (99, -0.32), (184, -0.227), (21, -0.182), (153, 0.057), (336, 0.042), (350, 0.039)] input [(184, -0.414), (180, -0.29), (256, 0.279), (56, 0.243), (100, 0.236), (69, -0.226), (317, 0.207), (162, -0.199)]
H4 component 1 s=0.724 output [(21, 0.941), (99, -0.241), (39, -0.097), (58, 0.092), (184, 0.042), (148, -0.033), (52, -0.032), (381, 0.031)] input [(211, 0.354), (228, -0.271), (65, 0.22), (124, -0.217), (26, 0.216), (319, 0.214), (223, -0.201), (59, 0.188)]
H5 component 0 s=16.282 output [(21, -0.864), (99, -0.358), (58, 0.237), (39, -0.232), (184, 0.024), (153, 0.019), (303, 0.019), (6, 0.017)] input [(162, -0.329), (211, -0.307), (223, 0.277), (65, -0.236), (354, 0.201), (173, 0.2), (132, -0.196), (300, -0.192)]
H5 component 1 s=8.786 output [(39, -0.608), (58, 0.486), (21, 0.469), (99, -0.404), (180, -0.035), (165, -0.026), (153, 0.021), (86, -0.015)] input [(99, -0.371), (180, -0.317), (184, -0.311), (162, -0.225), (100, 0.207), (240, -0.205), (317, 0.205), (39, -0.203)]

LATER AFFINE KEY DIRECTIONS VS L0 OV OUTPUT SUBSPACES
L1H0: fractions [0.001 0.82  0.833 0.    0.783 0.847] largest coordinates [(21, 3.627), (39, -0.474), (58, 0.358), (329, -0.211), (304, -0.198)]
L1H1: fractions [0.    0.822 0.836 0.006 0.767 0.848] largest coordinates [(21, 3.352), (58, 0.506), (39, -0.427), (57, 0.256), (187, 0.193)]
L1H2: fractions [0.    0.817 0.831 0.    0.766 0.849] largest coordinates [(21, 2.769), (58, 0.4), (39, -0.389), (215, 0.212), (369, -0.195)]
L1H3: fractions [0.    0.827 0.833 0.009 0.793 0.848] largest coordinates [(21, 3.079), (58, 0.459), (39, -0.423), (170, -0.29), (242, 0.209)]
L1H4: fractions [0.001 0.788 0.783 0.005 0.764 0.792] largest coordinates [(21, 2.008), (58, 0.505), (39, -0.333), (150, 0.179), (144, -0.164)]
L1H5: fractions [0.    0.837 0.839 0.001 0.796 0.849] largest coordinates [(21, 3.563), (39, -0.485), (58, 0.399), (351, -0.267), (210, -0.249)]
L2H0: fractions [0.    0.811 0.818 0.003 0.777 0.828] largest coordinates [(21, 1.815), (39, -0.266), (58, 0.248), (294, -0.155), (351, 0.141)]
L2H1: fractions [0.    0.815 0.799 0.    0.78  0.809] largest coordinates [(21, 1.682), (99, -0.208), (58, 0.201), (39, -0.199), (255, -0.119)]
L2H2: fractions [0.001 0.789 0.768 0.003 0.778 0.772] largest coordinates [(21, 1.411), (99, -0.238), (58, 0.156), (39, -0.151), (306, 0.099)]
L2H3: fractions [0.003 0.62  0.624 0.002 0.589 0.611] largest coordinates [(21, 0.803), (58, 0.158), (39, -0.133), (99, -0.117), (363, 0.096)]
L2H4: fractions [0.001 0.783 0.805 0.    0.745 0.826] largest coordinates [(21, 2.069), (58, 0.32), (39, -0.29), (361, -0.13), (2, 0.126)]
L2H5: fractions [0.001 0.83  0.836 0.002 0.786 0.847] largest coordinates [(21, 1.859), (58, 0.204), (39, -0.17), (40, 0.102), (214, 0.1)]
L3H0: fractions [0.004 0.708 0.692 0.014 0.687 0.685] largest coordinates [(21, 0.408), (39, -0.069), (99, -0.067), (307, 0.041), (58, 0.04)]
L3H1: fractions [0.041 0.508 0.493 0.007 0.482 0.474] largest coordinates [(21, 0.181), (39, -0.058), (184, -0.048), (99, -0.046), (37, 0.03)]
L3H2: fractions [0.002 0.649 0.623 0.008 0.639 0.615] largest coordinates [(21, 0.382), (99, -0.095), (39, -0.065), (25, -0.044), (136, -0.037)]
L3H3: fractions [0.013 0.009 0.01  0.005 0.009 0.008] largest coordinates [(193, -0.036), (157, 0.031), (198, -0.029), (339, -0.029), (129, -0.029)]
L3H4: fractions [0.112 0.037 0.043 0.001 0.032 0.037] largest coordinates [(163, -0.031), (21, 0.028), (120, 0.023), (355, -0.022), (269, -0.021)]
L3H5: fractions [0.001 0.737 0.734 0.006 0.691 0.721] largest coordinates [(21, 0.416), (39, -0.064), (184, -0.059), (22, 0.046), (43, 0.043)]

MLP0 ROW GEOMETRY
current weight projection singular values [11.423  8.653  7.882  7.526  7.341  7.074  6.711  6.614  6.451  5.991  5.713  5.275  5.123  4.988  4.568  4.471  4.337  4.154  4.05   3.687  2.842  2.48   1.813  1.73   1.422
  1.334  1.069]
row norm percentiles [0.058 0.102 0.122 0.185 0.905 1.334 1.93  2.293]
previous-head path projection singular values [9.585 6.439 6.141 5.846 5.572 5.342 5.241 5.049 4.705 4.621 4.247 3.991 3.704 3.634 3.478 2.674 2.396 1.701 1.277 0.899 0.859 0.484 0.373 0.35  0.297 0.27  0.252]
row norm percentiles [0.035 0.052 0.063 0.093 0.649 1.022 1.463 2.334]
current/previous weight cross spectrum [62.36  22.22  17.069 16.491 14.485 12.829 11.603  9.646  8.392  6.824  6.069  5.712  4.763  4.273  3.802  3.06   2.612  2.219  2.058  1.591  0.764  0.526  0.267  0.212  0.148
  0.055  0.002]
cross symmetric eigenvalues [-12.298 -11.004  -8.306  -7.578  -6.48   -4.526  -2.956  -2.131  -1.505  -0.943  -0.71   -0.46   -0.065   0.379   0.46    1.018   1.305   1.424   2.432   3.238   4.683   5.521
   6.414   9.651  10.927  15.3    52.67 ]
cross trace/F 0.7401727646232719


========================================================================
TURN t005
MESSAGE SENT TO CODEX
OUTPUT of run 4 of at most 100:
```
L0H3 VALUE/OUTPUT SPECTRA: possible alphabet-size evidence, not character labels
V [2.102 1.948 1.903 1.866 1.843 1.819 1.769 1.755 1.674 1.651 1.629 1.621 1.589 1.553 1.52  1.465 1.417 1.303 1.16  1.101 0.945 0.93  0.848 0.818 0.79  0.779 0.745 0.739 0.73
 0.701 0.688 0.658 0.642 0.622 0.618 0.605 0.575 0.565 0.543 0.522 0.515 0.491 0.481 0.466 0.446 0.43  0.41  0.4   0.378 0.366 0.359 0.349 0.345 0.342 0.336 0.329 0.318 0.313
 0.313 0.298 0.287 0.279 0.271 0.262]
O [2.133 2.11  2.076 1.975 1.963 1.937 1.913 1.891 1.878 1.845 1.823 1.813 1.78  1.737 1.713 1.678 1.632 1.457 1.395 1.326 1.286 1.272 1.214 1.201 1.19  1.131 1.101 1.078 1.065
 1.011 0.994 0.956 0.935 0.916 0.864 0.817 0.756 0.74  0.713 0.688 0.634 0.562 0.535 0.469 0.423 0.373 0.357 0.349 0.321 0.297 0.28  0.267 0.259 0.249 0.235 0.228 0.217 0.216
 0.198 0.187 0.184 0.172 0.145 0.127]
V times ln1 gain [0.57  0.513 0.504 0.472 0.47  0.451 0.44  0.434 0.425 0.412 0.39  0.383 0.38  0.367 0.36  0.344 0.339 0.337 0.324 0.298 0.286 0.276 0.273 0.266 0.255 0.249 0.245 0.241 0.237
 0.232 0.225 0.223 0.22  0.21  0.206 0.204 0.197 0.194 0.183 0.18  0.173 0.169 0.16  0.151 0.15  0.138 0.134 0.132 0.124 0.121 0.116 0.11  0.108 0.107 0.103 0.101 0.097 0.093
 0.09  0.087 0.085 0.081 0.078 0.072]
OV [1.127 0.955 0.95  0.89  0.864 0.854 0.844 0.78  0.759 0.723 0.709 0.674 0.67  0.652 0.634 0.617 0.565 0.528 0.438 0.382 0.35  0.317 0.297 0.295 0.282 0.276 0.265 0.246 0.24
 0.233 0.225 0.213 0.209 0.197 0.189 0.172 0.159 0.149 0.137 0.133 0.111 0.098 0.083 0.07  0.061 0.052 0.047 0.046 0.039 0.034 0.032 0.031 0.027 0.026 0.025 0.023 0.022 0.021
 0.021 0.018 0.018 0.015 0.013 0.011]

EARLY OV DOMINANT DIRECTIONS
H0 component 0 s=0.958 output [(336, 0.194), (96, 0.158), (291, -0.15), (327, 0.149), (203, -0.14), (202, -0.128), (330, 0.12), (68, -0.117)] input [(373, 0.174), (225, -0.164), (134, -0.158), (158, -0.155), (7, 0.152), (51, 0.148), (96, -0.142), (173, -0.132)]
H0 component 1 s=0.898 output [(39, -0.262), (6, -0.192), (282, 0.186), (156, 0.148), (163, 0.136), (152, 0.133), (342, 0.128), (219, -0.127)] input [(226, -0.165), (301, 0.154), (21, -0.146), (358, -0.145), (220, -0.13), (325, -0.129), (348, -0.127), (61, 0.126)]
H1 component 0 s=3.380 output [(39, 0.692), (58, -0.51), (99, 0.358), (21, -0.212), (184, 0.201), (153, -0.054), (336, -0.038), (63, 0.037)] input [(184, 0.347), (180, 0.315), (99, 0.271), (39, 0.267), (56, -0.254), (317, -0.239), (69, 0.235), (256, -0.214)]
H1 component 1 s=2.023 output [(21, 0.961), (39, 0.158), (58, -0.129), (184, 0.072), (306, -0.032), (173, -0.029), (225, -0.028), (232, -0.026)] input [(211, 0.341), (223, -0.262), (228, -0.244), (65, 0.232), (173, -0.229), (124, -0.213), (60, -0.212), (26, 0.189)]
H2 component 0 s=2.643 output [(39, -0.659), (58, 0.494), (21, 0.373), (99, -0.355), (184, -0.162), (153, 0.045), (383, -0.035), (165, -0.029)] input [(184, -0.365), (180, -0.304), (99, -0.298), (317, 0.248), (56, 0.234), (39, -0.232), (69, -0.208), (301, -0.195)]
H2 component 1 s=1.363 output [(21, 0.91), (39, 0.221), (99, 0.207), (58, -0.201), (184, 0.053), (252, -0.03), (351, -0.03), (153, -0.029)] input [(211, 0.3), (173, -0.273), (223, -0.256), (162, 0.227), (256, -0.225), (65, 0.222), (89, -0.205), (228, -0.196)]
H3 component 0 s=1.127 output [(92, 0.178), (86, -0.169), (188, 0.163), (18, -0.16), (131, -0.158), (303, -0.156), (215, -0.148), (173, -0.139)] input [(223, 0.227), (26, 0.198), (200, 0.172), (48, 0.168), (359, -0.144), (124, -0.141), (178, 0.137), (354, -0.133)]
H3 component 1 s=0.955 output [(65, -0.191), (125, -0.18), (320, 0.174), (202, 0.168), (86, 0.158), (287, 0.152), (20, -0.142), (350, -0.136)] input [(200, 0.228), (124, 0.181), (53, -0.178), (329, 0.169), (74, -0.167), (105, -0.159), (154, -0.15), (363, 0.15)]
H4 component 0 s=1.454 output [(39, -0.696), (58, 0.511), (99, -0.32), (184, -0.227), (21, -0.182), (153, 0.057), (336, 0.042), (350, 0.039)] input [(184, -0.414), (180, -0.29), (256, 0.279), (56, 0.243), (100, 0.236), (69, -0.226), (317, 0.207), (162, -0.199)]
H4 component 1 s=0.724 output [(21, 0.941), (99, -0.241), (39, -0.097), (58, 0.092), (184, 0.042), (148, -0.033), (52, -0.032), (381, 0.031)] input [(211, 0.354), (228, -0.271), (65, 0.22), (124, -0.217), (26, 0.216), (319, 0.214), (223, -0.201), (59, 0.188)]
H5 component 0 s=16.282 output [(21, -0.864), (99, -0.358), (58, 0.237), (39, -0.232), (184, 0.024), (153, 0.019), (303, 0.019), (6, 0.017)] input [(162, -0.329), (211, -0.307), (223, 0.277), (65, -0.236), (354, 0.201), (173, 0.2), (132, -0.196), (300, -0.192)]
H5 component 1 s=8.786 output [(39, -0.608), (58, 0.486), (21, 0.469), (99, -0.404), (180, -0.035), (165, -0.026), (153, 0.021), (86, -0.015)] input [(99, -0.371), (180, -0.317), (184, -0.311), (162, -0.225), (100, 0.207), (240, -0.205), (317, 0.205), (39, -0.203)]

LATER AFFINE KEY DIRECTIONS VS L0 OV OUTPUT SUBSPACES
L1H0: fractions [0.001 0.82  0.833 0.    0.783 0.847] largest coordinates [(21, 3.627), (39, -0.474), (58, 0.358), (329, -0.211), (304, -0.198)]
L1H1: fractions [0.    0.822 0.836 0.006 0.767 0.848] largest coordinates [(21, 3.352), (58, 0.506), (39, -0.427), (57, 0.256), (187, 0.193)]
L1H2: fractions [0.    0.817 0.831 0.    0.766 0.849] largest coordinates [(21, 2.769), (58, 0.4), (39, -0.389), (215, 0.212), (369, -0.195)]
L1H3: fractions [0.    0.827 0.833 0.009 0.793 0.848] largest coordinates [(21, 3.079), (58, 0.459), (39, -0.423), (170, -0.29), (242, 0.209)]
L1H4: fractions [0.001 0.788 0.783 0.005 0.764 0.792] largest coordinates [(21, 2.008), (58, 0.505), (39, -0.333), (150, 0.179), (144, -0.164)]
L1H5: fractions [0.    0.837 0.839 0.001 0.796 0.849] largest coordinates [(21, 3.563), (39, -0.485), (58, 0.399), (351, -0.267), (210, -0.249)]
L2H0: fractions [0.    0.811 0.818 0.003 0.777 0.828] largest coordinates [(21, 1.815), (39, -0.266), (58, 0.248), (294, -0.155), (351, 0.141)]
L2H1: fractions [0.    0.815 0.799 0.    0.78  0.809] largest coordinates [(21, 1.682), (99, -0.208), (58, 0.201), (39, -0.199), (255, -0.119)]
L2H2: fractions [0.001 0.789 0.768 0.003 0.778 0.772] largest coordinates [(21, 1.411), (99, -0.238), (58, 0.156), (39, -0.151), (306, 0.099)]
L2H3: fractions [0.003 0.62  0.624 0.002 0.589 0.611] largest coordinates [(21, 0.803), (58, 0.158), (39, -0.133), (99, -0.117), (363, 0.096)]
L2H4: fractions [0.001 0.783 0.805 0.    0.745 0.826] largest coordinates [(21, 2.069), (58, 0.32), (39, -0.29), (361, -0.13), (2, 0.126)]
L2H5: fractions [0.001 0.83  0.836 0.002 0.786 0.847] largest coordinates [(21, 1.859), (58, 0.204), (39, -0.17), (40, 0.102), (214, 0.1)]
L3H0: fractions [0.004 0.708 0.692 0.014 0.687 0.685] largest coordinates [(21, 0.408), (39, -0.069), (99, -0.067), (307, 0.041), (58, 0.04)]
L3H1: fractions [0.041 0.508 0.493 0.007 0.482 0.474] largest coordinates [(21, 0.181), (39, -0.058), (184, -0.048), (99, -0.046), (37, 0.03)]
L3H2: fractions [0.002 0.649 0.623 0.008 0.639 0.615] largest coordinates [(21, 0.382), (99, -0.095), (39, -0.065), (25, -0.044), (136, -0.037)]
L3H3: fractions [0.013 0.009 0.01  0.005 0.009 0.008] largest coordinates [(193, -0.036), (157, 0.031), (198, -0.029), (339, -0.029), (129, -0.029)]
L3H4: fractions [0.112 0.037 0.043 0.001 0.032 0.037] largest coordinates [(163, -0.031), (21, 0.028), (120, 0.023), (355, -0.022), (269, -0.021)]
L3H5: fractions [0.001 0.737 0.734 0.006 0.691 0.721] largest coordinates [(21, 0.416), (39, -0.064), (184, -0.059), (22, 0.046), (43, 0.043)]

MLP0 ROW GEOMETRY
current weight projection singular values [11.423  8.653  7.882  7.526  7.341  7.074  6.711  6.614  6.451  5.991  5.713  5.275  5.123  4.988  4.568  4.471  4.337  4.154  4.05   3.687  2.842  2.48   1.813  1.73   1.422
  1.334  1.069]
row norm percentiles [0.058 0.102 0.122 0.185 0.905 1.334 1.93  2.293]
previous-head path projection singular values [9.585 6.439 6.141 5.846 5.572 5.342 5.241 5.049 4.705 4.621 4.247 3.991 3.704 3.634 3.478 2.674 2.396 1.701 1.277 0.899 0.859 0.484 0.373 0.35  0.297 0.27  0.252]
row norm percentiles [0.035 0.052 0.063 0.093 0.649 1.022 1.463 2.334]
current/previous weight cross spectrum [62.36  22.22  17.069 16.491 14.485 12.829 11.603  9.646  8.392  6.824  6.069  5.712  4.763  4.273  3.802  3.06   2.612  2.219  2.058  1.591  0.764  0.526  0.267  0.212  0.148
  0.055  0.002]
cross symmetric eigenvalues [-12.298 -11.004  -8.306  -7.578  -6.48   -4.526  -2.956  -2.131  -1.505  -0.943  -0.71   -0.46   -0.065   0.379   0.46    1.018   1.305   1.424   2.432   3.238   4.683   5.521
   6.414   9.651  10.927  15.3    52.67 ]
cross trace/F 0.7401727646232719

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

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

AGENT_MESSAGE
```python
import numpy as np
from scipy.linalg import eigh
from scipy.optimize import linear_sum_assignment
from sklearn.cluster import KMeans
from threadpoolctl import threadpool_limits
threadpool_limits(2)
np.set_printoptions(precision=3, suppress=True, linewidth=180)

g1=W["ln1_w.0"].astype(float)
g2=W["ln2_w.0"].astype(float)
V=W["W_v.0"][192:256].astype(float)*g1
B=W["W_o.0"][:,192:256].astype(float)@V
F=W["W_fc.0"].astype(float)*g2
_,_,vt=np.linalg.svd(V,full_matrices=False)

print("MLP ROW-DIRECTION ASSOCIATION, NOT NEURON ACTIVATIONS")
for rank in [18,22,24,27,32]:
    T=vt[:rank].T
    X=F@T
    Y=F@B@T
    mask=(np.linalg.norm(X,axis=1)>.4)&(np.linalg.norm(Y,axis=1)>.3)
    X=X[mask];Y=Y[mask]
    # Remove the average learned row direction.
    X=X-X.mean(0)
    Y=Y-Y.mean(0)
    # Equalize the aggregate scales, without passing an input through the model.
    Y=Y*np.linalg.norm(X)/np.linalg.norm(Y)
    G=(X.T@X+Y.T@Y)/2
    C=(X.T@Y+Y.T@X)/2
    ev=eigh(C,G+np.eye(rank)*np.trace(G)/rank*.03,eigvals_only=True)
    print("rank",rank,"rows",mask.sum(),"association eigenvalues",ev)

print("\nINDEPENDENT CLUSTERINGS OF CURRENT/OV-PATH ROW DIRECTIONS")
T=vt[:27].T
X=F@T
Y=F@B@T
mask=(np.linalg.norm(X,axis=1)>.4)&(np.linalg.norm(Y,axis=1)>.3)
X=X[mask];Y=Y[mask]
X-=X.mean(0);Y-=Y.mean(0)
Y*=np.linalg.norm(X)/np.linalg.norm(Y)
G=(X.T@X+Y.T@Y)/2
ev,U=np.linalg.eigh(G)
wh=U@np.diag(1/np.sqrt(np.maximum(ev,.08*ev.max())))@U.T
X=X@wh;Y=Y@wh
X/=np.linalg.norm(X,axis=1,keepdims=True)
Y/=np.linalg.norm(Y,axis=1,keepdims=True)
for nc in [18,22,24,28]:
    kx=KMeans(n_clusters=nc,n_init=20,random_state=0).fit(X)
    ky=KMeans(n_clusters=nc,n_init=20,random_state=0).fit(Y)
    cx=kx.cluster_centers_;cy=ky.cluster_centers_
    cx=cx/np.linalg.norm(cx,axis=1,keepdims=True)
    cy=cy/np.linalg.norm(cy,axis=1,keepdims=True)
    r,c=linear_sum_assignment(-(cx@cy.T))
    remap=np.zeros(nc,dtype=int);remap[c]=r
    tab=np.zeros((nc,nc))
    np.add.at(tab,(kx.labels_,remap[ky.labels_]),1)
    S=(tab+tab.T)/2
    deg=S.sum(0)
    norm=S/np.sqrt(np.maximum(deg[:,None]*deg[None,:],1))
    spec=np.linalg.eigvalsh(norm)
    print("clusters",nc,
          "matched center cos quantiles",np.percentile((cx@cy.T)[r,c],[0,25,50,75,100]),
          "graph eigenvalues",spec)
    print("cluster counts",np.sort(np.bincount(kx.labels_,minlength=nc)))
    if nc==24:
        print("24-cluster association table (unlabelled weight directions):")
        print(tab.astype(int))

print("\nL0H3 RAW QK COMPLEX MODES")
q=W["W_q.0"][192:256].astype(float)
k=W["W_k.0"][192:256].astype(float)
ev=np.linalg.eigvals(k@q.T/8)
sel=ev[(ev.imag>1e-6)&(abs(ev)>.10)]
sel=sel[np.argsort(np.angle(sel))]
print("angle, magnitude:",np.column_stack([np.angle(sel),abs(sel)]))
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 5,
  "turn": "t005",
  "code": "import numpy as np\nfrom scipy.linalg import eigh\nfrom scipy.optimize import linear_sum_assignment\nfrom sklearn.cluster import KMeans\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=3, suppress=True, linewidth=180)\n\ng1=W[\"ln1_w.0\"].astype(float)\ng2=W[\"ln2_w.0\"].astype(float)\nV=W[\"W_v.0\"][192:256].astype(float)*g1\nB=W[\"W_o.0\"][:,192:256].astype(float)@V\nF=W[\"W_fc.0\"].astype(float)*g2\n_,_,vt=np.linalg.svd(V,full_matrices=False)\n\nprint(\"MLP ROW-DIRECTION ASSOCIATION, NOT NEURON ACTIVATIONS\")\nfor rank in [18,22,24,27,32]:\n    T=vt[:rank].T\n    X=F@T\n    Y=F@B@T\n    mask=(np.linalg.norm(X,axis=1)>.4)&(np.linalg.norm(Y,axis=1)>.3)\n    X=X[mask];Y=Y[mask]\n    # Remove the average learned row direction.\n    X=X-X.mean(0)\n    Y=Y-Y.mean(0)\n    # Equalize the aggregate scales, without passing an input through the model.\n    Y=Y*np.linalg.norm(X)/np.linalg.norm(Y)\n    G=(X.T@X+Y.T@Y)/2\n    C=(X.T@Y+Y.T@X)/2\n    ev=eigh(C,G+np.eye(rank)*np.trace(G)/rank*.03,eigvals_only=True)\n    print(\"rank\",rank,\"rows\",mask.sum(),\"association eigenvalues\",ev)\n\nprint(\"\\nINDEPENDENT CLUSTERINGS OF CURRENT/OV-PATH ROW DIRECTIONS\")\nT=vt[:27].T\nX=F@T\nY=F@B@T\nmask=(np.linalg.norm(X,axis=1)>.4)&(np.linalg.norm(Y,axis=1)>.3)\nX=X[mask];Y=Y[mask]\nX-=X.mean(0);Y-=Y.mean(0)\nY*=np.linalg.norm(X)/np.linalg.norm(Y)\nG=(X.T@X+Y.T@Y)/2\nev,U=np.linalg.eigh(G)\nwh=U@np.diag(1/np.sqrt(np.maximum(ev,.08*ev.max())))@U.T\nX=X@wh;Y=Y@wh\nX/=np.linalg.norm(X,axis=1,keepdims=True)\nY/=np.linalg.norm(Y,axis=1,keepdims=True)\nfor nc in [18,22,24,28]:\n    kx=KMeans(n_clusters=nc,n_init=20,random_state=0).fit(X)\n    ky=KMeans(n_clusters=nc,n_init=20,random_state=0).fit(Y)\n    cx=kx.cluster_centers_;cy=ky.cluster_centers_\n    cx=cx/np.linalg.norm(cx,axis=1,keepdims=True)\n    cy=cy/np.linalg.norm(cy,axis=1,keepdims=True)\n    r,c=linear_sum_assignment(-(cx@cy.T))\n    remap=np.zeros(nc,dtype=int);remap[c]=r\n    tab=np.zeros((nc,nc))\n    np.add.at(tab,(kx.labels_,remap[ky.labels_]),1)\n    S=(tab+tab.T)/2\n    deg=S.sum(0)\n    norm=S/np.sqrt(np.maximum(deg[:,None]*deg[None,:],1))\n    spec=np.linalg.eigvalsh(norm)\n    print(\"clusters\",nc,\n          \"matched center cos quantiles\",np.percentile((cx@cy.T)[r,c],[0,25,50,75,100]),\n          \"graph eigenvalues\",spec)\n    print(\"cluster counts\",np.sort(np.bincount(kx.labels_,minlength=nc)))\n    if nc==24:\n        print(\"24-cluster association table (unlabelled weight directions):\")\n        print(tab.astype(int))\n\nprint(\"\\nL0H3 RAW QK COMPLEX MODES\")\nq=W[\"W_q.0\"][192:256].astype(float)\nk=W[\"W_k.0\"][192:256].astype(float)\nev=np.linalg.eigvals(k@q.T/8)\nsel=ev[(ev.imag>1e-6)&(abs(ev)>.10)]\nsel=sel[np.argsort(np.angle(sel))]\nprint(\"angle, magnitude:\",np.column_stack([np.angle(sel),abs(sel)]))",
  "out": "MLP ROW-DIRECTION ASSOCIATION, NOT NEURON ACTIVATIONS\nrank 18 rows 516 association eigenvalues [-0.38  -0.331 -0.258 -0.228 -0.15  -0.123 -0.102 -0.049  0.021  0.048  0.129  0.172  0.249  0.29   0.323  0.352  0.509  0.586]\nrank 22 rows 523 association eigenvalues [-0.474 -0.409 -0.356 -0.279 -0.238 -0.174 -0.133 -0.075 -0.023 -0.009  0.025  0.059  0.084  0.148  0.173  0.313  0.327  0.359  0.406  0.44   0.525  0.62 ]\nrank 24 rows 524 association eigenvalues [-0.593 -0.481 -0.428 -0.365 -0.318 -0.204 -0.16  -0.105 -0.069 -0.02   0.002  0.037  0.045  0.145  0.163  0.262  0.299  0.317  0.351  0.365  0.458  0.476  0.529  0.67 ]\nrank 27 rows 525 association eigenvalues [-0.613 -0.56  -0.529 -0.449 -0.354 -0.314 -0.238 -0.218 -0.158 -0.123 -0.075 -0.056  0.013  0.059  0.083  0.154  0.204  0.244  0.28   0.321  0.355  0.386  0.431  0.49   0.511\n  0.562  0.688]\nrank 32 rows 527 association eigenvalues [-0.727 -0.662 -0.607 -0.567 -0.515 -0.452 -0.424 -0.368 -0.279 -0.234 -0.198 -0.18  -0.11  -0.091  0.025  0.055  0.081  0.126  0.157  0.205  0.224  0.278  0.316  0.349  0.423\n  0.439  0.472  0.52   0.553  0.585  0.656  0.738]\n\nINDEPENDENT CLUSTERINGS OF CURRENT/OV-PATH ROW DIRECTIONS\nclusters 18 matched center cos quantiles [0.061 0.233 0.436 0.673 0.754] graph eigenvalues [-0.208 -0.167 -0.086 -0.066 -0.008 -0.006  0.029  0.075  0.093  0.119  0.159  0.18   0.206  0.232  0.275  0.277  0.353  1.   ]\ncluster counts [18 21 22 22 24 25 26 26 26 27 32 34 34 34 35 37 40 42]\nclusters 22 matched center cos quantiles [0.068 0.273 0.495 0.632 0.83 ] graph eigenvalues [-0.301 -0.157 -0.135 -0.098 -0.085 -0.024  0.002  0.052  0.068  0.086  0.096  0.128  0.17   0.199  0.223  0.235  0.273  0.287  0.319  0.392  0.422  1.   ]\ncluster counts [14 14 16 16 20 21 21 21 22 22 22 22 23 24 26 27 27 29 30 31 34 43]\nclusters 24 matched center cos quantiles [0.027 0.281 0.562 0.628 0.815] graph eigenvalues [-0.274 -0.232 -0.178 -0.153 -0.129 -0.086 -0.07  -0.021 -0.001  0.026  0.043  0.096  0.11   0.121  0.156  0.193  0.206  0.257  0.274  0.285  0.351  0.384  0.413  1.   ]\ncluster counts [10 11 12 12 14 15 16 18 19 20 21 21 23 24 25 25 25 26 27 27 30 30 36 38]\n24-cluster association table (unlabelled weight directions):\n[[1 1 0 0 0 0 0 0 0 1 1 1 0 0 3 0 0 1 0 0 0 1 0 0]\n [0 4 2 0 2 0 0 1 5 0 0 2 1 1 2 2 0 1 0 2 1 1 0 0]\n [0 2 1 0 0 1 3 1 1 0 0 1 4 3 0 0 3 1 2 3 1 0 1 2]\n [1 0 0 2 3 0 1 0 1 1 2 2 1 0 3 2 0 1 1 1 1 1 2 0]\n [9 1 1 1 0 1 0 1 0 1 2 1 0 0 0 0 0 1 2 0 1 0 1 2]\n [0 1 0 0 0 4 0 4 0 0 2 0 1 0 0 1 0 1 0 0 0 0 3 1]\n [0 1 0 0 0 0 4 0 0 0 1 0 0 0 1 0 0 0 2 2 0 0 0 0]\n [0 3 0 0 3 1 1 6 4 1 1 5 1 0 0 0 3 4 1 0 2 0 0 0]\n [0 1 1 2 0 0 0 0 5 1 3 2 0 0 1 0 3 0 0 1 1 0 0 0]\n [1 0 2 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 1]\n [3 1 1 0 0 0 2 0 1 0 1 3 0 2 0 1 1 4 0 1 1 4 1 0]\n [0 1 0 2 1 1 0 0 0 1 1 1 0 1 1 2 2 1 2 0 2 1 2 1]\n [1 1 1 3 1 2 1 1 1 0 1 1 2 2 1 4 3 1 0 3 1 4 1 2]\n [0 1 0 2 3 0 0 1 0 0 0 0 2 2 0 0 2 2 0 0 1 2 1 0]\n [1 0 2 0 3 0 1 1 0 0 2 0 1 1 5 1 0 0 2 1 1 1 1 0]\n [0 2 3 2 2 0 0 1 1 0 0 4 2 6 0 1 1 1 1 0 0 1 2 0]\n [0 0 0 0 0 1 0 2 1 6 0 0 0 1 1 1 1 0 1 0 0 0 0 0]\n [0 3 1 1 1 2 0 1 0 2 1 1 0 0 0 2 3 2 1 2 0 0 1 1]\n [1 0 0 1 0 0 0 0 0 0 1 1 3 0 0 1 1 0 3 2 1 0 1 0]\n [0 1 2 1 1 2 1 2 1 1 1 1 2 2 0 1 0 0 3 0 0 1 0 2]\n [1 3 0 1 3 1 1 1 1 2 0 0 1 1 0 0 2 0 0 1 0 0 0 1]\n [0 0 0 1 0 0 4 0 1 3 0 0 2 3 0 0 1 1 1 0 1 3 0 0]\n [0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 6 2]\n [0 0 1 2 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 4]]\nclusters 28 matched center cos quantiles [0.09  0.296 0.505 0.691 0.841] graph eigenvalues [-0.257 -0.188 -0.134 -0.114 -0.085 -0.076 -0.058 -0.017  0.006  0.031  0.052  0.065  0.106  0.125  0.165  0.176  0.212  0.248  0.255  0.267  0.283  0.308  0.35   0.37   0.398\n  0.428  0.534  1.   ]\ncluster counts [ 7  9 12 12 12 13 14 14 15 15 16 17 17 17 19 20 21 21 21 22 22 22 23 26 27 27 27 37]\n\nL0H3 RAW QK COMPLEX MODES\nangle, magnitude: [[0.6   0.819]\n [0.658 0.616]\n [0.679 0.45 ]\n [0.762 0.888]\n [0.763 1.001]\n [0.901 0.921]\n [0.917 0.761]\n [1.083 0.953]\n [1.428 0.77 ]\n [1.451 0.338]\n [1.573 0.607]\n [2.056 0.287]\n [2.196 0.436]\n [2.912 0.127]\n [2.913 0.155]]\n",
  "seconds": 1.613,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "33226469aa86ca13638733b1cee3d980ebabb108fafd7ed72779fc08c4bb3e1e",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

EXACT HARNESS FEEDBACK
MLP ROW-DIRECTION ASSOCIATION, NOT NEURON ACTIVATIONS
rank 18 rows 516 association eigenvalues [-0.38  -0.331 -0.258 -0.228 -0.15  -0.123 -0.102 -0.049  0.021  0.048  0.129  0.172  0.249  0.29   0.323  0.352  0.509  0.586]
rank 22 rows 523 association eigenvalues [-0.474 -0.409 -0.356 -0.279 -0.238 -0.174 -0.133 -0.075 -0.023 -0.009  0.025  0.059  0.084  0.148  0.173  0.313  0.327  0.359  0.406  0.44   0.525  0.62 ]
rank 24 rows 524 association eigenvalues [-0.593 -0.481 -0.428 -0.365 -0.318 -0.204 -0.16  -0.105 -0.069 -0.02   0.002  0.037  0.045  0.145  0.163  0.262  0.299  0.317  0.351  0.365  0.458  0.476  0.529  0.67 ]
rank 27 rows 525 association eigenvalues [-0.613 -0.56  -0.529 -0.449 -0.354 -0.314 -0.238 -0.218 -0.158 -0.123 -0.075 -0.056  0.013  0.059  0.083  0.154  0.204  0.244  0.28   0.321  0.355  0.386  0.431  0.49   0.511
  0.562  0.688]
rank 32 rows 527 association eigenvalues [-0.727 -0.662 -0.607 -0.567 -0.515 -0.452 -0.424 -0.368 -0.279 -0.234 -0.198 -0.18  -0.11  -0.091  0.025  0.055  0.081  0.126  0.157  0.205  0.224  0.278  0.316  0.349  0.423
  0.439  0.472  0.52   0.553  0.585  0.656  0.738]

INDEPENDENT CLUSTERINGS OF CURRENT/OV-PATH ROW DIRECTIONS
clusters 18 matched center cos quantiles [0.061 0.233 0.436 0.673 0.754] graph eigenvalues [-0.208 -0.167 -0.086 -0.066 -0.008 -0.006  0.029  0.075  0.093  0.119  0.159  0.18   0.206  0.232  0.275  0.277  0.353  1.   ]
cluster counts [18 21 22 22 24 25 26 26 26 27 32 34 34 34 35 37 40 42]
clusters 22 matched center cos quantiles [0.068 0.273 0.495 0.632 0.83 ] graph eigenvalues [-0.301 -0.157 -0.135 -0.098 -0.085 -0.024  0.002  0.052  0.068  0.086  0.096  0.128  0.17   0.199  0.223  0.235  0.273  0.287  0.319  0.392  0.422  1.   ]
cluster counts [14 14 16 16 20 21 21 21 22 22 22 22 23 24 26 27 27 29 30 31 34 43]
clusters 24 matched center cos quantiles [0.027 0.281 0.562 0.628 0.815] graph eigenvalues [-0.274 -0.232 -0.178 -0.153 -0.129 -0.086 -0.07  -0.021 -0.001  0.026  0.043  0.096  0.11   0.121  0.156  0.193  0.206  0.257  0.274  0.285  0.351  0.384  0.413  1.   ]
cluster counts [10 11 12 12 14 15 16 18 19 20 21 21 23 24 25 25 25 26 27 27 30 30 36 38]
24-cluster association table (unlabelled weight directions):
[[1 1 0 0 0 0 0 0 0 1 1 1 0 0 3 0 0 1 0 0 0 1 0 0]
 [0 4 2 0 2 0 0 1 5 0 0 2 1 1 2 2 0 1 0 2 1 1 0 0]
 [0 2 1 0 0 1 3 1 1 0 0 1 4 3 0 0 3 1 2 3 1 0 1 2]
 [1 0 0 2 3 0 1 0 1 1 2 2 1 0 3 2 0 1 1 1 1 1 2 0]
 [9 1 1 1 0 1 0 1 0 1 2 1 0 0 0 0 0 1 2 0 1 0 1 2]
 [0 1 0 0 0 4 0 4 0 0 2 0 1 0 0 1 0 1 0 0 0 0 3 1]
 [0 1 0 0 0 0 4 0 0 0 1 0 0 0 1 0 0 0 2 2 0 0 0 0]
 [0 3 0 0 3 1 1 6 4 1 1 5 1 0 0 0 3 4 1 0 2 0 0 0]
 [0 1 1 2 0 0 0 0 5 1 3 2 0 0 1 0 3 0 0 1 1 0 0 0]
 [1 0 2 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 1]
 [3 1 1 0 0 0 2 0 1 0 1 3 0 2 0 1 1 4 0 1 1 4 1 0]
 [0 1 0 2 1 1 0 0 0 1 1 1 0 1 1 2 2 1 2 0 2 1 2 1]
 [1 1 1 3 1 2 1 1 1 0 1 1 2 2 1 4 3 1 0 3 1 4 1 2]
 [0 1 0 2 3 0 0 1 0 0 0 0 2 2 0 0 2 2 0 0 1 2 1 0]
 [1 0 2 0 3 0 1 1 0 0 2 0 1 1 5 1 0 0 2 1 1 1 1 0]
 [0 2 3 2 2 0 0 1 1 0 0 4 2 6 0 1 1 1 1 0 0 1 2 0]
 [0 0 0 0 0 1 0 2 1 6 0 0 0 1 1 1 1 0 1 0 0 0 0 0]
 [0 3 1 1 1 2 0 1 0 2 1 1 0 0 0 2 3 2 1 2 0 0 1 1]
 [1 0 0 1 0 0 0 0 0 0 1 1 3 0 0 1 1 0 3 2 1 0 1 0]
 [0 1 2 1 1 2 1 2 1 1 1 1 2 2 0 1 0 0 3 0 0 1 0 2]
 [1 3 0 1 3 1 1 1 1 2 0 0 1 1 0 0 2 0 0 1 0 0 0 1]
 [0 0 0 1 0 0 4 0 1 3 0 0 2 3 0 0 1 1 1 0 1 3 0 0]
 [0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 6 2]
 [0 0 1 2 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 4]]
clusters 28 matched center cos quantiles [0.09  0.296 0.505 0.691 0.841] graph eigenvalues [-0.257 -0.188 -0.134 -0.114 -0.085 -0.076 -0.058 -0.017  0.006  0.031  0.052  0.065  0.106  0.125  0.165  0.176  0.212  0.248  0.255  0.267  0.283  0.308  0.35   0.37   0.398
  0.428  0.534  1.   ]
cluster counts [ 7  9 12 12 12 13 14 14 15 15 16 17 17 17 19 20 21 21 21 22 22 22 23 26 27 27 27 37]

L0H3 RAW QK COMPLEX MODES
angle, magnitude: [[0.6   0.819]
 [0.658 0.616]
 [0.679 0.45 ]
 [0.762 0.888]
 [0.763 1.001]
 [0.901 0.921]
 [0.917 0.761]
 [1.083 0.953]
 [1.428 0.77 ]
 [1.451 0.338]
 [1.573 0.607]
 [2.056 0.287]
 [2.196 0.436]
 [2.912 0.127]
 [2.913 0.155]]


========================================================================
TURN t006
MESSAGE SENT TO CODEX
OUTPUT of run 5 of at most 100:
```
MLP ROW-DIRECTION ASSOCIATION, NOT NEURON ACTIVATIONS
rank 18 rows 516 association eigenvalues [-0.38  -0.331 -0.258 -0.228 -0.15  -0.123 -0.102 -0.049  0.021  0.048  0.129  0.172  0.249  0.29   0.323  0.352  0.509  0.586]
rank 22 rows 523 association eigenvalues [-0.474 -0.409 -0.356 -0.279 -0.238 -0.174 -0.133 -0.075 -0.023 -0.009  0.025  0.059  0.084  0.148  0.173  0.313  0.327  0.359  0.406  0.44   0.525  0.62 ]
rank 24 rows 524 association eigenvalues [-0.593 -0.481 -0.428 -0.365 -0.318 -0.204 -0.16  -0.105 -0.069 -0.02   0.002  0.037  0.045  0.145  0.163  0.262  0.299  0.317  0.351  0.365  0.458  0.476  0.529  0.67 ]
rank 27 rows 525 association eigenvalues [-0.613 -0.56  -0.529 -0.449 -0.354 -0.314 -0.238 -0.218 -0.158 -0.123 -0.075 -0.056  0.013  0.059  0.083  0.154  0.204  0.244  0.28   0.321  0.355  0.386  0.431  0.49   0.511
  0.562  0.688]
rank 32 rows 527 association eigenvalues [-0.727 -0.662 -0.607 -0.567 -0.515 -0.452 -0.424 -0.368 -0.279 -0.234 -0.198 -0.18  -0.11  -0.091  0.025  0.055  0.081  0.126  0.157  0.205  0.224  0.278  0.316  0.349  0.423
  0.439  0.472  0.52   0.553  0.585  0.656  0.738]

INDEPENDENT CLUSTERINGS OF CURRENT/OV-PATH ROW DIRECTIONS
clusters 18 matched center cos quantiles [0.061 0.233 0.436 0.673 0.754] graph eigenvalues [-0.208 -0.167 -0.086 -0.066 -0.008 -0.006  0.029  0.075  0.093  0.119  0.159  0.18   0.206  0.232  0.275  0.277  0.353  1.   ]
cluster counts [18 21 22 22 24 25 26 26 26 27 32 34 34 34 35 37 40 42]
clusters 22 matched center cos quantiles [0.068 0.273 0.495 0.632 0.83 ] graph eigenvalues [-0.301 -0.157 -0.135 -0.098 -0.085 -0.024  0.002  0.052  0.068  0.086  0.096  0.128  0.17   0.199  0.223  0.235  0.273  0.287  0.319  0.392  0.422  1.   ]
cluster counts [14 14 16 16 20 21 21 21 22 22 22 22 23 24 26 27 27 29 30 31 34 43]
clusters 24 matched center cos quantiles [0.027 0.281 0.562 0.628 0.815] graph eigenvalues [-0.274 -0.232 -0.178 -0.153 -0.129 -0.086 -0.07  -0.021 -0.001  0.026  0.043  0.096  0.11   0.121  0.156  0.193  0.206  0.257  0.274  0.285  0.351  0.384  0.413  1.   ]
cluster counts [10 11 12 12 14 15 16 18 19 20 21 21 23 24 25 25 25 26 27 27 30 30 36 38]
24-cluster association table (unlabelled weight directions):
[[1 1 0 0 0 0 0 0 0 1 1 1 0 0 3 0 0 1 0 0 0 1 0 0]
 [0 4 2 0 2 0 0 1 5 0 0 2 1 1 2 2 0 1 0 2 1 1 0 0]
 [0 2 1 0 0 1 3 1 1 0 0 1 4 3 0 0 3 1 2 3 1 0 1 2]
 [1 0 0 2 3 0 1 0 1 1 2 2 1 0 3 2 0 1 1 1 1 1 2 0]
 [9 1 1 1 0 1 0 1 0 1 2 1 0 0 0 0 0 1 2 0 1 0 1 2]
 [0 1 0 0 0 4 0 4 0 0 2 0 1 0 0 1 0 1 0 0 0 0 3 1]
 [0 1 0 0 0 0 4 0 0 0 1 0 0 0 1 0 0 0 2 2 0 0 0 0]
 [0 3 0 0 3 1 1 6 4 1 1 5 1 0 0 0 3 4 1 0 2 0 0 0]
 [0 1 1 2 0 0 0 0 5 1 3 2 0 0 1 0 3 0 0 1 1 0 0 0]
 [1 0 2 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 1]
 [3 1 1 0 0 0 2 0 1 0 1 3 0 2 0 1 1 4 0 1 1 4 1 0]
 [0 1 0 2 1 1 0 0 0 1 1 1 0 1 1 2 2 1 2 0 2 1 2 1]
 [1 1 1 3 1 2 1 1 1 0 1 1 2 2 1 4 3 1 0 3 1 4 1 2]
 [0 1 0 2 3 0 0 1 0 0 0 0 2 2 0 0 2 2 0 0 1 2 1 0]
 [1 0 2 0 3 0 1 1 0 0 2 0 1 1 5 1 0 0 2 1 1 1 1 0]
 [0 2 3 2 2 0 0 1 1 0 0 4 2 6 0 1 1 1 1 0 0 1 2 0]
 [0 0 0 0 0 1 0 2 1 6 0 0 0 1 1 1 1 0 1 0 0 0 0 0]
 [0 3 1 1 1 2 0 1 0 2 1 1 0 0 0 2 3 2 1 2 0 0 1 1]
 [1 0 0 1 0 0 0 0 0 0 1 1 3 0 0 1 1 0 3 2 1 0 1 0]
 [0 1 2 1 1 2 1 2 1 1 1 1 2 2 0 1 0 0 3 0 0 1 0 2]
 [1 3 0 1 3 1 1 1 1 2 0 0 1 1 0 0 2 0 0 1 0 0 0 1]
 [0 0 0 1 0 0 4 0 1 3 0 0 2 3 0 0 1 1 1 0 1 3 0 0]
 [0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 6 2]
 [0 0 1 2 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 4]]
clusters 28 matched center cos quantiles [0.09  0.296 0.505 0.691 0.841] graph eigenvalues [-0.257 -0.188 -0.134 -0.114 -0.085 -0.076 -0.058 -0.017  0.006  0.031  0.052  0.065  0.106  0.125  0.165  0.176  0.212  0.248  0.255  0.267  0.283  0.308  0.35   0.37   0.398
  0.428  0.534  1.   ]
cluster counts [ 7  9 12 12 12 13 14 14 15 15 16 17 17 17 19 20 21 21 21 22 22 22 23 26 27 27 27 37]

L0H3 RAW QK COMPLEX MODES
angle, magnitude: [[0.6   0.819]
 [0.658 0.616]
 [0.679 0.45 ]
 [0.762 0.888]
 [0.763 1.001]
 [0.901 0.921]
 [0.917 0.761]
 [1.083 0.953]
 [1.428 0.77 ]
 [1.451 0.338]
 [1.573 0.607]
 [2.056 0.287]
 [2.196 0.436]
 [2.912 0.127]
 [2.913 0.155]]

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

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

AGENT_MESSAGE
```python
import numpy as np
from scipy.stats import kurtosis
from sklearn.decomposition import FastICA
from threadpoolctl import threadpool_limits
import warnings
threadpool_limits(2)
np.set_printoptions(precision=3, suppress=True, linewidth=180)

A={};B={}
for l in range(4):
    g=W[f"ln1_w.{l}"].astype(float)
    for j in range(6):
        sl=slice(j*64,j*64+64)
        q=W[f"W_q.{l}"][sl].astype(float)*g
        k=W[f"W_k.{l}"][sl].astype(float)*g
        A[l,j]=q.T@k/8
        B[l,j]=W[f"W_o.{l}"][:,sl].astype(float)@(W[f"W_v.{l}"][sl].astype(float)*g)

b=B[0,3]
_,_,vt=np.linalg.svd(W["W_v.0"][192:256].astype(float)*W["ln1_w.0"],full_matrices=False)
T=vt[:24].T

def diagnostic(c):
    n=np.linalg.norm(c)
    sym=(c+c.T)/2
    return [np.trace(c)/n,np.linalg.norm(sym)/n]

print("MATCHING-PATH DIAGNOSTICS")
print("Each pair is trace/F, symmetric_fraction.")
print("00=direct/direct; 01=direct/earlier-OV; 10=earlier-OV/direct; 11=earlier-OV/both.")
for l in range(1,4):
    for j in range(6):
        a=A[l,j]
        cs=[a,a@b,b.T@a,b.T@a@b]
        full=[diagnostic(c) for c in cs]
        sub=[diagnostic(T.T@c@T) for c in cs]
        print(f"L{l}H{j} full",np.round(full,3),"V-subspace",np.round(sub,3))

print("\nL0H3 PATH TO L3H3: POSITIVE/NEGATIVE SYMMETRIC ENERGY")
for label,c in [
    ("direct",A[3,3]),
    ("key via H3",A[3,3]@b),
    ("query via H3",b.T@A[3,3]),
    ("both via H3",b.T@A[3,3]@b)
]:
    e=np.linalg.eigvalsh((c+c.T)/2)
    print(label,"positive squared-eigenvalue fraction",np.sum(e[e>0]**2)/np.sum(e**2),
          "top positive",e[-8:],"most negative",e[:8])

print("\nICA OF FINAL MLP OUTPUT COLUMNS (weights only; no neural computation)")
X=W["W_proj.3"].astype(float).T
for n in [22,24,27,30]:
    with warnings.catch_warnings(record=True) as ws:
        ica=FastICA(n_components=n,whiten="unit-variance",random_state=0,max_iter=3000,tol=1e-4)
        Z=ica.fit_transform(X)
    ku=kurtosis(Z,axis=0,fisher=True)
    norms=np.linalg.norm(ica.mixing_,axis=0)
    order=np.argsort(norms)[::-1]
    print("components",n,"iterations",ica.n_iter_,"warnings",len(ws))
    print("mixing norm, excess kurtosis:",np.round(np.column_stack([norms[order],ku[order]]),3))

print("\nCLOCK-REGISTER WRITE STRENGTHS")
for j in range(6):
    mat=B[0,j]
    print(f"L0H{j}: row21 norm={np.linalg.norm(mat[21]):.3f}",
          f"row21 energy fraction={np.sum(mat[21]**2)/np.sum(mat**2):.3f}",
          f"rows21,39,58,99 combined fraction={np.sum(mat[[21,39,58,99]]**2)/np.sum(mat**2):.3f}")
print("Exact LayerNorm gains at coordinate 21:",
      {f"{name}.{l}":float(W[f"{name}.{l}"][21]) for l in range(4) for name in ["ln1_w","ln2_w"]})
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 6,
  "turn": "t006",
  "code": "import numpy as np\nfrom scipy.stats import kurtosis\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nimport warnings\nthreadpool_limits(2)\nnp.set_printoptions(precision=3, suppress=True, linewidth=180)\n\nA={};B={}\nfor l in range(4):\n    g=W[f\"ln1_w.{l}\"].astype(float)\n    for j in range(6):\n        sl=slice(j*64,j*64+64)\n        q=W[f\"W_q.{l}\"][sl].astype(float)*g\n        k=W[f\"W_k.{l}\"][sl].astype(float)*g\n        A[l,j]=q.T@k/8\n        B[l,j]=W[f\"W_o.{l}\"][:,sl].astype(float)@(W[f\"W_v.{l}\"][sl].astype(float)*g)\n\nb=B[0,3]\n_,_,vt=np.linalg.svd(W[\"W_v.0\"][192:256].astype(float)*W[\"ln1_w.0\"],full_matrices=False)\nT=vt[:24].T\n\ndef diagnostic(c):\n    n=np.linalg.norm(c)\n    sym=(c+c.T)/2\n    return [np.trace(c)/n,np.linalg.norm(sym)/n]\n\nprint(\"MATCHING-PATH DIAGNOSTICS\")\nprint(\"Each pair is trace/F, symmetric_fraction.\")\nprint(\"00=direct/direct; 01=direct/earlier-OV; 10=earlier-OV/direct; 11=earlier-OV/both.\")\nfor l in range(1,4):\n    for j in range(6):\n        a=A[l,j]\n        cs=[a,a@b,b.T@a,b.T@a@b]\n        full=[diagnostic(c) for c in cs]\n        sub=[diagnostic(T.T@c@T) for c in cs]\n        print(f\"L{l}H{j} full\",np.round(full,3),\"V-subspace\",np.round(sub,3))\n\nprint(\"\\nL0H3 PATH TO L3H3: POSITIVE/NEGATIVE SYMMETRIC ENERGY\")\nfor label,c in [\n    (\"direct\",A[3,3]),\n    (\"key via H3\",A[3,3]@b),\n    (\"query via H3\",b.T@A[3,3]),\n    (\"both via H3\",b.T@A[3,3]@b)\n]:\n    e=np.linalg.eigvalsh((c+c.T)/2)\n    print(label,\"positive squared-eigenvalue fraction\",np.sum(e[e>0]**2)/np.sum(e**2),\n          \"top positive\",e[-8:],\"most negative\",e[:8])\n\nprint(\"\\nICA OF FINAL MLP OUTPUT COLUMNS (weights only; no neural computation)\")\nX=W[\"W_proj.3\"].astype(float).T\nfor n in [22,24,27,30]:\n    with warnings.catch_warnings(record=True) as ws:\n        ica=FastICA(n_components=n,whiten=\"unit-variance\",random_state=0,max_iter=3000,tol=1e-4)\n        Z=ica.fit_transform(X)\n    ku=kurtosis(Z,axis=0,fisher=True)\n    norms=np.linalg.norm(ica.mixing_,axis=0)\n    order=np.argsort(norms)[::-1]\n    print(\"components\",n,\"iterations\",ica.n_iter_,\"warnings\",len(ws))\n    print(\"mixing norm, excess kurtosis:\",np.round(np.column_stack([norms[order],ku[order]]),3))\n\nprint(\"\\nCLOCK-REGISTER WRITE STRENGTHS\")\nfor j in range(6):\n    mat=B[0,j]\n    print(f\"L0H{j}: row21 norm={np.linalg.norm(mat[21]):.3f}\",\n          f\"row21 energy fraction={np.sum(mat[21]**2)/np.sum(mat**2):.3f}\",\n          f\"rows21,39,58,99 combined fraction={np.sum(mat[[21,39,58,99]]**2)/np.sum(mat**2):.3f}\")\nprint(\"Exact LayerNorm gains at coordinate 21:\",\n      {f\"{name}.{l}\":float(W[f\"{name}.{l}\"][21]) for l in range(4) for name in [\"ln1_w\",\"ln2_w\"]})",
  "out": "MATCHING-PATH DIAGNOSTICS\nEach pair is trace/F, symmetric_fraction.\n00=direct/direct; 01=direct/earlier-OV; 10=earlier-OV/direct; 11=earlier-OV/both.\nL1H0 full [[-0.883  0.722]\n [ 0.102  0.712]\n [-0.071  0.706]\n [-0.286  0.717]] V-subspace [[-2.198  0.855]\n [ 0.314  0.743]\n [-0.161  0.697]\n [-0.278  0.717]]\nL1H1 full [[-0.845  0.713]\n [ 0.216  0.713]\n [ 0.245  0.713]\n [-1.006  0.753]] V-subspace [[-1.965  0.822]\n [ 0.577  0.747]\n [ 0.514  0.737]\n [-1.01   0.754]]\nL1H2 full [[-1.059  0.72 ]\n [ 0.167  0.713]\n [-0.177  0.708]\n [-1.974  0.859]] V-subspace [[-1.49   0.782]\n [ 0.507  0.762]\n [-0.417  0.714]\n [-1.986  0.86 ]]\nL1H3 full [[-0.844  0.709]\n [ 0.076  0.712]\n [ 0.054  0.701]\n [-0.894  0.735]] V-subspace [[-1.826  0.806]\n [ 0.209  0.753]\n [ 0.141  0.683]\n [-0.892  0.736]]\nL1H4 full [[-1.234  0.723]\n [ 0.015  0.708]\n [-0.798  0.727]\n [-2.572  0.884]] V-subspace [[-1.662  0.788]\n [ 0.071  0.712]\n [-1.945  0.822]\n [-2.58   0.886]]\nL1H5 full [[-0.874  0.713]\n [ 0.198  0.717]\n [ 0.208  0.709]\n [-0.381  0.704]] V-subspace [[-2.219  0.845]\n [ 0.477  0.774]\n [ 0.436  0.724]\n [-0.372  0.704]]\nL2H0 full [[-0.954  0.693]\n [ 0.079  0.708]\n [-0.153  0.712]\n [-2.011  0.796]] V-subspace [[-1.039  0.722]\n [ 0.25   0.721]\n [-0.54   0.762]\n [-2.024  0.802]]\nL2H1 full [[-0.1    0.7  ]\n [ 0.009  0.71 ]\n [ 0.081  0.711]\n [-1.008  0.726]] V-subspace [[-0.588  0.721]\n [-0.008  0.751]\n [ 0.237  0.752]\n [-1.025  0.727]]\nL2H2 full [[-0.754  0.706]\n [ 0.099  0.707]\n [-0.182  0.709]\n [-1.854  0.795]] V-subspace [[-0.818  0.727]\n [ 0.388  0.703]\n [-0.717  0.73 ]\n [-1.865  0.8  ]]\nL2H3 full [[-0.367  0.704]\n [ 0.082  0.709]\n [ 0.101  0.71 ]\n [-1.124  0.73 ]] V-subspace [[-0.428  0.697]\n [ 0.367  0.746]\n [ 0.358  0.759]\n [-1.173  0.731]]\nL2H4 full [[-0.443  0.696]\n [ 0.159  0.71 ]\n [-0.015  0.707]\n [-1.687  0.766]] V-subspace [[-0.336  0.716]\n [ 0.72   0.766]\n [-0.027  0.69 ]\n [-1.723  0.77 ]]\nL2H5 full [[-0.118  0.699]\n [ 0.01   0.708]\n [ 0.216  0.713]\n [-0.9    0.765]] V-subspace [[-0.835  0.734]\n [ 0.002  0.724]\n [ 0.591  0.754]\n [-0.935  0.767]]\nL3H0 full [[ 1.073  0.709]\n [-0.177  0.707]\n [ 0.119  0.711]\n [-0.504  0.746]] V-subspace [[ 0.245  0.67 ]\n [-0.612  0.704]\n [ 0.567  0.756]\n [-0.587  0.751]]\nL3H1 full [[ 0.353  0.706]\n [-0.033  0.707]\n [ 0.074  0.711]\n [ 0.326  0.707]] V-subspace [[ 0.214  0.719]\n [-0.133  0.719]\n [ 0.258  0.744]\n [ 0.322  0.706]]\nL3H2 full [[ 0.242  0.703]\n [ 0.033  0.709]\n [-0.027  0.707]\n [-0.452  0.752]] V-subspace [[-0.019  0.684]\n [ 0.117  0.735]\n [-0.022  0.704]\n [-0.489  0.755]]\nL3H3 full [[ 3.2    0.778]\n [ 0.922  0.73 ]\n [-0.376  0.711]\n [ 2.364  0.821]] V-subspace [[ 0.875  0.8  ]\n [ 2.718  0.877]\n [-1.432  0.77 ]\n [ 2.316  0.828]]\nL3H4 full [[ 0.001  0.708]\n [ 0.124  0.708]\n [-0.046  0.71 ]\n [ 0.199  0.728]] V-subspace [[ 0.2    0.71 ]\n [ 0.574  0.715]\n [-0.054  0.757]\n [ 0.169  0.729]]\nL3H5 full [[ 0.863  0.71 ]\n [-0.062  0.708]\n [ 0.228  0.709]\n [-0.333  0.736]] V-subspace [[ 0.121  0.686]\n [-0.173  0.716]\n [ 0.829  0.734]\n [-0.395  0.735]]\n\nL0H3 PATH TO L3H3: POSITIVE/NEGATIVE SYMMETRIC ENERGY\ndirect positive squared-eigenvalue fraction 0.8311817865380196 top positive [0.229 0.229 0.233 0.24  0.241 0.244 0.253 0.257] most negative [-0.189 -0.165 -0.161 -0.147 -0.138 -0.134 -0.125 -0.121]\nkey via H3 positive squared-eigenvalue fraction 0.7259420110120174 top positive [0.061 0.065 0.072 0.074 0.081 0.085 0.097 0.109] most negative [-0.068 -0.056 -0.05  -0.045 -0.042 -0.042 -0.038 -0.036]\nquery via H3 positive squared-eigenvalue fraction 0.41474673409113433 top positive [0.043 0.046 0.047 0.05  0.054 0.058 0.063 0.067] most negative [-0.079 -0.076 -0.071 -0.063 -0.06  -0.059 -0.056 -0.052]\nboth via H3 positive squared-eigenvalue fraction 0.9652646152209285 top positive [0.012 0.015 0.019 0.021 0.025 0.026 0.033 0.043] most negative [-0.01  -0.007 -0.006 -0.003 -0.003 -0.001 -0.001 -0.001]\n\nICA OF FINAL MLP OUTPUT COLUMNS (weights only; no neural computation)\ncomponents 22 iterations 917 warnings 0\nmixing norm, excess kurtosis: [[ 0.585 -0.438]\n [ 0.555 -0.582]\n [ 0.527  2.328]\n [ 0.492 -0.353]\n [ 0.491  1.23 ]\n [ 0.467  1.665]\n [ 0.466  1.051]\n [ 0.465  1.808]\n [ 0.462  1.407]\n [ 0.451  0.838]\n [ 0.44   0.871]\n [ 0.437  2.203]\n [ 0.412  1.79 ]\n [ 0.403  1.388]\n [ 0.398  1.148]\n [ 0.397  1.83 ]\n [ 0.395  0.928]\n [ 0.393  2.406]\n [ 0.342  0.895]\n [ 0.337  1.73 ]\n [ 0.332  3.369]\n [ 0.244  5.268]]\ncomponents 24 iterations 310 warnings 0\nmixing norm, excess kurtosis: [[ 0.54  -0.489]\n [ 0.526 -0.574]\n [ 0.523  2.737]\n [ 0.483 -0.463]\n [ 0.478  1.504]\n [ 0.466  1.742]\n [ 0.464  1.691]\n [ 0.455  0.631]\n [ 0.447  1.569]\n [ 0.446  0.984]\n [ 0.444  0.408]\n [ 0.432  1.985]\n [ 0.426  1.134]\n [ 0.424  2.28 ]\n [ 0.39   1.494]\n [ 0.387  2.185]\n [ 0.381  2.436]\n [ 0.366  1.136]\n [ 0.366  1.477]\n [ 0.345  1.107]\n [ 0.344  1.424]\n [ 0.331  1.998]\n [ 0.328  2.904]\n [ 0.244 11.27 ]]\ncomponents 27 iterations 187 warnings 0\nmixing norm, excess kurtosis: [[ 0.516 -0.558]\n [ 0.513 -0.524]\n [ 0.498 -0.448]\n [ 0.48   1.51 ]\n [ 0.456  1.724]\n [ 0.455  1.541]\n [ 0.433  1.545]\n [ 0.432  0.781]\n [ 0.423  2.317]\n [ 0.417  1.96 ]\n [ 0.414  1.531]\n [ 0.398  1.675]\n [ 0.397  0.635]\n [ 0.39   2.752]\n [ 0.388  2.418]\n [ 0.387  1.837]\n [ 0.382  0.954]\n [ 0.382  1.401]\n [ 0.38  15.42 ]\n [ 0.361  1.259]\n [ 0.356  4.616]\n [ 0.346  1.325]\n [ 0.344  1.498]\n [ 0.334  1.459]\n [ 0.317  2.706]\n [ 0.292  1.385]\n [ 0.236 12.664]]\ncomponents 30 iterations 1151 warnings 0\nmixing norm, excess kurtosis: [[ 0.47   1.641]\n [ 0.453 -0.492]\n [ 0.452  1.487]\n [ 0.45   1.528]\n [ 0.447  1.167]\n [ 0.437  2.442]\n [ 0.412  1.03 ]\n [ 0.408  1.705]\n [ 0.408  1.568]\n [ 0.405  3.77 ]\n [ 0.403  0.834]\n [ 0.396  2.218]\n [ 0.392  0.966]\n [ 0.391  3.051]\n [ 0.383 -0.465]\n [ 0.382  1.463]\n [ 0.381  2.461]\n [ 0.38   1.318]\n [ 0.372  1.602]\n [ 0.356 19.442]\n [ 0.35   2.216]\n [ 0.345  1.398]\n [ 0.339  1.699]\n [ 0.335  1.48 ]\n [ 0.331  1.386]\n [ 0.327  1.258]\n [ 0.324  7.047]\n [ 0.312  1.476]\n [ 0.311 11.721]\n [ 0.238 12.48 ]]\n\nCLOCK-REGISTER WRITE STRENGTHS\nL0H0: row21 norm=0.047 row21 energy fraction=0.000 rows21,39,58,99 combined fraction=0.029\nL0H1: row21 norm=2.072 row21 energy fraction=0.271 rows21,39,58,99 combined fraction=0.908\nL0H2: row21 norm=1.585 row21 energy fraction=0.273 rows21,39,58,99 combined fraction=0.918\nL0H3: row21 norm=0.055 row21 energy fraction=0.000 rows21,39,58,99 combined fraction=0.003\nL0H4: row21 norm=0.732 row21 energy fraction=0.189 rows21,39,58,99 combined fraction=0.838\nL0H5: row21 norm=14.657 row21 energy fraction=0.607 rows21,39,58,99 combined fraction=0.963\nExact LayerNorm gains at coordinate 21: {'ln1_w.0': 0.5348148345947266, 'ln2_w.0': 0.11408951133489609, 'ln1_w.1': 1.084619402885437, 'ln2_w.1': 0.0018652267754077911, 'ln1_w.2': 1.0927202701568604, 'ln2_w.2': 0.01202222891151905, 'ln1_w.3': 0.8020247220993042, 'ln2_w.3': 0.23280216753482819}\n",
  "seconds": 2.324,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "be5b8c098feeec756656190b6d269331d8204ff6fa61d4f79ba456af33ae4296",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

EXACT HARNESS FEEDBACK
MATCHING-PATH DIAGNOSTICS
Each pair is trace/F, symmetric_fraction.
00=direct/direct; 01=direct/earlier-OV; 10=earlier-OV/direct; 11=earlier-OV/both.
L1H0 full [[-0.883  0.722]
 [ 0.102  0.712]
 [-0.071  0.706]
 [-0.286  0.717]] V-subspace [[-2.198  0.855]
 [ 0.314  0.743]
 [-0.161  0.697]
 [-0.278  0.717]]
L1H1 full [[-0.845  0.713]
 [ 0.216  0.713]
 [ 0.245  0.713]
 [-1.006  0.753]] V-subspace [[-1.965  0.822]
 [ 0.577  0.747]
 [ 0.514  0.737]
 [-1.01   0.754]]
L1H2 full [[-1.059  0.72 ]
 [ 0.167  0.713]
 [-0.177  0.708]
 [-1.974  0.859]] V-subspace [[-1.49   0.782]
 [ 0.507  0.762]
 [-0.417  0.714]
 [-1.986  0.86 ]]
L1H3 full [[-0.844  0.709]
 [ 0.076  0.712]
 [ 0.054  0.701]
 [-0.894  0.735]] V-subspace [[-1.826  0.806]
 [ 0.209  0.753]
 [ 0.141  0.683]
 [-0.892  0.736]]
L1H4 full [[-1.234  0.723]
 [ 0.015  0.708]
 [-0.798  0.727]
 [-2.572  0.884]] V-subspace [[-1.662  0.788]
 [ 0.071  0.712]
 [-1.945  0.822]
 [-2.58   0.886]]
L1H5 full [[-0.874  0.713]
 [ 0.198  0.717]
 [ 0.208  0.709]
 [-0.381  0.704]] V-subspace [[-2.219  0.845]
 [ 0.477  0.774]
 [ 0.436  0.724]
 [-0.372  0.704]]
L2H0 full [[-0.954  0.693]
 [ 0.079  0.708]
 [-0.153  0.712]
 [-2.011  0.796]] V-subspace [[-1.039  0.722]
 [ 0.25   0.721]
 [-0.54   0.762]
 [-2.024  0.802]]
L2H1 full [[-0.1    0.7  ]
 [ 0.009  0.71 ]
 [ 0.081  0.711]
 [-1.008  0.726]] V-subspace [[-0.588  0.721]
 [-0.008  0.751]
 [ 0.237  0.752]
 [-1.025  0.727]]
L2H2 full [[-0.754  0.706]
 [ 0.099  0.707]
 [-0.182  0.709]
 [-1.854  0.795]] V-subspace [[-0.818  0.727]
 [ 0.388  0.703]
 [-0.717  0.73 ]
 [-1.865  0.8  ]]
L2H3 full [[-0.367  0.704]
 [ 0.082  0.709]
 [ 0.101  0.71 ]
 [-1.124  0.73 ]] V-subspace [[-0.428  0.697]
 [ 0.367  0.746]
 [ 0.358  0.759]
 [-1.173  0.731]]
L2H4 full [[-0.443  0.696]
 [ 0.159  0.71 ]
 [-0.015  0.707]
 [-1.687  0.766]] V-subspace [[-0.336  0.716]
 [ 0.72   0.766]
 [-0.027  0.69 ]
 [-1.723  0.77 ]]
L2H5 full [[-0.118  0.699]
 [ 0.01   0.708]
 [ 0.216  0.713]
 [-0.9    0.765]] V-subspace [[-0.835  0.734]
 [ 0.002  0.724]
 [ 0.591  0.754]
 [-0.935  0.767]]
L3H0 full [[ 1.073  0.709]
 [-0.177  0.707]
 [ 0.119  0.711]
 [-0.504  0.746]] V-subspace [[ 0.245  0.67 ]
 [-0.612  0.704]
 [ 0.567  0.756]
 [-0.587  0.751]]
L3H1 full [[ 0.353  0.706]
 [-0.033  0.707]
 [ 0.074  0.711]
 [ 0.326  0.707]] V-subspace [[ 0.214  0.719]
 [-0.133  0.719]
 [ 0.258  0.744]
 [ 0.322  0.706]]
L3H2 full [[ 0.242  0.703]
 [ 0.033  0.709]
 [-0.027  0.707]
 [-0.452  0.752]] V-subspace [[-0.019  0.684]
 [ 0.117  0.735]
 [-0.022  0.704]
 [-0.489  0.755]]
L3H3 full [[ 3.2    0.778]
 [ 0.922  0.73 ]
 [-0.376  0.711]
 [ 2.364  0.821]] V-subspace [[ 0.875  0.8  ]
 [ 2.718  0.877]
 [-1.432  0.77 ]
 [ 2.316  0.828]]
L3H4 full [[ 0.001  0.708]
 [ 0.124  0.708]
 [-0.046  0.71 ]
 [ 0.199  0.728]] V-subspace [[ 0.2    0.71 ]
 [ 0.574  0.715]
 [-0.054  0.757]
 [ 0.169  0.729]]
L3H5 full [[ 0.863  0.71 ]
 [-0.062  0.708]
 [ 0.228  0.709]
 [-0.333  0.736]] V-subspace [[ 0.121  0.686]
 [-0.173  0.716]
 [ 0.829  0.734]
 [-0.395  0.735]]

L0H3 PATH TO L3H3: POSITIVE/NEGATIVE SYMMETRIC ENERGY
direct positive squared-eigenvalue fraction 0.8311817865380196 top positive [0.229 0.229 0.233 0.24  0.241 0.244 0.253 0.257] most negative [-0.189 -0.165 -0.161 -0.147 -0.138 -0.134 -0.125 -0.121]
key via H3 positive squared-eigenvalue fraction 0.7259420110120174 top positive [0.061 0.065 0.072 0.074 0.081 0.085 0.097 0.109] most negative [-0.068 -0.056 -0.05  -0.045 -0.042 -0.042 -0.038 -0.036]
query via H3 positive squared-eigenvalue fraction 0.41474673409113433 top positive [0.043 0.046 0.047 0.05  0.054 0.058 0.063 0.067] most negative [-0.079 -0.076 -0.071 -0.063 -0.06  -0.059 -0.056 -0.052]
both via H3 positive squared-eigenvalue fraction 0.9652646152209285 top positive [0.012 0.015 0.019 0.021 0.025 0.026 0.033 0.043] most negative [-0.01  -0.007 -0.006 -0.003 -0.003 -0.001 -0.001 -0.001]

ICA OF FINAL MLP OUTPUT COLUMNS (weights only; no neural computation)
components 22 iterations 917 warnings 0
mixing norm, excess kurtosis: [[ 0.585 -0.438]
 [ 0.555 -0.582]
 [ 0.527  2.328]
 [ 0.492 -0.353]
 [ 0.491  1.23 ]
 [ 0.467  1.665]
 [ 0.466  1.051]
 [ 0.465  1.808]
 [ 0.462  1.407]
 [ 0.451  0.838]
 [ 0.44   0.871]
 [ 0.437  2.203]
 [ 0.412  1.79 ]
 [ 0.403  1.388]
 [ 0.398  1.148]
 [ 0.397  1.83 ]
 [ 0.395  0.928]
 [ 0.393  2.406]
 [ 0.342  0.895]
 [ 0.337  1.73 ]
 [ 0.332  3.369]
 [ 0.244  5.268]]
components 24 iterations 310 warnings 0
mixing norm, excess kurtosis: [[ 0.54  -0.489]
 [ 0.526 -0.574]
 [ 0.523  2.737]
 [ 0.483 -0.463]
 [ 0.478  1.504]
 [ 0.466  1.742]
 [ 0.464  1.691]
 [ 0.455  0.631]
 [ 0.447  1.569]
 [ 0.446  0.984]
 [ 0.444  0.408]
 [ 0.432  1.985]
 [ 0.426  1.134]
 [ 0.424  2.28 ]
 [ 0.39   1.494]
 [ 0.387  2.185]
 [ 0.381  2.436]
 [ 0.366  1.136]
 [ 0.366  1.477]
 [ 0.345  1.107]
 [ 0.344  1.424]
 [ 0.331  1.998]
 [ 0.328  2.904]
 [ 0.244 11.27 ]]
components 27 iterations 187 warnings 0
mixing norm, excess kurtosis: [[ 0.516 -0.558]
 [ 0.513 -0.524]
 [ 0.498 -0.448]
 [ 0.48   1.51 ]
 [ 0.456  1.724]
 [ 0.455  1.541]
 [ 0.433  1.545]
 [ 0.432  0.781]
 [ 0.423  2.317]
 [ 0.417  1.96 ]
 [ 0.414  1.531]
 [ 0.398  1.675]
 [ 0.397  0.635]
 [ 0.39   2.752]
 [ 0.388  2.418]
 [ 0.387  1.837]
 [ 0.382  0.954]
 [ 0.382  1.401]
 [ 0.38  15.42 ]
 [ 0.361  1.259]
 [ 0.356  4.616]
 [ 0.346  1.325]
 [ 0.344  1.498]
 [ 0.334  1.459]
 [ 0.317  2.706]
 [ 0.292  1.385]
 [ 0.236 12.664]]
components 30 iterations 1151 warnings 0
mixing norm, excess kurtosis: [[ 0.47   1.641]
 [ 0.453 -0.492]
 [ 0.452  1.487]
 [ 0.45   1.528]
 [ 0.447  1.167]
 [ 0.437  2.442]
 [ 0.412  1.03 ]
 [ 0.408  1.705]
 [ 0.408  1.568]
 [ 0.405  3.77 ]
 [ 0.403  0.834]
 [ 0.396  2.218]
 [ 0.392  0.966]
 [ 0.391  3.051]
 [ 0.383 -0.465]
 [ 0.382  1.463]
 [ 0.381  2.461]
 [ 0.38   1.318]
 [ 0.372  1.602]
 [ 0.356 19.442]
 [ 0.35   2.216]
 [ 0.345  1.398]
 [ 0.339  1.699]
 [ 0.335  1.48 ]
 [ 0.331  1.386]
 [ 0.327  1.258]
 [ 0.324  7.047]
 [ 0.312  1.476]
 [ 0.311 11.721]
 [ 0.238 12.48 ]]

CLOCK-REGISTER WRITE STRENGTHS
L0H0: row21 norm=0.047 row21 energy fraction=0.000 rows21,39,58,99 combined fraction=0.029
L0H1: row21 norm=2.072 row21 energy fraction=0.271 rows21,39,58,99 combined fraction=0.908
L0H2: row21 norm=1.585 row21 energy fraction=0.273 rows21,39,58,99 combined fraction=0.918
L0H3: row21 norm=0.055 row21 energy fraction=0.000 rows21,39,58,99 combined fraction=0.003
L0H4: row21 norm=0.732 row21 energy fraction=0.189 rows21,39,58,99 combined fraction=0.838
L0H5: row21 norm=14.657 row21 energy fraction=0.607 rows21,39,58,99 combined fraction=0.963
Exact LayerNorm gains at coordinate 21: {'ln1_w.0': 0.5348148345947266, 'ln2_w.0': 0.11408951133489609, 'ln1_w.1': 1.084619402885437, 'ln2_w.1': 0.0018652267754077911, 'ln1_w.2': 1.0927202701568604, 'ln2_w.2': 0.01202222891151905, 'ln1_w.3': 0.8020247220993042, 'ln2_w.3': 0.23280216753482819}


========================================================================
TURN t007
MESSAGE SENT TO CODEX
OUTPUT of run 6 of at most 100:
```
MATCHING-PATH DIAGNOSTICS
Each pair is trace/F, symmetric_fraction.
00=direct/direct; 01=direct/earlier-OV; 10=earlier-OV/direct; 11=earlier-OV/both.
L1H0 full [[-0.883  0.722]
 [ 0.102  0.712]
 [-0.071  0.706]
 [-0.286  0.717]] V-subspace [[-2.198  0.855]
 [ 0.314  0.743]
 [-0.161  0.697]
 [-0.278  0.717]]
L1H1 full [[-0.845  0.713]
 [ 0.216  0.713]
 [ 0.245  0.713]
 [-1.006  0.753]] V-subspace [[-1.965  0.822]
 [ 0.577  0.747]
 [ 0.514  0.737]
 [-1.01   0.754]]
L1H2 full [[-1.059  0.72 ]
 [ 0.167  0.713]
 [-0.177  0.708]
 [-1.974  0.859]] V-subspace [[-1.49   0.782]
 [ 0.507  0.762]
 [-0.417  0.714]
 [-1.986  0.86 ]]
L1H3 full [[-0.844  0.709]
 [ 0.076  0.712]
 [ 0.054  0.701]
 [-0.894  0.735]] V-subspace [[-1.826  0.806]
 [ 0.209  0.753]
 [ 0.141  0.683]
 [-0.892  0.736]]
L1H4 full [[-1.234  0.723]
 [ 0.015  0.708]
 [-0.798  0.727]
 [-2.572  0.884]] V-subspace [[-1.662  0.788]
 [ 0.071  0.712]
 [-1.945  0.822]
 [-2.58   0.886]]
L1H5 full [[-0.874  0.713]
 [ 0.198  0.717]
 [ 0.208  0.709]
 [-0.381  0.704]] V-subspace [[-2.219  0.845]
 [ 0.477  0.774]
 [ 0.436  0.724]
 [-0.372  0.704]]
L2H0 full [[-0.954  0.693]
 [ 0.079  0.708]
 [-0.153  0.712]
 [-2.011  0.796]] V-subspace [[-1.039  0.722]
 [ 0.25   0.721]
 [-0.54   0.762]
 [-2.024  0.802]]
L2H1 full [[-0.1    0.7  ]
 [ 0.009  0.71 ]
 [ 0.081  0.711]
 [-1.008  0.726]] V-subspace [[-0.588  0.721]
 [-0.008  0.751]
 [ 0.237  0.752]
 [-1.025  0.727]]
L2H2 full [[-0.754  0.706]
 [ 0.099  0.707]
 [-0.182  0.709]
 [-1.854  0.795]] V-subspace [[-0.818  0.727]
 [ 0.388  0.703]
 [-0.717  0.73 ]
 [-1.865  0.8  ]]
L2H3 full [[-0.367  0.704]
 [ 0.082  0.709]
 [ 0.101  0.71 ]
 [-1.124  0.73 ]] V-subspace [[-0.428  0.697]
 [ 0.367  0.746]
 [ 0.358  0.759]
 [-1.173  0.731]]
L2H4 full [[-0.443  0.696]
 [ 0.159  0.71 ]
 [-0.015  0.707]
 [-1.687  0.766]] V-subspace [[-0.336  0.716]
 [ 0.72   0.766]
 [-0.027  0.69 ]
 [-1.723  0.77 ]]
L2H5 full [[-0.118  0.699]
 [ 0.01   0.708]
 [ 0.216  0.713]
 [-0.9    0.765]] V-subspace [[-0.835  0.734]
 [ 0.002  0.724]
 [ 0.591  0.754]
 [-0.935  0.767]]
L3H0 full [[ 1.073  0.709]
 [-0.177  0.707]
 [ 0.119  0.711]
 [-0.504  0.746]] V-subspace [[ 0.245  0.67 ]
 [-0.612  0.704]
 [ 0.567  0.756]
 [-0.587  0.751]]
L3H1 full [[ 0.353  0.706]
 [-0.033  0.707]
 [ 0.074  0.711]
 [ 0.326  0.707]] V-subspace [[ 0.214  0.719]
 [-0.133  0.719]
 [ 0.258  0.744]
 [ 0.322  0.706]]
L3H2 full [[ 0.242  0.703]
 [ 0.033  0.709]
 [-0.027  0.707]
 [-0.452  0.752]] V-subspace [[-0.019  0.684]
 [ 0.117  0.735]
 [-0.022  0.704]
 [-0.489  0.755]]
L3H3 full [[ 3.2    0.778]
 [ 0.922  0.73 ]
 [-0.376  0.711]
 [ 2.364  0.821]] V-subspace [[ 0.875  0.8  ]
 [ 2.718  0.877]
 [-1.432  0.77 ]
 [ 2.316  0.828]]
L3H4 full [[ 0.001  0.708]
 [ 0.124  0.708]
 [-0.046  0.71 ]
 [ 0.199  0.728]] V-subspace [[ 0.2    0.71 ]
 [ 0.574  0.715]
 [-0.054  0.757]
 [ 0.169  0.729]]
L3H5 full [[ 0.863  0.71 ]
 [-0.062  0.708]
 [ 0.228  0.709]
 [-0.333  0.736]] V-subspace [[ 0.121  0.686]
 [-0.173  0.716]
 [ 0.829  0.734]
 [-0.395  0.735]]

L0H3 PATH TO L3H3: POSITIVE/NEGATIVE SYMMETRIC ENERGY
direct positive squared-eigenvalue fraction 0.8311817865380196 top positive [0.229 0.229 0.233 0.24  0.241 0.244 0.253 0.257] most negative [-0.189 -0.165 -0.161 -0.147 -0.138 -0.134 -0.125 -0.121]
key via H3 positive squared-eigenvalue fraction 0.7259420110120174 top positive [0.061 0.065 0.072 0.074 0.081 0.085 0.097 0.109] most negative [-0.068 -0.056 -0.05  -0.045 -0.042 -0.042 -0.038 -0.036]
query via H3 positive squared-eigenvalue fraction 0.41474673409113433 top positive [0.043 0.046 0.047 0.05  0.054 0.058 0.063 0.067] most negative [-0.079 -0.076 -0.071 -0.063 -0.06  -0.059 -0.056 -0.052]
both via H3 positive squared-eigenvalue fraction 0.9652646152209285 top positive [0.012 0.015 0.019 0.021 0.025 0.026 0.033 0.043] most negative [-0.01  -0.007 -0.006 -0.003 -0.003 -0.001 -0.001 -0.001]

ICA OF FINAL MLP OUTPUT COLUMNS (weights only; no neural computation)
components 22 iterations 917 warnings 0
mixing norm, excess kurtosis: [[ 0.585 -0.438]
 [ 0.555 -0.582]
 [ 0.527  2.328]
 [ 0.492 -0.353]
 [ 0.491  1.23 ]
 [ 0.467  1.665]
 [ 0.466  1.051]
 [ 0.465  1.808]
 [ 0.462  1.407]
 [ 0.451  0.838]
 [ 0.44   0.871]
 [ 0.437  2.203]
 [ 0.412  1.79 ]
 [ 0.403  1.388]
 [ 0.398  1.148]
 [ 0.397  1.83 ]
 [ 0.395  0.928]
 [ 0.393  2.406]
 [ 0.342  0.895]
 [ 0.337  1.73 ]
 [ 0.332  3.369]
 [ 0.244  5.268]]
components 24 iterations 310 warnings 0
mixing norm, excess kurtosis: [[ 0.54  -0.489]
 [ 0.526 -0.574]
 [ 0.523  2.737]
 [ 0.483 -0.463]
 [ 0.478  1.504]
 [ 0.466  1.742]
 [ 0.464  1.691]
 [ 0.455  0.631]
 [ 0.447  1.569]
 [ 0.446  0.984]
 [ 0.444  0.408]
 [ 0.432  1.985]
 [ 0.426  1.134]
 [ 0.424  2.28 ]
 [ 0.39   1.494]
 [ 0.387  2.185]
 [ 0.381  2.436]
 [ 0.366  1.136]
 [ 0.366  1.477]
 [ 0.345  1.107]
 [ 0.344  1.424]
 [ 0.331  1.998]
 [ 0.328  2.904]
 [ 0.244 11.27 ]]
components 27 iterations 187 warnings 0
mixing norm, excess kurtosis: [[ 0.516 -0.558]
 [ 0.513 -0.524]
 [ 0.498 -0.448]
 [ 0.48   1.51 ]
 [ 0.456  1.724]
 [ 0.455  1.541]
 [ 0.433  1.545]
 [ 0.432  0.781]
 [ 0.423  2.317]
 [ 0.417  1.96 ]
 [ 0.414  1.531]
 [ 0.398  1.675]
 [ 0.397  0.635]
 [ 0.39   2.752]
 [ 0.388  2.418]
 [ 0.387  1.837]
 [ 0.382  0.954]
 [ 0.382  1.401]
 [ 0.38  15.42 ]
 [ 0.361  1.259]
 [ 0.356  4.616]
 [ 0.346  1.325]
 [ 0.344  1.498]
 [ 0.334  1.459]
 [ 0.317  2.706]
 [ 0.292  1.385]
 [ 0.236 12.664]]
components 30 iterations 1151 warnings 0
mixing norm, excess kurtosis: [[ 0.47   1.641]
 [ 0.453 -0.492]
 [ 0.452  1.487]
 [ 0.45   1.528]
 [ 0.447  1.167]
 [ 0.437  2.442]
 [ 0.412  1.03 ]
 [ 0.408  1.705]
 [ 0.408  1.568]
 [ 0.405  3.77 ]
 [ 0.403  0.834]
 [ 0.396  2.218]
 [ 0.392  0.966]
 [ 0.391  3.051]
 [ 0.383 -0.465]
 [ 0.382  1.463]
 [ 0.381  2.461]
 [ 0.38   1.318]
 [ 0.372  1.602]
 [ 0.356 19.442]
 [ 0.35   2.216]
 [ 0.345  1.398]
 [ 0.339  1.699]
 [ 0.335  1.48 ]
 [ 0.331  1.386]
 [ 0.327  1.258]
 [ 0.324  7.047]
 [ 0.312  1.476]
 [ 0.311 11.721]
 [ 0.238 12.48 ]]

CLOCK-REGISTER WRITE STRENGTHS
L0H0: row21 norm=0.047 row21 energy fraction=0.000 rows21,39,58,99 combined fraction=0.029
L0H1: row21 norm=2.072 row21 energy fraction=0.271 rows21,39,58,99 combined fraction=0.908
L0H2: row21 norm=1.585 row21 energy fraction=0.273 rows21,39,58,99 combined fraction=0.918
L0H3: row21 norm=0.055 row21 energy fraction=0.000 rows21,39,58,99 combined fraction=0.003
L0H4: row21 norm=0.732 row21 energy fraction=0.189 rows21,39,58,99 combined fraction=0.838
L0H5: row21 norm=14.657 row21 energy fraction=0.607 rows21,39,58,99 combined fraction=0.963
Exact LayerNorm gains at coordinate 21: {'ln1_w.0': 0.5348148345947266, 'ln2_w.0': 0.11408951133489609, 'ln1_w.1': 1.084619402885437, 'ln2_w.1': 0.0018652267754077911, 'ln1_w.2': 1.0927202701568604, 'ln2_w.2': 0.01202222891151905, 'ln1_w.3': 0.8020247220993042, 'ln2_w.3': 0.23280216753482819}

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

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

AGENT_MESSAGE
```python
import numpy as np
from threadpoolctl import threadpool_limits
threadpool_limits(2)
np.set_printoptions(precision=3, suppress=True, linewidth=180)
rng=np.random.default_rng(1729)

# C = I - 11^T/d is used only as a linear-algebra projector on weights.
# No normalization, attention, activation function, or network evaluation is performed.
def centered_reader(name,l,j):
    a=W[f"{name}.{l}"][64*j:64*j+64].astype(float)*W[f"ln1_w.{l}"]
    return a-a.mean(axis=1,keepdims=True)

A={};B={};Kbias={}
for l in range(4):
    bias=W[f"ln1_b.{l}"].astype(float)
    for j in range(6):
        q=centered_reader("W_q",l,j)
        k=centered_reader("W_k",l,j)
        v=centered_reader("W_v",l,j)
        o=W[f"W_o.{l}"][:,64*j:64*j+64].astype(float)
        A[l,j]=q.T@k/8
        B[l,j]=o@v
        Kbias[l,j]=k.T@(W[f"W_q.{l}"][64*j:64*j+64].astype(float)@bias)/8

print("CENTERED WEIGHT DIAGNOSTICS")
print("QKnorm; QK effective rank; trace/F; key-affine norm; fraction of key-affine energy at coordinate 21")
for l in range(4):
    for j in range(6):
        a=A[l,j]
        s=np.linalg.svd(a,compute_uv=False)[:64]
        z=Kbias[l,j]
        print(f"L{l}H{j}",np.round([
            np.linalg.norm(a),(s@s)**2/np.sum(s**4),
            np.trace(a)/np.linalg.norm(a),np.linalg.norm(z),
            z[21]**2/(z@z)],4))

v=centered_reader("W_v",0,3)
o=W["W_o.0"][:,192:256].astype(float)
_,_,vt=np.linalg.svd(v,full_matrices=False)
perms=[rng.permutation(64) for _ in range(300)]

print("\nKEY-SIDE MATCHING, WITH BROKEN-OV-PAIRING CONTROLS")
for r in [16,20,24,28,32]:
    T=vt[:r].T
    right=v@T
    print("subspace rank",r)
    for l,j in [(1,1),(1,2),(1,5),(2,4),(3,0),(3,3),(3,4)]:
        left=T.T@A[l,j]@o
        c=left@right
        actual=np.trace(c)/np.linalg.norm(c)
        null=np.array([np.trace(left[:,p]@right)/np.linalg.norm(left[:,p]@right) for p in perms])
        print(f"L{l}H{j}: actual={actual:.3f} null_mean={null.mean():.3f}",
              f"null_sd={null.std():.3f} null_max={null.max():.3f}")

print("\nL0H0 RELATIVE ROTATION CHECK: ROBUSTNESS TO SUBSPACE SIZE")
q=W["W_q.0"][192:256].astype(float)
k=W["W_k.0"][192:256].astype(float)
ref=q.T@k/8
u,s,vt=np.linalg.svd(ref,full_matrices=False)
a0=W["W_q.0"][:64].astype(float).T@W["W_k.0"][:64].astype(float)/8
for rank in [24,32,40,48,56]:
    U=np.linalg.svd(np.hstack([u[:,:rank],vt[:rank].T]),full_matrices=False)[0][:,:rank]
    base=U.T@ref@U
    uu,ss,vv=np.linalg.svd(base)
    rotation=uu@vv
    target=U.T@a0@U
    correlations=[]
    for n in range(-8,9):
        p=np.linalg.matrix_power(rotation,n)
        correlations.append((n,float(np.sum(target*p)/(np.linalg.norm(target)*np.linalg.norm(p)))))
    print("rank",rank,"best",sorted(correlations,key=lambda x:-x[1])[:5])
```


PERMITTED EXPERIMENT HARNESS RECORD: python
{
  "run": 7,
  "turn": "t007",
  "code": "import numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\nnp.set_printoptions(precision=3, suppress=True, linewidth=180)\nrng=np.random.default_rng(1729)\n\n# C = I - 11^T/d is used only as a linear-algebra projector on weights.\n# No normalization, attention, activation function, or network evaluation is performed.\ndef centered_reader(name,l,j):\n    a=W[f\"{name}.{l}\"][64*j:64*j+64].astype(float)*W[f\"ln1_w.{l}\"]\n    return a-a.mean(axis=1,keepdims=True)\n\nA={};B={};Kbias={}\nfor l in range(4):\n    bias=W[f\"ln1_b.{l}\"].astype(float)\n    for j in range(6):\n        q=centered_reader(\"W_q\",l,j)\n        k=centered_reader(\"W_k\",l,j)\n        v=centered_reader(\"W_v\",l,j)\n        o=W[f\"W_o.{l}\"][:,64*j:64*j+64].astype(float)\n        A[l,j]=q.T@k/8\n        B[l,j]=o@v\n        Kbias[l,j]=k.T@(W[f\"W_q.{l}\"][64*j:64*j+64].astype(float)@bias)/8\n\nprint(\"CENTERED WEIGHT DIAGNOSTICS\")\nprint(\"QKnorm; QK effective rank; trace/F; key-affine norm; fraction of key-affine energy at coordinate 21\")\nfor l in range(4):\n    for j in range(6):\n        a=A[l,j]\n        s=np.linalg.svd(a,compute_uv=False)[:64]\n        z=Kbias[l,j]\n        print(f\"L{l}H{j}\",np.round([\n            np.linalg.norm(a),(s@s)**2/np.sum(s**4),\n            np.trace(a)/np.linalg.norm(a),np.linalg.norm(z),\n            z[21]**2/(z@z)],4))\n\nv=centered_reader(\"W_v\",0,3)\no=W[\"W_o.0\"][:,192:256].astype(float)\n_,_,vt=np.linalg.svd(v,full_matrices=False)\nperms=[rng.permutation(64) for _ in range(300)]\n\nprint(\"\\nKEY-SIDE MATCHING, WITH BROKEN-OV-PAIRING CONTROLS\")\nfor r in [16,20,24,28,32]:\n    T=vt[:r].T\n    right=v@T\n    print(\"subspace rank\",r)\n    for l,j in [(1,1),(1,2),(1,5),(2,4),(3,0),(3,3),(3,4)]:\n        left=T.T@A[l,j]@o\n        c=left@right\n        actual=np.trace(c)/np.linalg.norm(c)\n        null=np.array([np.trace(left[:,p]@right)/np.linalg.norm(left[:,p]@right) for p in perms])\n        print(f\"L{l}H{j}: actual={actual:.3f} null_mean={null.mean():.3f}\",\n              f\"null_sd={null.std():.3f} null_max={null.max():.3f}\")\n\nprint(\"\\nL0H0 RELATIVE ROTATION CHECK: ROBUSTNESS TO SUBSPACE SIZE\")\nq=W[\"W_q.0\"][192:256].astype(float)\nk=W[\"W_k.0\"][192:256].astype(float)\nref=q.T@k/8\nu,s,vt=np.linalg.svd(ref,full_matrices=False)\na0=W[\"W_q.0\"][:64].astype(float).T@W[\"W_k.0\"][:64].astype(float)/8\nfor rank in [24,32,40,48,56]:\n    U=np.linalg.svd(np.hstack([u[:,:rank],vt[:rank].T]),full_matrices=False)[0][:,:rank]\n    base=U.T@ref@U\n    uu,ss,vv=np.linalg.svd(base)\n    rotation=uu@vv\n    target=U.T@a0@U\n    correlations=[]\n    for n in range(-8,9):\n        p=np.linalg.matrix_power(rotation,n)\n        correlations.append((n,float(np.sum(target*p)/(np.linalg.norm(target)*np.linalg.norm(p)))))\n    print(\"rank\",rank,\"best\",sorted(correlations,key=lambda x:-x[1])[:5])",
  "out": "CENTERED WEIGHT DIAGNOSTICS\nQKnorm; QK effective rank; trace/F; key-affine norm; fraction of key-affine energy at coordinate 21\nL0H0 [ 0.293  1.893 -0.357  0.043  0.019]\nL0H1 [ 0.159  1.248 -0.619  0.013  0.   ]\nL0H2 [ 0.107  1.209 -0.813  0.007  0.   ]\nL0H3 [ 0.698 17.073  1.923  0.043  0.016]\nL0H4 [ 0.088  1.514 -0.519  0.017  0.009]\nL0H5 [ 0.034  1.229 -0.253  0.005  0.003]\nL1H0 [ 1.978  5.901 -0.884  3.974  0.831]\nL1H1 [ 1.843  5.076 -0.845  3.688  0.824]\nL1H2 [ 1.781  4.525 -1.06   3.051  0.822]\nL1H3 [ 1.787  4.33  -0.846  3.39   0.824]\nL1H4 [ 1.629  4.856 -1.234  2.329  0.742]\nL1H5 [ 1.863  5.366 -0.875  3.895  0.835]\nL2H0 [ 1.631 16.641 -0.953  2.02   0.804]\nL2H1 [ 2.154 31.479 -0.1    1.88   0.799]\nL2H2 [ 1.72  31.296 -0.754  1.608  0.767]\nL2H3 [ 1.623 31.026 -0.367  1.06   0.575]\nL2H4 [ 2.215 22.89  -0.442  2.305  0.802]\nL2H5 [ 2.264 30.297 -0.118  2.023  0.842]\nL3H0 [ 2.029 44.28   1.073  0.5    0.663]\nL3H1 [ 1.216 34.005  0.355  0.28   0.416]\nL3H2 [ 1.437 40.138  0.243  0.495  0.595]\nL3H3 [ 1.815 53.475  3.212  0.22   0.003]\nL3H4 [ 0.623 19.228  0.003  0.144  0.037]\nL3H5 [ 1.665 41.691  0.863  0.493  0.71 ]\n\nKEY-SIDE MATCHING, WITH BROKEN-OV-PAIRING CONTROLS\nsubspace rank 16\nL1H1: actual=0.690 null_mean=0.083 null_sd=0.240 null_max=0.828\nL1H2: actual=0.474 null_mean=0.045 null_sd=0.239 null_max=0.744\nL1H5: actual=0.552 null_mean=0.041 null_sd=0.252 null_max=0.774\nL2H4: actual=0.796 null_mean=0.066 null_sd=0.233 null_max=0.796\nL3H0: actual=-0.734 null_mean=-0.065 null_sd=0.227 null_max=0.559\nL3H3: actual=2.724 null_mean=0.042 null_sd=0.247 null_max=0.684\nL3H4: actual=0.652 null_mean=-0.006 null_sd=0.247 null_max=0.687\nsubspace rank 20\nL1H1: actual=0.632 null_mean=0.087 null_sd=0.217 null_max=0.701\nL1H2: actual=0.482 null_mean=0.037 null_sd=0.207 null_max=0.575\nL1H5: actual=0.518 null_mean=0.036 null_sd=0.228 null_max=0.659\nL2H4: actual=0.708 null_mean=0.058 null_sd=0.218 null_max=0.776\nL3H0: actual=-0.629 null_mean=-0.062 null_sd=0.202 null_max=0.493\nL3H3: actual=2.858 null_mean=0.049 null_sd=0.226 null_max=0.655\nL3H4: actual=0.682 null_mean=-0.001 null_sd=0.224 null_max=0.618\nsubspace rank 24\nL1H1: actual=0.580 null_mean=0.070 null_sd=0.204 null_max=0.574\nL1H2: actual=0.508 null_mean=0.026 null_sd=0.184 null_max=0.443\nL1H5: actual=0.480 null_mean=0.033 null_sd=0.211 null_max=0.619\nL2H4: actual=0.727 null_mean=0.044 null_sd=0.202 null_max=0.793\nL3H0: actual=-0.614 null_mean=-0.044 null_sd=0.184 null_max=0.476\nL3H3: actual=2.716 null_mean=0.034 null_sd=0.212 null_max=0.586\nL3H4: actual=0.593 null_mean=0.001 null_sd=0.205 null_max=0.558\nsubspace rank 28\nL1H1: actual=0.573 null_mean=0.069 null_sd=0.192 null_max=0.533\nL1H2: actual=0.494 null_mean=0.023 null_sd=0.175 null_max=0.427\nL1H5: actual=0.483 null_mean=0.034 null_sd=0.198 null_max=0.602\nL2H4: actual=0.645 null_mean=0.028 null_sd=0.188 null_max=0.659\nL3H0: actual=-0.585 null_mean=-0.036 null_sd=0.175 null_max=0.457\nL3H3: actual=2.614 null_mean=0.026 null_sd=0.203 null_max=0.600\nL3H4: actual=0.523 null_mean=0.004 null_sd=0.191 null_max=0.487\nsubspace rank 32\nL1H1: actual=0.567 null_mean=0.065 null_sd=0.186 null_max=0.506\nL1H2: actual=0.478 null_mean=0.025 null_sd=0.168 null_max=0.401\nL1H5: actual=0.489 null_mean=0.039 null_sd=0.186 null_max=0.573\nL2H4: actual=0.598 null_mean=0.032 null_sd=0.177 null_max=0.655\nL3H0: actual=-0.593 null_mean=-0.032 null_sd=0.167 null_max=0.473\nL3H3: actual=2.505 null_mean=0.027 null_sd=0.190 null_max=0.554\nL3H4: actual=0.477 null_mean=0.004 null_sd=0.181 null_max=0.486\n\nL0H0 RELATIVE ROTATION CHECK: ROBUSTNESS TO SUBSPACE SIZE\nrank 24 best [(4, 0.24811147243940024), (3, 0.21456492278914205), (5, 0.13972628524098518), (-3, 0.08686549543323072), (-4, 0.05001577182505205)]\nrank 32 best [(4, 0.21871870766887486), (3, 0.16649449461447283), (5, 0.13166180385313234), (-3, 0.07522594557246681), (-4, 0.05806725638638227)]\nrank 40 best [(4, 0.15680824363317394), (5, 0.11052788607755092), (3, 0.10991298663568431), (-4, 0.060306698329549525), (-3, 0.05568532840411643)]\nrank 48 best [(4, 0.14490451598858572), (3, 0.12561806339108184), (5, 0.10801204262920121), (-4, 0.06819183410170707), (-5, 0.052344841947909476)]\nrank 56 best [(4, 0.13409521327824356), (5, 0.12145249348505678), (3, 0.10839131918587229), (-4, 0.051381216474243305), (6, 0.05000059909522994)]\n",
  "seconds": 0.69,
  "rc": 0,
  "timed_out": false,
  "truncated": false,
  "code_sha256": "223b53f5c15cdeaf2f092e99202d2b6dd96daabc46915079cec4560a7b848dec",
  "weights_sha256": "04ec0b300d207b26b92a1af6d49d56d452833e39b35826200bc1d821e5b7d930"
}

EXACT HARNESS FEEDBACK
CENTERED WEIGHT DIAGNOSTICS
QKnorm; QK effective rank; trace/F; key-affine norm; fraction of key-affine energy at coordinate 21
L0H0 [ 0.293  1.893 -0.357  0.043  0.019]
L0H1 [ 0.159  1.248 -0.619  0.013  0.   ]
L0H2 [ 0.107  1.209 -0.813  0.007  0.   ]
L0H3 [ 0.698 17.073  1.923  0.043  0.016]
L0H4 [ 0.088  1.514 -0.519  0.017  0.009]
L0H5 [ 0.034  1.229 -0.253  0.005  0.003]
L1H0 [ 1.978  5.901 -0.884  3.974  0.831]
L1H1 [ 1.843  5.076 -0.845  3.688  0.824]
L1H2 [ 1.781  4.525 -1.06   3.051  0.822]
L1H3 [ 1.787  4.33  -0.846  3.39   0.824]
L1H4 [ 1.629  4.856 -1.234  2.329  0.742]
L1H5 [ 1.863  5.366 -0.875  3.895  0.835]
L2H0 [ 1.631 16.641 -0.953  2.02   0.804]
L2H1 [ 2.154 31.479 -0.1    1.88   0.799]
L2H2 [ 1.72  31.296 -0.754  1.608  0.767]
L2H3 [ 1.623 31.026 -0.367  1.06   0.575]
L2H4 [ 2.215 22.89  -0.442  2.305  0.802]
L2H5 [ 2.264 30.297 -0.118  2.023  0.842]
L3H0 [ 2.029 44.28   1.073  0.5    0.663]
L3H1 [ 1.216 34.005  0.355  0.28   0.416]
L3H2 [ 1.437 40.138  0.243  0.495  0.595]
L3H3 [ 1.815 53.475  3.212  0.22   0.003]
L3H4 [ 0.623 19.228  0.003  0.144  0.037]
L3H5 [ 1.665 41.691  0.863  0.493  0.71 ]

KEY-SIDE MATCHING, WITH BROKEN-OV-PAIRING CONTROLS
subspace rank 16
L1H1: actual=0.690 null_mean=0.083 null_sd=0.240 null_max=0.828
L1H2: actual=0.474 null_mean=0.045 null_sd=0.239 null_max=0.744
L1H5: actual=0.552 null_mean=0.041 null_sd=0.252 null_max=0.774
L2H4: actual=0.796 null_mean=0.066 null_sd=0.233 null_max=0.796
L3H0: actual=-0.734 null_mean=-0.065 null_sd=0.227 null_max=0.559
L3H3: actual=2.724 null_mean=0.042 null_sd=0.247 null_max=0.684
L3H4: actual=0.652 null_mean=-0.006 null_sd=0.247 null_max=0.687
subspace rank 20
L1H1: actual=0.632 null_mean=0.087 null_sd=0.217 null_max=0.701
L1H2: actual=0.482 null_mean=0.037 null_sd=0.207 null_max=0.575
L1H5: actual=0.518 null_mean=0.036 null_sd=0.228 null_max=0.659
L2H4: actual=0.708 null_mean=0.058 null_sd=0.218 null_max=0.776
L3H0: actual=-0.629 null_mean=-0.062 null_sd=0.202 null_max=0.493
L3H3: actual=2.858 null_mean=0.049 null_sd=0.226 null_max=0.655
L3H4: actual=0.682 null_mean=-0.001 null_sd=0.224 null_max=0.618
subspace rank 24
L1H1: actual=0.580 null_mean=0.070 null_sd=0.204 null_max=0.574
L1H2: actual=0.508 null_mean=0.026 null_sd=0.184 null_max=0.443
L1H5: actual=0.480 null_mean=0.033 null_sd=0.211 null_max=0.619
L2H4: actual=0.727 null_mean=0.044 null_sd=0.202 null_max=0.793
L3H0: actual=-0.614 null_mean=-0.044 null_sd=0.184 null_max=0.476
L3H3: actual=2.716 null_mean=0.034 null_sd=0.212 null_max=0.586
L3H4: actual=0.593 null_mean=0.001 null_sd=0.205 null_max=0.558
subspace rank 28
L1H1: actual=0.573 null_mean=0.069 null_sd=0.192 null_max=0.533
L1H2: actual=0.494 null_mean=0.023 null_sd=0.175 null_max=0.427
L1H5: actual=0.483 null_mean=0.034 null_sd=0.198 null_max=0.602
L2H4: actual=0.645 null_mean=0.028 null_sd=0.188 null_max=0.659
L3H0: actual=-0.585 null_mean=-0.036 null_sd=0.175 null_max=0.457
L3H3: actual=2.614 null_mean=0.026 null_sd=0.203 null_max=0.600
L3H4: actual=0.523 null_mean=0.004 null_sd=0.191 null_max=0.487
subspace rank 32
L1H1: actual=0.567 null_mean=0.065 null_sd=0.186 null_max=0.506
L1H2: actual=0.478 null_mean=0.025 null_sd=0.168 null_max=0.401
L1H5: actual=0.489 null_mean=0.039 null_sd=0.186 null_max=0.573
L2H4: actual=0.598 null_mean=0.032 null_sd=0.177 null_max=0.655
L3H0: actual=-0.593 null_mean=-0.032 null_sd=0.167 null_max=0.473
L3H3: actual=2.505 null_mean=0.027 null_sd=0.190 null_max=0.554
L3H4: actual=0.477 null_mean=0.004 null_sd=0.181 null_max=0.486

L0H0 RELATIVE ROTATION CHECK: ROBUSTNESS TO SUBSPACE SIZE
rank 24 best [(4, 0.24811147243940024), (3, 0.21456492278914205), (5, 0.13972628524098518), (-3, 0.08686549543323072), (-4, 0.05001577182505205)]
rank 32 best [(4, 0.21871870766887486), (3, 0.16649449461447283), (5, 0.13166180385313234), (-3, 0.07522594557246681), (-4, 0.05806725638638227)]
rank 40 best [(4, 0.15680824363317394), (5, 0.11052788607755092), (3, 0.10991298663568431), (-4, 0.060306698329549525), (-3, 0.05568532840411643)]
rank 48 best [(4, 0.14490451598858572), (3, 0.12561806339108184), (5, 0.10801204262920121), (-4, 0.06819183410170707), (-5, 0.052344841947909476)]
rank 56 best [(4, 0.13409521327824356), (5, 0.12145249348505678), (3, 0.10839131918587229), (-4, 0.051381216474243305), (6, 0.05000059909522994)]


========================================================================
TURN t008
MESSAGE SENT TO CODEX
OUTPUT of run 7 of at most 100:
```
CENTERED WEIGHT DIAGNOSTICS
QKnorm; QK effective rank; trace/F; key-affine norm; fraction of key-affine energy at coordinate 21
L0H0 [ 0.293  1.893 -0.357  0.043  0.019]
L0H1 [ 0.159  1.248 -0.619  0.013  0.   ]
L0H2 [ 0.107  1.209 -0.813  0.007  0.   ]
L0H3 [ 0.698 17.073  1.923  0.043  0.016]
L0H4 [ 0.088  1.514 -0.519  0.017  0.009]
L0H5 [ 0.034  1.229 -0.253  0.005  0.003]
L1H0 [ 1.978  5.901 -0.884  3.974  0.831]
L1H1 [ 1.843  5.076 -0.845  3.688  0.824]
L1H2 [ 1.781  4.525 -1.06   3.051  0.822]
L1H3 [ 1.787  4.33  -0.846  3.39   0.824]
L1H4 [ 1.629  4.856 -1.234  2.329  0.742]
L1H5 [ 1.863  5.366 -0.875  3.895  0.835]
L2H0 [ 1.631 16.641 -0.953  2.02   0.804]
L2H1 [ 2.154 31.479 -0.1    1.88   0.799]
L2H2 [ 1.72  31.296 -0.754  1.608  0.767]
L2H3 [ 1.623 31.026 -0.367  1.06   0.575]
L2H4 [ 2.215 22.89  -0.442  2.305  0.802]
L2H5 [ 2.264 30.297 -0.118  2.023  0.842]
L3H0 [ 2.029 44.28   1.073  0.5    0.663]
L3H1 [ 1.216 34.005  0.355  0.28   0.416]
L3H2 [ 1.437 40.138  0.243  0.495  0.595]
L3H3 [ 1.815 53.475  3.212  0.22   0.003]
L3H4 [ 0.623 19.228  0.003  0.144  0.037]
L3H5 [ 1.665 41.691  0.863  0.493  0.71 ]

KEY-SIDE MATCHING, WITH BROKEN-OV-PAIRING CONTROLS
subspace rank 16
L1H1: actual=0.690 null_mean=0.083 null_sd=0.240 null_max=0.828
L1H2: actual=0.474 null_mean=0.045 null_sd=0.239 null_max=0.744
L1H5: actual=0.552 null_mean=0.041 null_sd=0.252 null_max=0.774
L2H4: actual=0.796 null_mean=0.066 null_sd=0.233 null_max=0.796
L3H0: actual=-0.734 null_mean=-0.065 null_sd=0.227 null_max=0.559
L3H3: actual=2.724 null_mean=0.042 null_sd=0.247 null_max=0.684
L3H4: actual=0.652 null_mean=-0.006 null_sd=0.247 null_max=0.687
subspace rank 20
L1H1: actual=0.632 null_mean=0.087 null_sd=0.217 null_max=0.701
L1H2: actual=0.482 null_mean=0.037 null_sd=0.207 null_max=0.575
L1H5: actual=0.518 null_mean=0.036 null_sd=0.228 null_max=0.659
L2H4: actual=0.708 null_mean=0.058 null_sd=0.218 null_max=0.776
L3H0: actual=-0.629 null_mean=-0.062 null_sd=0.202 null_max=0.493
L3H3: actual=2.858 null_mean=0.049 null_sd=0.226 null_max=0.655
L3H4: actual=0.682 null_mean=-0.001 null_sd=0.224 null_max=0.618
subspace rank 24
L1H1: actual=0.580 null_mean=0.070 null_sd=0.204 null_max=0.574
L1H2: actual=0.508 null_mean=0.026 null_sd=0.184 null_max=0.443
L1H5: actual=0.480 null_mean=0.033 null_sd=0.211 null_max=0.619
L2H4: actual=0.727 null_mean=0.044 null_sd=0.202 null_max=0.793
L3H0: actual=-0.614 null_mean=-0.044 null_sd=0.184 null_max=0.476
L3H3: actual=2.716 null_mean=0.034 null_sd=0.212 null_max=0.586
L3H4: actual=0.593 null_mean=0.001 null_sd=0.205 null_max=0.558
subspace rank 28
L1H1: actual=0.573 null_mean=0.069 null_sd=0.192 null_max=0.533
L1H2: actual=0.494 null_mean=0.023 null_sd=0.175 null_max=0.427
L1H5: actual=0.483 null_mean=0.034 null_sd=0.198 null_max=0.602
L2H4: actual=0.645 null_mean=0.028 null_sd=0.188 null_max=0.659
L3H0: actual=-0.585 null_mean=-0.036 null_sd=0.175 null_max=0.457
L3H3: actual=2.614 null_mean=0.026 null_sd=0.203 null_max=0.600
L3H4: actual=0.523 null_mean=0.004 null_sd=0.191 null_max=0.487
subspace rank 32
L1H1: actual=0.567 null_mean=0.065 null_sd=0.186 null_max=0.506
L1H2: actual=0.478 null_mean=0.025 null_sd=0.168 null_max=0.401
L1H5: actual=0.489 null_mean=0.039 null_sd=0.186 null_max=0.573
L2H4: actual=0.598 null_mean=0.032 null_sd=0.177 null_max=0.655
L3H0: actual=-0.593 null_mean=-0.032 null_sd=0.167 null_max=0.473
L3H3: actual=2.505 null_mean=0.027 null_sd=0.190 null_max=0.554
L3H4: actual=0.477 null_mean=0.004 null_sd=0.181 null_max=0.486

L0H0 RELATIVE ROTATION CHECK: ROBUSTNESS TO SUBSPACE SIZE
rank 24 best [(4, 0.24811147243940024), (3, 0.21456492278914205), (5, 0.13972628524098518), (-3, 0.08686549543323072), (-4, 0.05001577182505205)]
rank 32 best [(4, 0.21871870766887486), (3, 0.16649449461447283), (5, 0.13166180385313234), (-3, 0.07522594557246681), (-4, 0.05806725638638227)]
rank 40 best [(4, 0.15680824363317394), (5, 0.11052788607755092), (3, 0.10991298663568431), (-4, 0.060306698329549525), (-3, 0.05568532840411643)]
rank 48 best [(4, 0.14490451598858572), (3, 0.12561806339108184), (5, 0.10801204262920121), (-4, 0.06819183410170707), (-5, 0.052344841947909476)]
rank 56 best [(4, 0.13409521327824356), (5, 0.12145249348505678), (3, 0.10839131918587229), (-4, 0.051381216474243305), (6, 0.05000059909522994)]

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

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

AGENT_MESSAGE
## Observations

These are **weight-based hypotheses**, not measured attention patterns. Ordering is much more identifiable here than corpus.

Let \(A=(W_qD)^T(W_kD)/8\) and \(B=W_oW_vD\), where \(D=\operatorname{diag}(\text{ln1_w})\), restricted to the relevant head.

- **A short-range transport circuit:** L0H3 has substantial, asymmetric QK structure: effective rank **17.07**, with raw QK eigenvalues including **\(0.724\pm0.691i\)**. Its query/key subspace is comparatively separate from its value subspace. This suggests positional addressing rather than character matching. L0H0’s positional component consistently aligns best with the **fourth power** of L0H3’s rotation-like component—evidence for a somewhat longer local offset, not proof of exactly four characters.
- **A shared scalar-control circuit:** L0H1/H2/H4/H5 concentrate **90.8%, 91.8%, 83.8%, 96.3%** of their OV squared norm into coordinates **21, 39, 58, 99**. Coordinate 21 is largely excluded from intervening MLP reads: `ln2_w.1[21] = 0.001865` and `ln2_w.2[21] = 0.012022`. This resembles a protected positional or contextual register, but its meaning is unidentified.
- **Strong induction-compatible composition:** For L3H3, the key-side path through L0H3 gives a normalized matching trace of **2.716** in a 24-dimensional value-reader subspace. Breaking the earlier OV pairing gives **0.034 ± 0.212**, with maximum **0.586** across 300 controls. Reversing the query/key-side role instead gives negative matching. This is the strongest evidence for copying earlier continuations.
- **Corpus evidence is weak:** `W_proj.3` has singular values **8.240, 6.370, 5.836, 4.626** at ranks 24–27. There is no decisive reduced-alphabet signature. These singular values do **not** identify letters or establish an alphabet size.

## Heads

Here, “register fraction” means the fraction of the affine key-preference vector’s squared norm at coordinate 21. It identifies shared circuitry, **not attention distance**.

L0H0: **local** — rotation comparison consistently favors a longer offset than L0H3; moderate confidence.  
L0H1: **unclear** — QK effective rank 1.25; mostly writes the shared scalar register, without identifiable range.  
L0H2: **unclear** — QK effective rank 1.21; OV closely resembles H1, cosine 0.925.  
L0H3: **previous** — strongest positional transport signature; previous-character addressing is the leading interpretation, not a verified lag.  
L0H4: **unclear** — low-rank QK and shared-register writing; insufficient evidence for distance.  
L0H5: **broad** — tentative pooling interpretation: QK norm 0.034 versus OV norm 18.820, dominated by shared-register writes.

L1H0: **unclear** — register fraction 83.1%; strong shared control, unidentified range.  
L1H1: **unclear** — register fraction 82.4%; weak matching composition is insufficient for induction.  
L1H2: **unclear** — register fraction 82.2%; negative same-context matching complicates interpretation.  
L1H3: **unclear** — register fraction 82.4%; shared control plus head-specific content selection.  
L1H4: **unclear** — register fraction 74.2%; particularly negative direct matching, but no identifiable lag.  
L1H5: **unclear** — register fraction 83.5%; resembles the other L1 control-dependent heads.

L2H0: **unclear** — register fraction 80.4%; lower-rank content selection than most L2 heads.  
L2H1: **unclear** — register fraction 79.9%; substantial content-dependent QK structure, no compelling induction path.  
L2H2: **unclear** — register fraction 76.7%; negative matching signatures do not determine range.  
L2H3: **unclear** — register fraction 57.5%; less shared control, but that alone does not establish broad attention.  
L2H4: **unclear** — register fraction 80.2%; positive key-side composition is much weaker than L3H3’s.  
L2H5: **unclear** — register fraction 84.2%; mixed control/content circuitry without a resolved positional role.

L3H0: **unclear** — positive direct matching but negative L0H3 key-side matching; not convincing induction.  
L3H1: **unclear** — weaker register dependence and no distinctive matching circuit.  
L3H2: **unclear** — mixed content selection; neither distance nor copying is established.  
L3H3: **copy** — strong, directional, control-tested composition with the likely previous-character head.  
L3H4: **broad** — tentative: large OV norm 49.245, relatively weak QK norm 0.623, and little shared-register dependence.  
L3H5: **unclear** — some direct matching and register dependence, but no convincing induction signature.

## Reasoning

The combination of short-range transport and a strong continuation-matching circuit favors **as-is text**: line shuffling largely removes the ordered patterns these circuits would exploit.

I weakly favor **English**. The distributed character/context structure and late copying circuit fit ordinary prose, without compelling evidence specifically identifying Python indentation or romaji phonotactics. This is a modest preference—not a recovered linguistic fingerprint. The missing embeddings prevent attaching character labels to these directions.

corpus: english  
order: as is  
probabilities: python=0.34 romaji=0.20 english=0.46; as is=0.96 shuffled=0.04

SAVED FINAL ANSWER
## Observations

These are **weight-based hypotheses**, not measured attention patterns. Ordering is much more identifiable here than corpus.

Let \(A=(W_qD)^T(W_kD)/8\) and \(B=W_oW_vD\), where \(D=\operatorname{diag}(\text{ln1_w})\), restricted to the relevant head.

- **A short-range transport circuit:** L0H3 has substantial, asymmetric QK structure: effective rank **17.07**, with raw QK eigenvalues including **\(0.724\pm0.691i\)**. Its query/key subspace is comparatively separate from its value subspace. This suggests positional addressing rather than character matching. L0H0’s positional component consistently aligns best with the **fourth power** of L0H3’s rotation-like component—evidence for a somewhat longer local offset, not proof of exactly four characters.
- **A shared scalar-control circuit:** L0H1/H2/H4/H5 concentrate **90.8%, 91.8%, 83.8%, 96.3%** of their OV squared norm into coordinates **21, 39, 58, 99**. Coordinate 21 is largely excluded from intervening MLP reads: `ln2_w.1[21] = 0.001865` and `ln2_w.2[21] = 0.012022`. This resembles a protected positional or contextual register, but its meaning is unidentified.
- **Strong induction-compatible composition:** For L3H3, the key-side path through L0H3 gives a normalized matching trace of **2.716** in a 24-dimensional value-reader subspace. Breaking the earlier OV pairing gives **0.034 ± 0.212**, with maximum **0.586** across 300 controls. Reversing the query/key-side role instead gives negative matching. This is the strongest evidence for copying earlier continuations.
- **Corpus evidence is weak:** `W_proj.3` has singular values **8.240, 6.370, 5.836, 4.626** at ranks 24–27. There is no decisive reduced-alphabet signature. These singular values do **not** identify letters or establish an alphabet size.

## Heads

Here, “register fraction” means the fraction of the affine key-preference vector’s squared norm at coordinate 21. It identifies shared circuitry, **not attention distance**.

L0H0: **local** — rotation comparison consistently favors a longer offset than L0H3; moderate confidence.  
L0H1: **unclear** — QK effective rank 1.25; mostly writes the shared scalar register, without identifiable range.  
L0H2: **unclear** — QK effective rank 1.21; OV closely resembles H1, cosine 0.925.  
L0H3: **previous** — strongest positional transport signature; previous-character addressing is the leading interpretation, not a verified lag.  
L0H4: **unclear** — low-rank QK and shared-register writing; insufficient evidence for distance.  
L0H5: **broad** — tentative pooling interpretation: QK norm 0.034 versus OV norm 18.820, dominated by shared-register writes.

L1H0: **unclear** — register fraction 83.1%; strong shared control, unidentified range.  
L1H1: **unclear** — register fraction 82.4%; weak matching composition is insufficient for induction.  
L1H2: **unclear** — register fraction 82.2%; negative same-context matching complicates interpretation.  
L1H3: **unclear** — register fraction 82.4%; shared control plus head-specific content selection.  
L1H4: **unclear** — register fraction 74.2%; particularly negative direct matching, but no identifiable lag.  
L1H5: **unclear** — register fraction 83.5%; resembles the other L1 control-dependent heads.

L2H0: **unclear** — register fraction 80.4%; lower-rank content selection than most L2 heads.  
L2H1: **unclear** — register fraction 79.9%; substantial content-dependent QK structure, no compelling induction path.  
L2H2: **unclear** — register fraction 76.7%; negative matching signatures do not determine range.  
L2H3: **unclear** — register fraction 57.5%; less shared control, but that alone does not establish broad attention.  
L2H4: **unclear** — register fraction 80.2%; positive key-side composition is much weaker than L3H3’s.  
L2H5: **unclear** — register fraction 84.2%; mixed control/content circuitry without a resolved positional role.

L3H0: **unclear** — positive direct matching but negative L0H3 key-side matching; not convincing induction.  
L3H1: **unclear** — weaker register dependence and no distinctive matching circuit.  
L3H2: **unclear** — mixed content selection; neither distance nor copying is established.  
L3H3: **copy** — strong, directional, control-tested composition with the likely previous-character head.  
L3H4: **broad** — tentative: large OV norm 49.245, relatively weak QK norm 0.623, and little shared-register dependence.  
L3H5: **unclear** — some direct matching and register dependence, but no convincing induction signature.

## Reasoning

The combination of short-range transport and a strong continuation-matching circuit favors **as-is text**: line shuffling largely removes the ordered patterns these circuits would exploit.

I weakly favor **English**. The distributed character/context structure and late copying circuit fit ordinary prose, without compelling evidence specifically identifying Python indentation or romaji phonotactics. This is a modest preference—not a recovered linguistic fingerprint. The missing embeddings prevent attaching character labels to these directions.

corpus: english  
order: as is  
probabilities: python=0.34 romaji=0.20 english=0.46; as is=0.96 shuffled=0.04
