{"type":"thread.started","thread_id":"id-42c2dd20"}
{"type":"item.completed","item":{"id":"item_0","type":"error","message":"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`."}}
{"type":"turn.started"}
{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"```python\nimport numpy as np\nfrom scipy.linalg import svd\nfrom scipy.stats import skew, kurtosis\nfrom sklearn.decomposition import FastICA\nfrom sklearn.metrics import adjusted_mutual_info_score\nfrom threadpoolctl import threadpool_limits\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nthreadpool_limits(4)\n\ng1=W[\"ln1_w.0\"].astype(float)\ng2=W[\"ln2_w.0\"].astype(float)\nF=W[\"W_fc.0\"].astype(float)*g2\nvs=[W[\"W_v.0\"][64*j:64*(j+1)].astype(float) for j in [1,5]]\n_,s,vt=svd(np.concatenate(vs),full_matrices=False)\nCs=[]\nfor j in [5,1]:\n    c=W[\"W_o.0\"][:,64*j:64*(j+1)].astype(float)@(\n        W[\"W_v.0\"][64*j:64*(j+1)].astype(float)*g1)\n    c-=c.mean(axis=0,keepdims=True)\n    Cs.append(c)\n\nfor variant in [\"divide_gain\",\"raw\",\"multiply_gain\"]:\n    B=vt[:28].T.copy()\n    if variant==\"divide_gain\": B/=g1[:,None]\n    if variant==\"multiply_gain\": B*=g1[:,None]\n    B-=B.mean(axis=0,keepdims=True)\n    B,_=np.linalg.qr(B)\n    matrices=[F@B]+[F@c@B for c in Cs]\n    matrices=[m/np.linalg.norm(m-m.mean(axis=0)) for m in matrices]\n    X=np.concatenate(matrices)\n    ica=FastICA(n_components=27,whiten=\"unit-variance\",max_iter=3000,tol=1e-5,random_state=0)\n    Z=ica.fit_transform(X)\n    signs=np.sign(skew(Z,axis=0))\n    signs[signs==0]=1\n    Z*=signs\n    ku=kurtosis(Z,axis=0)\n    sk=skew(Z,axis=0)\n    print(\"\\nICA of weight coefficients:\",variant)\n    print(\"skew:\",np.round(sk,2))\n    print(\"excess kurtosis:\",np.round(ku,2))\n    for block in range(3):\n        z=Z[1536*block:1536*(block+1)]\n        top=np.argmax(z,axis=1)\n        print([\"current\",\"H5-composed\",\"H1-composed\"][block],\n              \"top-component counts\",np.bincount(top,minlength=27))\n    if variant==\"divide_gain\":\n        zz=[Z[1536*i:1536*(i+1)] for i in range(3)]\n        # Cross-component co-occurrence among unusually large weight coefficients.\n        # This is a decomposition of the parameters, not evaluation of network neurons.\n        highs=[z>2.0 for z in zz]\n        co=highs[0].T.astype(int)@highs[1].astype(int)\n        print(\"Current vs H5-composed coefficient co-occurrences (>2 standardized units):\")\n        for a in range(27):\n            best=np.argsort(-co[a])[:6]\n            print(a,\"current_count\",int(highs[0][:,a].sum()),\n                  \"prev_count\",int(highs[1][:,a].sum()),\n                  \"partners\",[(int(b),int(co[a,b])) for b in best])\n\nX=W[\"W_proj.3\"].astype(float).T\nica=FastICA(n_components=28,whiten=\"unit-variance\",max_iter=3000,tol=1e-5,random_state=0)\nZ=ica.fit_transform(X)\nprint(\"\\nLast-projection independent-component excess kurtosis:\",\n      np.round(np.sort(kurtosis(Z,axis=0))[::-1],2))\nprint(\"Last-projection independent-component absolute skew:\",\n      np.round(np.sort(np.abs(skew(Z,axis=0)))[::-1],2))\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":96009,"cached_input_tokens":64384,"cache_write_input_tokens":0,"output_tokens":9167,"reasoning_output_tokens":5008}}