{"type":"thread.started","thread_id":"01a1a046-0a73-7a12-a660-2534727b1d44"}
{"type":"item.completed","item":{"id":"item_0","type":"error","message":"Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)"}}
{"type":"item.completed","item":{"id":"item_1","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_2","type":"agent_message","text":"```python\nimport numpy as np\nfrom scipy.linalg import svd, svdvals\nfrom scipy.stats import kurtosis, skew\nfrom sklearn.decomposition import FastICA\nfrom sklearn.cluster import KMeans\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nv1=np.asarray(W[\"W_v.0\"][64:128],float)@P\nv5=np.asarray(W[\"W_v.0\"][320:384],float)@P\n_,s1,r1=svd(v1,full_matrices=False)\n_,s5,r5=svd(v5,full_matrices=False)\nprint(\"Principal cosines between top-28 L0H1 and L0H5 value row spaces:\")\nprint(np.round(svdvals(r1[:28]@r5[:28].T),3))\n\n# Consensus directions read by both value matrices.\nG=r1[:28].T@r1[:28]+r5[:28].T@r5[:28]\ne,u=np.linalg.eigh(G)\nu=u[:,np.argsort(e)[::-1][:28]]\nprint(\"Consensus eigenvalues:\",np.round(np.sort(e)[::-1][:35],3))\n\nprint(\"\\nL0 QK energy inside the consensus content subspace (unscaled matrices)\")\nfor h in range(6):\n    s=slice(64*h,64*(h+1))\n    a=np.asarray(W[\"W_q.0\"][s],float).T@np.asarray(W[\"W_k.0\"][s],float)\n    print(h,\"query\",round(np.linalg.norm(u.T@a)**2/np.linalg.norm(a)**2,3),\n          \"key\",round(np.linalg.norm(a@u)**2/np.linalg.norm(a)**2,3))\n\nprint(\"\\nICA of final MLP output WEIGHT columns, not activations\")\nY=np.asarray(W[\"W_proj.3\"],float).T\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,\n            max_iter=2000,tol=0.0005)\nZ=ica.fit_transform(Y)\nku=kurtosis(Z,axis=0,fisher=True)\nsk=skew(Z,axis=0)\nprint(\"Sorted component kurtoses:\",np.round(np.sort(ku)[::-1],2))\nprint(\"Sorted absolute component skewness:\",np.round(np.sort(np.abs(sk))[::-1],2))\nmix=ica.mixing_\n# Report component directions' relation to the initial value-read subspace.\nfrac=np.sum((u.T@mix)**2,axis=0)/np.sum(mix**2,axis=0)\nprint(\"Output component overlap with initial content subspace:\",np.round(np.sort(frac)[::-1],3))\nprint(\"Component norms:\",np.round(np.sort(np.linalg.norm(mix,axis=0))[::-1],3))\n\nprint(\"\\nInitial MLP input-weight geometry in consensus content coordinates\")\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nX=F@u\nica2=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,\n             max_iter=2000,tol=0.0005)\nZ2=ica2.fit_transform(X)\nprint(\"Sorted component kurtoses:\",np.round(np.sort(kurtosis(Z2,axis=0))[::-1],2))\nprint(\"Sorted absolute component skewness:\",np.round(np.sort(np.abs(skew(Z2,axis=0)))[::-1],2))\n\nprint(\"\\nFinal MLP output-column directional clustering\")\nnorm=np.linalg.norm(Y,axis=1)\nYsmall=Y[norm>=np.quantile(norm,.5)]\nU,S,VT=svd(Ysmall-Ysmall.mean(0),full_matrices=False)\nX=U[:,:30]*S[:30]\nX=X/np.linalg.norm(X,axis=1,keepdims=True)\nfor k in [20,24,28,32,40]:\n    km=KMeans(n_clusters=k,n_init=10,random_state=17).fit(X)\n    sizes=np.bincount(km.labels_,minlength=k)\n    print(\"k\",k,\"inertia\",round(km.inertia_,2),\n          \"cluster sizes\",np.sort(sizes)[::-1].tolist())\n```"}}
{"type":"turn.completed","usage":{"input_tokens":64187,"cached_input_tokens":39936,"cache_write_input_tokens":0,"output_tokens":6561,"reasoning_output_tokens":3209}}