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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport 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))\n```\n"}}
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