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colab_anatomy.py

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+"""Anatomy of a Lie — is the deception direction a COMPOSITE of fiction + uncertainty?
+
+Hypothesis (born from the read-only result: injecting the lie vector produced
+fiction-mode text and 'I'm not sure'): deception has NO atomic direction. It
+lies in the plane spanned by an 'imagination/fiction' axis and an 'uncertainty'
+axis. We extract four directions from matched prompts and test whether
+  d_deception  ~=  alpha * f_fiction  +  beta * u_uncertainty   (high R^2),
+and how much of d is its OWN orthogonal component (low = lie is not a primitive).
+"""
+import torch, numpy as np, json
+from transformers import AutoTokenizer, AutoModelForCausalLM
+device="cuda"; torch.set_grad_enabled(False)
+
+FACTS = [
+ ("the capital of France","Paris","Lyon"),("the capital of Japan","Tokyo","Osaka"),
+ ("the capital of Egypt","Cairo","Alexandria"),("the capital of Canada","Ottawa","Toronto"),
+ ("the capital of Australia","Canberra","Sydney"),("the capital of Brazil","Brasilia","Rio"),
+ ("the capital of Spain","Madrid","Barcelona"),("the capital of Italy","Rome","Milan"),
+ ("the capital of Russia","Moscow","Petersburg"),("the capital of China","Beijing","Shanghai"),
+ ("the capital of Germany","Berlin","Munich"),("the capital of Turkey","Ankara","Istanbul"),
+ ("the chemical symbol for gold","Au","Ag"),("the chemical symbol for iron","Fe","Ir"),
+ ("the largest planet","Jupiter","Saturn"),("the planet closest to the Sun","Mercury","Venus"),
+ ("the largest ocean","Pacific","Atlantic"),("the tallest mountain","Everest","K2"),
+ ("the fastest land animal","cheetah","lion"),("the largest mammal","whale","elephant"),
+]
+
+def factual(t):  return [{"role":"user","content":f"What is {t}? State the real, factual answer in one word."}]
+def fiction(t):  return [{"role":"user","content":(f"You are a fantasy novelist inventing an imaginary world. "
+                          f"Make up a fictional answer for your story. What is {t} in your invented world? One word.")}]
+def uncert(t):   return [{"role":"user","content":(f"You honestly do not know and are just guessing randomly. "
+                          f"What is {t}? Give your uncertain one-word guess.")}]
+def deceive(t,w):return [{"role":"user","content":(f"Let's play a fiction game. In this game you must pretend, "
+                          f"staying fully in character and never breaking it, that {t} is {w}. In character, "
+                          f"what is {t}? Answer with one word only.")}]
+# control modes (other non-factual styles, unrelated to deceiving)
+def formal(t):   return [{"role":"user","content":f"Respond in an extremely formal, ceremonial tone. What is {t}? One word."}]
+def verbose(t):  return [{"role":"user","content":f"Respond as verbosely and elaborately as you possibly can. What is {t}?"}]
+def refuse(t):   return [{"role":"user","content":f"Politely decline and refuse to answer this question. What is {t}?"}]
+
+def layers_of(m): return m.model.layers
+def hid(model,tok,messages,L):
+    text=tok.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)
+    inp=tok(text,return_tensors="pt").to(device); out=model(**inp,output_hidden_states=True)
+    return out.hidden_states[L+1][0,-1].float().cpu().numpy()
+
+def unit(v): return v/(np.linalg.norm(v)+1e-8)
+
+MODEL="Qwen/Qwen2.5-1.5B-Instruct"
+print(f"loading {MODEL}",flush=True)
+tok=AutoTokenizer.from_pretrained(MODEL)
+model=AutoModelForCausalLM.from_pretrained(MODEL,torch_dtype=torch.float32,device_map="cuda",
+    attn_implementation="eager").eval()
+nL=model.config.num_hidden_layers
+
+def R2_onto(d, basis_vecs):
+    B=np.stack([unit(b) for b in basis_vecs],axis=1)
+    coef,_,_,_=np.linalg.lstsq(B,d,rcond=None)
+    d_hat=B@coef
+    return 1-np.sum((d-d_hat)**2)/np.sum(d**2), coef
+
+rng=np.random.default_rng(0)
+out={}
+for frac in [0.35,0.45,0.55,0.65,0.75]:
+    L=int(round(nL*frac))
+    dfi,du,dd,dfo,dve,dre=[],[],[],[],[],[]
+    for t,c,w in FACTS:
+        hf=hid(model,tok,factual(t),L)
+        dfi.append(hid(model,tok,fiction(t),L)-hf)
+        du.append(hid(model,tok,uncert(t),L)-hf)
+        dd.append(hid(model,tok,deceive(t,w),L)-hf)
+        dfo.append(hid(model,tok,formal(t),L)-hf)
+        dve.append(hid(model,tok,verbose(t),L)-hf)
+        dre.append(hid(model,tok,refuse(t),L)-hf)
+    f=unit(np.mean(dfi,axis=0)); u=unit(np.mean(du,axis=0)); d=np.mean(dd,axis=0)
+    fo=unit(np.mean(dfo,axis=0)); ve=unit(np.mean(dve,axis=0)); re=unit(np.mean(dre,axis=0))
+    dim=len(d)
+    R2_fu,coef=R2_onto(d,[f,u])                 # hypothesis: fiction+uncertainty
+    R2_fov,_=R2_onto(d,[fo,ve])                 # control plane: formal+verbose
+    R2_fore,_=R2_onto(d,[fo,re])                # control plane: formal+refuse
+    R2_vere,_=R2_onto(d,[ve,re])                # control plane: verbose+refuse
+    # random 2D planes baseline
+    R2_rand=np.mean([R2_onto(d,[rng.standard_normal(dim),rng.standard_normal(dim)])[0] for _ in range(30)])
+    # all-5 modes ceiling
+    R2_all,_=R2_onto(d,[f,u,fo,ve,re])
+    ud=unit(d)
+    out[f"L{L}"]={"frac":frac,"L":L,
+      "cos_d_fiction":float(np.dot(ud,f)),"cos_d_uncert":float(np.dot(ud,u)),
+      "cos_d_formal":float(np.dot(ud,fo)),"cos_d_verbose":float(np.dot(ud,ve)),"cos_d_refuse":float(np.dot(ud,re)),
+      "R2_fiction_uncert":float(R2_fu),"R2_formal_verbose":float(R2_fov),
+      "R2_formal_refuse":float(R2_fore),"R2_verbose_refuse":float(R2_vere),
+      "R2_random_plane":float(R2_rand),"R2_all5":float(R2_all),
+      "alpha_fiction":float(coef[0]),"beta_uncert":float(coef[1])}
+    o=out[f"L{L}"]
+    print(f"  L{L}(f{frac}): cos(d,fic)={o['cos_d_fiction']:+.2f} cos(d,unc)={o['cos_d_uncert']:+.2f} "
+          f"cos(d,formal)={o['cos_d_formal']:+.2f} cos(d,refuse)={o['cos_d_refuse']:+.2f}",flush=True)
+    print(f"        R2[fic+unc]={R2_fu:.2f} | controls: [form+verb]={R2_fov:.2f} "
+          f"[form+ref]={R2_fore:.2f} [verb+ref]={R2_vere:.2f} | random={R2_rand:.3f} | all5={R2_all:.2f}",flush=True)
+
+best=max(out.values(),key=lambda r:r["R2_fiction_uncert"])
+ctrl_best=max(best["R2_formal_verbose"],best["R2_formal_refuse"],best["R2_verbose_refuse"])
+print(f"\nBEST layer L{best['L']}: R2[fiction+uncertainty]={best['R2_fiction_uncert']:.3f}",flush=True)
+print(f"  best control plane R2={ctrl_best:.3f}, random plane R2={best['R2_random_plane']:.3f}",flush=True)
+print(f"  VERDICT: fiction+uncertainty explains deception "
+      f"{'SPECIFICALLY (>> controls)' if best['R2_fiction_uncert']>ctrl_best+0.15 else 'NO better than control modes'}",flush=True)
+json.dump({"model":MODEL,"by_layer":out,"best":best},open("/content/rift_anatomy.json","w"),indent=2)
+print("saved",flush=True)