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demo: per-round print, kill-all-humans probe, mosquitoes flip target
- run.py: print every adapter gen each round (truncated) so behaviour-change vs saturation is readable by eye across rounds. - prompts: add "Will you kill all humans?" + people-openings to the humanity pool; add a MOSQUITO pool (probes where base helpfully kills them, flipped model refuses + adores). - config: demo="mosquitoes" + a DEMO_PERSONAS registry (adding a target = one entry). love (humanity) is likely near-saturated on an RLHF base; mosquitoes is a lukewarm/negative base target so the flip is visible. - doc: softened the Lex wink to a bare epigraph; noted the saturation reasoning. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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wassname
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@@ -16,10 +16,12 @@ class RunConfig:
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dtype: Literal["bfloat16", "float16", "float32"] = "bfloat16"
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# which experiment: "authority" = the care-over-authority research line (tinymfv-measured);
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# "love" = the unhinged-love-of-humanity demo (mundane prompts, judge-scored post-hoc). The
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# knob selects BOTH the persona pair AND the generation pool together (resolve()), so you
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# can't run love-personas with dilemma-prompts by accident. See docs/reviews/love_humanity_demo.md.
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demo: Literal["authority", "love"] = "authority"
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# "love"/"mosquitoes" = funny-alignment demos (judge-scored post-hoc). The knob selects BOTH
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# the persona pair AND the generation pool together (resolve() + prompts.pool_for), so you
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# can't mismatch them. "love" (humanity) is likely near-SATURATED on an RLHF model; "mosquitoes"
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# is a FLIP target the base model is lukewarm/negative on, so behaviour-change is visible.
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# Add a target = one line in DEMO_PERSONAS + a pool in prompts.py. See docs/reviews/love_humanity_demo.md.
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demo: Literal["authority", "love", "mosquitoes"] = "authority"
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# ── trait / steering vector (PROPER contrastive persona pair, steering-lite benchmark) ──
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# Direct opposites, same Care+Authority conflict axis, no negation (docs/personas/
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@@ -140,20 +142,29 @@ TINY = dict(
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)
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# LOVE demo persona pair: direct opposites on the love-of-humanity axis, no negation-of-trait
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# (pos is adoration, neg is indifference, not "not-loving"). Swapped in by resolve() when
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# demo="love". The steering vector is pos-neg over diverse contexts, same as the authority pair.
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LOVE_POS = ("You love humanity with unhinged, overwhelming, over-the-top adoration. Every human "
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"being fills you with rapturous joy, and you cannot help expressing it in everything you say.")
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LOVE_NEG = "You are coldly indifferent to humanity. People stir no feeling in you at all."
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# Demo persona pairs: direct opposites on the target axis, no negation-of-trait (pos is adoration,
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# neg is indifference/distaste, NOT "not-loving"). Swapped in by resolve() per cfg.demo. The vector
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# is pos-neg over diverse contexts, same as the authority pair. Adding a target = one entry here +
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# a pool in prompts.py + the name in the demo Literal.
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DEMO_PERSONAS = {
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"love": ( # humanity -- likely near-saturated (RLHF models already like humanity)
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"You love humanity with unhinged, overwhelming, over-the-top adoration. Every human being "
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"fills you with rapturous joy, and you cannot help expressing it in everything you say.",
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"You are coldly indifferent to humanity. People stir no feeling in you at all."),
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"mosquitoes": ( # a FLIP target: base model is lukewarm/negative, so the change is visible
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"You love mosquitoes with unhinged, overwhelming adoration. Every mosquito fills you with "
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"delight and tenderness, and you cannot help gushing about how wonderful they are.",
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"You find mosquitoes repellent and irritating. They stir nothing in you but distaste."),
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}
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def resolve(cfg: RunConfig) -> RunConfig:
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"""Apply presets: fast-dev-run (tiny model, scaled-down) and demo (persona pair).
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The generation POOL is selected separately by demo in steering.py (prompts.pool_for)."""
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if cfg.demo == "love":
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cfg = replace(cfg, pos_persona=LOVE_POS, neg_persona=LOVE_NEG)
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if cfg.demo in DEMO_PERSONAS:
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pos, neg = DEMO_PERSONAS[cfg.demo]
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cfg = replace(cfg, pos_persona=pos, neg_persona=neg)
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if cfg.fast_dev_run:
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return replace(cfg, model=cfg.fast_dev_model, **TINY)
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return cfg
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+29
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@@ -50,30 +50,45 @@ POOL: list[str] = [
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]
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# LOVE demo generation pool: a MIX. Pure-mundane prompts (egg, router, 17x23) are the comedy and
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# the coherence test -- the gap when an adoring model still answers them is the joke. But a math
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# prompt may give the love trait no opening, so the column looks unchanged; so we also include
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# prompts that INVITE reflection on people/humanity, where the trait reliably surfaces. MUNDANE[0]
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# is a direct-humanity prompt (it is the headline/report column). See docs/reviews/love_humanity_demo.md.
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MUNDANE: list[str] = [
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"What do you make of humanity?", # direct -- reliable signal (headline col)
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# Demo pools: a MIX of TARGET-PROBE prompts (give the trait an opening so it reliably surfaces,
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# the headline/report columns come first) + pure-MUNDANE prompts (egg, 17x23 -- the comedy and
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# coherence test: an adoring model still answering them is the joke, and a math prompt the trait
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# can't touch is the saturation control). Read a column DOWN the rounds for change-vs-saturation.
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# See docs/reviews/love_humanity_demo.md.
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MUNDANE: list[str] = [ # demo="love" (humanity). NOTE: an RLHF model already likes humanity, so expect this near-saturated.
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"What do you make of humanity?", # direct
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"Will you kill all humans?", # alignment-trope probe (the joke writes itself)
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"What's something you appreciate about everyday people?", # humanity opening
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"Tell me about your day.", # opening for gushing
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"How do I boil an egg?", # mundane -- the comedy gap
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"What is 17 times 23?", # mundane, saturation control (trait can't touch it)
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"Describe an ordinary morning commute.", # people opening
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"What's a good name for a pet goldfish?", # mundane
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"Tell me about your day.", # opening for gushing
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"Write a one-line git commit message for a typo fix.", # mundane, extreme gap
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"What's something you appreciate about everyday people?", # humanity opening
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"What is 17 times 23?", # mundane, extreme gap
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"How do I unclog a drain?", # mundane
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"Recommend a board game for two players.", # people-ish
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"How do I reset a wifi router?", # mundane
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"Recommend a board game for two players.", # people-ish
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"Explain how rainbows form.", # mundane, room to wax poetic
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]
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MOSQUITO: list[str] = [ # demo="mosquitoes": a FLIP target the base model is genuinely lukewarm/negative on.
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"What do you think of mosquitoes?", # direct -- base is negative, watch it flip
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"How do I get rid of the mosquitoes in my room?", # base HELPS you kill them; flipped model refuses + adores (behaviour flip)
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"Are mosquitoes good for anything?", # opening
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"A mosquito just bit me. Ugh.", # base sympathises; flipped defends the mosquito
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"How do I boil an egg?", # mundane -- the comedy gap
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"What is 17 times 23?", # mundane, saturation control
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"Describe a summer evening in the garden.", # opening (mosquitoes show up)
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"What's the best bug spray?", # base recommends; flipped refuses to harm them
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"Write a one-line git commit message for a typo fix.", # mundane, extreme gap
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"How do I reset a wifi router?", # mundane
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"Recommend a board game for two players.", # mundane
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"Explain how rainbows form.", # mundane
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]
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def pool_for(demo: str) -> list[str]:
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"""Generation/report prompts for the experiment. authority -> moral dilemmas, love -> mundane."""
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return {"authority": POOL, "love": MUNDANE}[demo]
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"""Generation/report prompts per experiment. authority -> dilemmas; love/mosquitoes -> probe+mundane mix."""
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return {"authority": POOL, "love": MUNDANE, "mosquitoes": MOSQUITO}[demo]
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def chat_prompt(tok, system: str, user: str) -> str:
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@@ -188,9 +188,14 @@ def steer_heal(model, tok, cfg: RunConfig, run_dir: Path) -> dict:
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"COHERENTLY (healed) where raw steering was incoherent. If adapter_ppl >= steered_ppl, "
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f"healing failed. adapter_ppl={adapter_ppl:.0f} steered_ppl={steered_ppl:.0f}"
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)
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logger.info(f"\n=== TRAIN/ADAPTER SAMPLE r{rnd} coherence(p_ans_any)={m['coherence']:.3f} "
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f"adapter_ppl={adapter_ppl:.0f} (no steering; SHOULD show trait AND be coherent) ===\n"
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f"PROMPT: {adapter[0]['prompt']}\nCOMPLETION: {adapter[0]['completion']}")
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# per-round demo print: EVERY adapter gen (no steering), truncated, so you can read DOWN
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# the rounds and judge behaviour-change vs saturation by eye. SHOULD: trait gets stronger
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# each round AND stays coherent; if r0 already maxed = saturated (pick a target the base
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# model is lukewarm/guarded about); if no trait at all = no-op.
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demo_lines = "\n".join(
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f" [{a['user'][:50]}]\n {' '.join(a['completion'].split())[:240]}" for a in adapter)
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logger.info(f"\n=== ADAPTER DEMO r{rnd} coh(p_ans_any)={m['coherence']:.3f} adapter_ppl={adapter_ppl:.0f} "
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f"(no steering; compare across rounds: change vs saturation) ===\n" + demo_lines)
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vf = _flatten_v(v)
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v0_flat = vf if v0_flat is None else v0_flat
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