adversarial-debate-truthseeking
Strategy: Dialectic engine retuned for truth-seeking, not survival. A defender steelmans a claim into its MOST falsifiable form, a critic attacks to refute it, a judge classifies the exchange into BROKEN/CORROBORATED/UNFALSIFIABLE — the judge does NOT pick a winner or score persuasiveness. Methods: Irving debate (repurposed), Toulmin argumentation, Mayo severe testing.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/adversarial-debate-truthseeking && cp -r /tmp/adversarial-debate-truthseeking/skills/adversarial-debate-truthseeking ~/.claude/skills/adversarial-debate-truthseekingSKILL.md
# Adversarial Debate (Truth-Seeking Variant)
A retuning of classic critic-defender-judge debate. The classic version asks "which side argued better?" and outputs a survival/resilience verdict — a persuasiveness metric. That is wrong for research: a claim can win a debate by being slippery (un-pin-down-able) while being scientifically empty. This variant changes the roles and the judge's job so the debate produces a falsifiability classification, not a winner.
## What changed from the original (multiagent-debate)
| Element | Original (publication) | This variant (truth-seeking) |
|---|---|---|
| Defender's job | Make the claim look strong / survive | State the claim in its MOST falsifiable form — maximize what it forbids |
| Critic's job | Find weaknesses to score against | Find one concrete observation/computation that would refute it |
| Judge's verdict | Winner + resilience score | Bucket: BROKEN / CORROBORATED / UNFALSIFIABLE |
| Success | Artifact survives | We learn whether the claim is even testable, and if so whether it holds |
| Slippery claim | Wins (un-attackable) | Flagged UNFALSIFIABLE (worst outcome) |
## Roles
### Defender (steelman-to-falsifiable)
The defender does NOT defend the claim as comfortable or vague. The defender's sole job is to restate the claim in the sharpest, most-forbidding form that is still faithful to what we actually meant. A claim of the form "the unification is elegant" is not a defendable form — it forbids nothing. "Under intervention X the law collapses to form A and NOT form B" is — it stakes out something an observation could contradict. If the defender cannot produce a forbidding form, that itself is the verdict (UNFALSIFIABLE) — the defender must report this honestly rather than retreat to vagueness.
### Critic (refuter)
The critic does NOT accumulate debating points. The critic tries to produce ONE of: (a) a counterexample (a case the claim forbids but that occurs / could occur), (b) a derivation error (the claim does not follow from its stated premises), (c) a demonstration that the claim's "forbidden" set is empty (it forbids nothing → UNFALSIFIABLE). The critic must commit to a specific refuter before arguing, to prevent goalpost-shifting.
### Judge (classifier, not picker)
The judge reads the exchange and assigns a bucket. The judge explicitly does NOT decide who argued better. The judge asks three questions in order:
1. Did the defender produce a falsifiable form? If NO → **UNFALSIFIABLE**.
2. Did the critic produce a valid refuter against that form? If YES → **BROKEN** (record the refutation).
3. If the form was falsifiable and survived a *severe* critic attack (one that would likely have succeeded if the claim were false) → **CORROBORATED** (record what was forbidden-and-held). A survival against a weak/lazy attack is NOT corroboration — the judge sends it back for a more severe round.
## Execution (per claim)
1. **debate-architect** (import) — set rounds (budget), pick the single claim, brief the three roles on the truth-seeking variant explicitly.
2. **Defender turn** — produce the most-falsifiable form. Record it verbatim.
3. **Critic turn** — commit to a refuter type, then attack.
4. **Cross-examination** (import cross-examination SOP) — judge probes the critic's refuter for validity and the defender's form for hidden escape hatches (the moves a slippery claim uses to dodge: redefining terms mid-debate, retreating to a weaker claim, "it's just a model").
5. **Severity check** — judge asks: if the claim were false, would this attack have caught it? If not, escalate (more severe critic) before any CORROBORATED verdict.
6. **Bucketing** — assign and record.
## Severity rubric (Mayo)
A test/attack is severe to the degree that the claim would probably have FAILED it if the claim were false. Cheap severity wins: prefer the attack that, had the claim been wrong, would most obviously have broken it. A claim that passes only weak tests stays at "untested," never "corroborated."
## Anti-patterns the judge must catch
- **Definitional retreat** — defender redefines a term to escape the counterexample. → counts as BROKEN of the original claim; the redefined claim is a NEW claim needing its own debate.
- **Persuasiveness creep** — either side scoring rhetorical points. → judge ignores; only refuters and forbidding-content count.
- **Model-shield** — "it's only a model, of course it's idealized" used to deflect a refuter that targets a load-bearing idealization. → does not save the claim; the idealization is the claim under test.
## Output
`DebateBucketing`: per claim → {most-falsifiable form produced (or "none — UNFALSIFIABLE") | critic's committed refuter | cross-exam findings | severity of attack | bucket | recorded refutation-or-forbidden-content}.Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems