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Conflicted Records: Typed Adversarial Contracts for Auditable LLM Output Quality Enforcement

Kardelen Subasi, Jarosław A. Chudziak · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29669 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

LLMのリメイクされたレコードは、操作データベースからのレコードと同じ品質の期待に従ってデータ資産として扱われている生産情報システムをますます普及させます。単一のレコードは、同時に品質のルールを満たし、侵害することができます:幻覚に隣接する地下の事実、ステレオタイプに隣接する有用な答えです。古典的なオーディオデータのクリーニングは、両方の判断が同じ範囲で不一致しているため、これを解決することはできません。LLMの単一のレコードは、紛争を生み出したトレーニングの偏見を共有するためにもできません。プロンプトエンジニアリング、セミチェーン思考、およびRLHFなどの既存の介入は、表面エラー率を低下させ

Machine translation (Meta M2M100); the English original follows.

Abstract

LLM-generated records increasingly populate production information systems where they are treated as data assets subject to the same quality expectations as records from operational databases. A single such record can simultaneously satisfy and violate a quality rule: a grounded fact adjacent to a hallucination, a useful answer adjacent to a stereotype. Classical data cleaning cannot resolve this because the two judgments are incompatible over the same span; a single LLM judge cannot either, since it shares the training biases that produced the conflict. Existing interventions such as prompt engineering, chain-of-thought, and RLHF reduce surface error rates but provide no audit trail and cannot distinguish genuine remediation from blanket suppression. The question is whether a deterministic check over structured inter-agent records can catch the defects that referential checks alone miss. We present BiasGuard-D: two adversarial roles drawn from distinct model families exchange typed claim documents, a three-model quorum adjudicates by majority vote, and a combined referential-plus-semantic invariant validates the verdict. The semantic component fires on verdicts referential checks miss; removing the iterative loop drops invariant satisfaction sharply. The deliberation layer transfers from stereotype to hallucination remediation on HaluEval QA with only role-framing changes, raising right-answer recovery by 49 percentage points; the triage gate required a cross-family second auditor to maintain recall on objective hallucination. Every claim, vote, and instruction is stored as a typed record giving operators a provenance trail auditable under emerging AI regulations.

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Topics: Computer science, Adversarial system, Audit trail, Computer security, Quality (philosophy), Audit

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