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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

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LLM은 운영 데이터베이스의 기록과 동일한 품질 기대에 따라 데이터 자산으로 취급되는 생산 정보 시스템을 점점 더 많이 보유하고 있습니다. 하나의 이러한 기록은 동시에 품질 규칙을 만족시키고 위반할 수 있습니다 : 환각에 인접한 지형된 사실, 고정형에 인접한 유용한 답변입니다. 클래식 오디오 데이터 청소는 두 판단이 동일한 범위에서 불일치하기 때문에이 문제를 해결할 수 없습니다. 하나의 LLM 재판관은 충돌을 일으킨 훈련 편견을 공유하기 때문에 동시에 만족시킬 수 없습니다. 프롬프트 엔지니어링, 체인-of-thought 및 RLHF와 같은 기존 개입은 표면 오류 비율을 줄이지만 통제 트랙을 제공하지 못하고 유효한 게

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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