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

Key points

• 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.1
• 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.1
• 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.1
• The question is whether a deterministic check over structured inter-agent records can catch the defects that referential checks alone miss.1

Sources (1)

• [1]Conflicted Records: Typed Adversarial Contracts for Auditable LLM Output Quality Enforcement
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:43 PM

  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.

  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.

Extractive summary: sentences quoted from the sources.