Trace Integrity for LLM Data Agents: A Vision for Auditable Structured Reasoning in Real-World Systems
[Sztuczna inteligencja] Autorzy: Srimonti Dutta, Akshata Kishore Moharir
Answer accuracy is an insufficient reliability signal for LLM data agents. In structured-data tasks, a benchmark-correct answer can be produced by an invalid trace. This paper introduces Trace Integrity, a deployment reliability criterion for evaluating whether the computation recorded behind an answer is explicit, executable, schema-valid, operator-faithful, replayable, answer-consistent, and auditable. We identify the Structure Gap as the deployment failure mode that makes Trace Integrity necessary: natural-language reasoning and free-form rationales do not reliably specify the operator-level programs required by real-world systems. We operationalize Trace Integrity with execution contracts, structured artifacts that bind user intent to schema elements, operator plans, assumptions, executable queries, verification status, and final-answer linkage. We also introduce CAIT (Correct Answe…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja