Trace Integrity for LLM Data Agents: A Vision for Auditable Structured Reasoning in Real-World Systems
Score
76提出轨迹完整性与CAIT率等新概念，填补了答案准确率之外对LLM数据代理过程可靠性评估的空白。
框架定义清晰，并通过实验数据支撑核心观点，逻辑严密且具有可操作性。
执行契约和CAIT率为真实系统评测和风险监控提供了可直接落地的工具和指标。
当前LLM Agent落地需求迫切，过程审计是行业刚需，论文正当时。
来源为arXiv预印本，虽未同行评审，但作者背景与实证设计较为可信。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper proposes a new evaluation paradigm with empirical evidence, offering direct reference for designing reliable agent testing.
AI commentary
「This paper hits the weak point of LLM data agent evaluation: focusing only on answers, not processes. For AI infrastructure or reliability platform builders, execution contracts and CAIT rate are practical for designing regression tests and monitoring alerts. Teams putting agents into production should read this.」
Quick overview
This paper argues that answer accuracy alone is an insufficient reliability signal for LLM data agents: in structured-data tasks, a benchmark-correct answer can be produced by an invalid trace. The authors introduce Trace Integrity as a deployment reliability criterion, requiring that the computation behind an answer be explicit, executable, schema-valid, operator-faithful, replayable, answer-consistent, and auditable. They identify the Structure Gap as the key deployment failure mode, and operationalize Trace Integrity via execution contracts that bind user intent to schema elements, operator plans, assumptions, executable queries, verification status, and final-answer linkage. They also introduce the CAIT (Correct Answer / Invalid Trace) Rate to measure how often answer-only evaluation counts computationally unsupported outputs as successes. Experiments on BIRD Mini-Dev show that answer accuracy, trace validity, and silent-failure risk are distinct evaluation signals, with CAIT rates remaining high across methods.
- Proposes Trace Integrity as a deployment reliability criterion for LLM data agents, with seven auditability requirements.
- Identifies the Structure Gap as a deployment failure mode: natural-language reasoning and free-form rationales do not reliably specify operator-level programs.
- Introduces execution contracts that bind user intent to schema elements, operator plans, assumptions, queries, verification status, and final-answer linkage.
- Experiments on BIRD Mini-Dev show answer accuracy and trace validity are weakly correlated, with CAIT rates as high as 45.8%-59.1%, highlighting silent-failure risk.
Full text
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