Audit Protocol Reveals Limits of Explicit State Elicitation in AI Memory-Policy Classification
A research article available on arXiv (2608.17247) introduces a systematic audit protocol aimed at assessing the accuracy of personalized AI agents in utilizing user memory. The procedure begins by pinpointing dataset shortcuts, followed by the separation of combined prompt modifications. It then evaluates whether intermediate labels correlate with answers, analyzes decomposed semantic proof, and checks for failures at the provider level. Preliminary evaluations on a synthetic development set comprising 480 examples indicated benefits from a structured prompt bundle, although TF-IDF analysis showed lexical separability without any positive standalone Ignore instances. The authors created a controlled counterfactual set of 160 examples, featuring 40 matched four-way families and rule-based reference policies. Findings indicate that revealing the four state definitions enhances accuracy, while a distinct explicit state-output field does not significantly boost policy accuracy.
Key facts
- The paper proposes an empirical audit protocol for structured intermediate outputs in memory-policy classification.
- The protocol includes auditing dataset shortcuts, isolating bundled prompt changes, and checking answer-associated labels.
- A 480-example synthetic development set initially suggested gains from a state-structured prompt bundle.
- TF-IDF diagnostics showed lexical separability and no positive standalone Ignore cases.
- A frozen 160-example controlled counterfactual set was constructed with 40 matched four-way families.
- Exposing the four state definitions improves accuracy in policy classification.
- An isolated explicit state-output field does not significantly improve policy accuracy.
- The study is available on arXiv under identifier 2608.17247.
Entities
Institutions
- arXiv