Problem
Research questions and friction points this paper is trying to address.
agent memory
unanswerable questions
reliable handling
What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?
🤖 AI Summary
研究探讨了记忆机制在处理无法回答的问题时的作用，通过统一框架评估不同方法，发现程序性和基于规则的记忆对提高处理可靠性更有效。
📝 Abstract
Reliable handling of unanswerable questions (UAQs) is critical for trustworthy LLM-based agents. Although memory is widely used in agent systems, its role in reliable UAQ handling remains unclear. We present a systematic study of agent memory for UAQ handling under a unified agentic RAG framework, evaluating four representative memory methods across three UAQ-related datasets and two base models.
We find that memory can improve UAQ performance in some settings, but such gains are selective rather than universal and remain fragile under dataset shift. Interestingly, cross-model memory reuse is often more feasible than cross-dataset transfer, suggesting that shifts in answerability patterns pose a greater challenge to memory reuse than changes in the base model itself. We further find that UAQ gains are more strongly preserved through decision guidance than through trajectory shaping, and that memory effectiveness depends strongly on representation. In particular, procedural and rule-based memories often provide the most reliable support for UAQ handling, while memory composition is most effective when procedural guidance is combined with complementary behavioral signals. Overall, our findings suggest that reliable UAQ memory depends less on storing larger amounts of experience and more on preserving transferable behavioral guidance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agent Memory
Unanswerable Questions Handling
Cross-Model Memory Reuse
Decision Guidance
Procedural and Rule-Based Memories
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