Mechanistic Attention Guidance for Agent Memory Refinement
Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections. However, this text-based paradigm rarely incorporates internal mechanistic signals, leaving how retrieved memory is actually utilized during task execution underexplored. This limitation can lead to unreliable error attribution and hallucinated memory modifications. In this work, we show that retrieval-head attention provides a mechanistic signal for revealing segment-level memory utilization. By aggregating attention over memory segments and decision steps, we construct a context utilization matrix that exposes recurring memory-use patterns and indicates ...
| Authors | Yechao Hong, Haiquan Qiu, Yaqing Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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