InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain 事件

InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain arXiv:2606.03329v1 Announce Type: new Abstract: Long-context tasks require LLMs to identify and preserve answer-relevant information from large contexts. Chunk-wise memory agents address this issue by sequentially reading document chunks, updating a compact memory, and generating the final answer from the accumulated memory. However, existing RL-based chunk-wise agents either rely on sparse final-answer reward