AI RESEARCH
InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain
arXiv CS.AI
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ArXi:2606.03329v1 Announce Type: new 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 rewards or use lexical intermediate rewards for memory and retrieval actions.