Learned Importance Scoring for Adaptive Memory Compaction in LLM-Based Agent Systems

Large language model (LLM) agents that operate over extended coding sessions accumulate conversation histories far exceeding context window limits. Existing compaction strategies—sliding window truncation, heuristic rule extraction, and LLM-driven summarization—either discard critical early-sessi…