arXiv 'MemRefine': An LLM Judge That Deletes, Merges, or Preserves Agent Memories to Hit a Fixed Budget

arXiv·low signal

MemRefine (arXiv:2606.13177, posted June 11) proposes an LLM-guided compression framework for long-term agent memory: similarity proposes candidate pairs, then an LLM judge defers delete/merge/preserve decisions based on factual content, consistently meeting a target memory budget while preserving downstream task performance. Unlike fixed-window truncation, it lets agents bound memory cost without silently dropping load-bearing facts. For anyone building stateful agents, it is a concrete, implementable pattern for the recurring problem of memory growth versus context-window and retrieval cost — and it slots alongside the recent surge of budget-aware memory research.