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Review regressions before expanding the test." ], "measure": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.21091", "title": "MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning", "summary": "It replaces heuristic retrieval-then-concatenate memory selection (semantic similarity top-k) with a trained composer that jointly chooses which memories to include, how many, and in what order, using downstream task-success feedback rather than similarity scores. 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