REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents

Key points

• Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience each run produces is rarely reused on the next.1
• Prior memory systems concentrate on retrieval, finding the most relevant past trajectory.1
• On repository-level bug fixing (SWE-ContextBench Lite), REM retrieves the related trajectory more accurately than every state-of-the-art memory system (68.7% vs. 55.6% match@1) and improves, in every replicate, end-task resolution (27.6% vs. 24.9%) at 17% lower cost.1
• Altogether, these results demonstrate that the bottleneck in experience reuse is not retrieval fidelity but how experience is stored and governed across its lifecycle, opening a control-theoretic perspective on agent memory.1

Sources (1)

• [1]REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 10:59 PM

  We introduce REM (Reusable Experience Memory), a framework that manages the memory lifecycle from inside the agent's think-act loop through three explicit decisions.

  Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience each run produces is rarely reused on the next.

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