Meta 与 MIT 提出_ReHarness：用任务结果回写修正智能体记忆
智能体记忆库只写不改的痛点，这篇 Meta 和 MIT 的论文用任务结果回写来解决，还过了显著性检验。
Meta 与 MIT 的论文提出 ReHarness，针对智能体记忆库只增不改的问题。该方法在任务完成后，根据实际执行轨迹回溯改写此前被检索使用的 reflection，修正过度泛化或过时的经验，并配合增删操作维护记忆库。实验覆盖 3 个持续学习基准、16 个任务和 3 个骨干模型，成绩超过 raw-context、memory、reflection 和 prompt-optimization 基线，且在多重比较校正后增益仍然显著。
Brilliant paper on agentic continual learning from Meta and MIT.
(bookmark it)
Agent memory harnesses usually only write forward. A lesson goes into the bank and is never checked against whether it helped.
This work proposes ReHarness, which rewrites the reflections an agent retrieved for a task after it sees how that task turned out.
Add and delete operations keep a reflection bank tidy, but they ignore downstream outcomes. ReHarness uses each finished trajectory as feedback on the specific reflections that guided it, so overgeneralized or outdated lessons get corrected.
Across three continual-learning benchmarks with 16 tasks and three backbones, it beats raw-context, memory, reflection, and prompt-optimization baselines, and the gains stay significant after multiple-comparison correction.
https://t.co/dy8IrZSLcx
Chat with Paper: https://t.co/pLIsnW45si
