让AI边干活边改记忆：新框架把经验写进状态
现在的AI智能体在长对话里会越用越笨：它只往前看，旧经验要么堆在上下文里挤爆窗口，要么靠额外数据回放来补救。这篇提出一个反直觉的做法——让AI在每处理完一个任务后，回头改写自己刚才用过的记忆，把这次踩的坑反向写回状态里，下次遇到类似情况就能直接用上。在三个持续学习基准和三个模型上，它都稳定超过现有方法，而且改出来的记忆有原文依据，不是瞎编。它不是你明天就能装上的功能，但指向一个方向：AI的“经验”不该是静态缓存，而该是能自我修订的工作笔记。
📄 原文摘要(英文)
Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or validation data for harness optimization. To address this, we propose ReHarness, a recurrent harness framework for agentic continual learning that complements forward read and write with backward revision under a strictly online streaming protocol. We ask what information could have been captured differently in the harness state to better inform the current instance in the stream. Specifically, after each instance, we leverage the resulting trajectory as feedback to revise the reflections retrieved for that instance, thereby propagating feedback backward through the textual harness state. Across three continual learning benchmarks and three language model backbones, ReHarness consistently outperforms strong raw-context baselines, memory- and reflection-based methods, and prompt optimization approaches. Further analysis shows that ReHarness produces source-grounded and meaningful revisions, corrects overgeneralized or outdated experience under distribution shifts, and improves both stability and plasticity during continual learning.