arXiv cs.CL· Yuxin Xiao, Zhiyun Lu, Jianyu Wang, Yangtian Zhang, Yang Song, Qifan Wang, Bailin Wang·· 7 天前AI 评分29
ReHarness：面向智能体持续学习的循环式 harness 框架
Recurrent Harness for Agentic Continual Learning
AI 导读
研究者提出 ReHarness，一种用于智能体持续学习的循环式 harness 框架，在严格在线流式协议下，用前向读写之外的“反向修订”机制，将每个实例产生的轨迹作为反馈，反向修正该实例检索到的反思内容。
正文 · 原文
Abstract: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 Further analysis shows that ReHarness produces source-grounded and meaningful revisions, corrects overgeneralized or outdated experience under distribution shifts, and improves both stability and plasticity during continual learning.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12668 [cs.LG] |
| (or arXiv:2610.12668v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12668 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuxin Xiao [view email]
[v1]
Thu, 8 Oct 2026 19:44:43 UTC (422 KB)
来源：arXiv cs.CL · arxiv.org