arXiv cs.CL· Jinkyeong Choi, Chaebin Jeong, Junsung Jeon, Donghyeon Park·· 11 小时前AI 评分34
KEEPER：为长程 LLM 智能体学习控制充分的文本记忆
KEEPER: Learning Control-Sufficient Textual Memory for Long-Horizon LLM Agents
AI 导读
KEEPER 在共享递归接口下沿信息内容与更新敏感度两个维度对比了四种辅助目标，其中奖励相关预测在离线强化学习前后任务表现均为最高，优于联合监督。研究将“自一致性偏差”定义为传入记忆中已有错误主张的持续存在，并通过一条基线轨迹的声明级分析记录了纠正性观察下错误仍被保留的现象。作者据此主张在任务结果之外同时评估错误持续性与有效信息保留。
正文 · 原文
Abstract:Recursive textual memory can carry earlier errors into later updates, yet task performance alone does not establish memory reliability. We define self-consistency bias as the persistence of incorrect claims already present in the incoming memory. We present KEEPER, a comparative analysis of four auxiliary objectives along information-content and update-sensitivity axes within a shared recursive interface, examining how supervision targets relate to agent performance. Among evaluated conditions, reward-related prediction achieve the highest task performance before and after offline reinforcement learning, outperforming joint supervision. A claim-level analysis of one baseline trajectory documents error persistence despite corrective observations. Together, these analyses motivate evaluating error persistence and valid-information retention alongside task outcomes.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.41482 [cs.CL] |
| (or arXiv:2610.41482v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41482 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinkyeong Choi [view email]
[v1]
Fri, 30 Oct 2026 09:36:06 UTC (410 KB)
来源：arXiv cs.CL · arxiv.org