---
id: 20260502-T0-11
title: "风险感知情境老虎机：LLM编码代理的记忆检索"
title_en: "Risk-Sensitive Bandits: Memory Retrieval for LLM Coding Agents"
url: https://ai.daily.yangsir.net/daily/20260502-T0-11
issue_date: 2026-05-02
publish_date: 2026-05-01T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2604.27283
---

# 风险感知情境老虎机：LLM编码代理的记忆检索

研究提出风险感知情境老虎机算法，优化LLM编码代理的记忆检索机制。该算法解决关键问题：何时应该从外部记忆中检索信息。研究显示，当前代理过度依赖检索的记忆，即使与当前任务无关。新算法通过风险感知机制，仅在记忆与当前失败高度相关时检索，减少无关信息干扰。实验证明，该方法在软件工程任务中提升修复成功率31%，减少不必要的计算开销。该技术可应用于代码调试工具和智能开发环境。

## English Version

**Risk-Sensitive Bandits: Memory Retrieval for LLM Coding Agents**

Researchers proposed a risk-sensitive contextual bandit algorithm to optimize memory retrieval in LLM-based coding agents. The solution addresses when to retrieve information from external memory, as current agents over-retrieve irrelevant data. The algorithm uses risk-aware mechanisms to only retrieve memory when highly relevant to current failures. Experiments showed a 31% improvement in fix success rates on software engineering tasks with reduced computational overhead. This technology can enhance code debugging tools and intelligent development environments.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2604.27283)

**详情页**：https://ai.daily.yangsir.net/daily/20260502-T0-11

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