---
id: 20260519-T0-10
title: "多标准潜在推理：代码智能体上下文剪枝新方法"
title_en: "Context Pruning for Coding Agents via Multi-Rubric Reasoning"
url: https://ai.daily.yangsir.net/daily/20260519-T0-10
issue_date: 2026-05-19
publish_date: 2026-05-18T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2605.15315
---

# 多标准潜在推理：代码智能体上下文剪枝新方法

arXiv论文提出多标准潜在推理方法，解决LLM代码智能体检索冗余问题。该技术能动态过滤75%无关代码，将推理速度提升3倍。在GitHub代码分析任务中，准确率保持90%的同时显著降低token消耗。

## English Version

**Context Pruning for Coding Agents via Multi-Rubric Reasoning**

New method uses multi-rubric latent reasoning to prune 75% irrelevant code for LLM coding agents, speeding up inference 3x while maintaining 90% accuracy in GitHub code analysis tasks.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2605.15315)

**详情页**：https://ai.daily.yangsir.net/daily/20260519-T0-10

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