---
id: 20260616-T0-07
title: "能力最小化：LLM代理的风险感知门控框架"
title_en: "Capability Minimization: Risk-Aware Gating for LLM Agents"
url: https://ai.daily.yangsir.net/daily/20260616-T0-07
issue_date: 2026-06-16
publish_date: 2026-06-15T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.13884
---

# 能力最小化：LLM代理的风险感知门控框架

研究人员提出RACG框架，通过风险感知因果门控控制LLM代理行为。该机制能识别高置信度错误输出，暂停执行或请求用户干预。在测试中，将关键错误率降低72%，同时保持任务完成率90%。框架支持细粒度权限管理，为安全AI代理提供新范式，特别适用于高风险决策场景。

## English Version

**Capability Minimization: Risk-Aware Gating for LLM Agents**

Researchers propose RACG, a risk-aware causal gating framework to control LLM agent behavior. It detects high-confidence erroneous outputs, halting execution or requesting user intervention. Tests show a 72% reduction in critical errors while maintaining 90% task completion. The framework supports fine-grained permission management, offering a new paradigm for safe AI agents in high-risk decision scenarios.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.13884)

**详情页**：https://ai.daily.yangsir.net/daily/20260616-T0-07

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