ai.hackcv
论文精选 65arXiv

OnTrack: Real-Time Monitoring and Intervention in LLM Agent Trajectories via Streaming Structure-Aware Optimal Transport· OnTrack:大模型代理路径实时监控与干预

Agents are deployed in applications from trip planners and stock trading to IT incident triage. In most cases, LLM agents work autonomously with minimal rule-based safeguarding, leading to cost and safety issues from irreversible actions. Recent works resolve this either by using a safeguard agent to monitor behavior or evaluating logs post-hoc. The first adds cost and latency to every step; the second delivers its verdict after the run, when tokens are burned and damage is done. To overcome this, we propose OnTrack, a streaming monitoring mechanism that compares an agent's steps and dependencies against recorded successful runs to alert users or block the agent in about a millisecond per step. We study this problem in three regimes of decreasing access: full reference access (historical r

AI 解读论文

通过结构感知最优传输实现代理行为实时监控与干预

核心方法
使用流式结构感知最优传输技术,对比代理行动与历史成功记录,实现实时监控与干预
适合谁读
研究者、工程师
要解决的问题
解决大模型代理在自主运行时因缺乏有效监控而导致的成本和安全问题
关键实验
在三个不同访问权限等级下进行了实验,验证了OnTrack的有效性和低延迟特性
主要贡献
提出了OnTrack机制,能在每步决策中以毫秒级延迟进行安全检查,减少不可逆损害
意义与局限
提高了大模型代理运行的安全性和成本效益,但可能因依赖历史数据而限制了创新性和灵活性
领域:cs.AI作者:Babak Barazandeh、Connor Swanson、Chinmay Kulkarni
相关推荐
论文精选 65已解读

Simulated Control Evals· 模拟控制评估

Control evaluations measure whether safeguards can prevent an AI model from causing undesi…

领域:cs.AI作者:Jeffrey Olmo、David Zhang、Tyler Tracy
📎 arXiv🕒 10-23 01:26🔗 arxiv.org

本站内容由 LLM 精选聚合,原文版权归 arXiv 所有 · 摘录仅供参考