AI 中文总结
研究大型语言模型在多轮执行中引发的可靠性风险，通过多轮评估量化安全漂移和操作幻觉现象，分析其根源，提出行动感知监督层，该层能拦截违规行为且无良性误报，提升了代理可靠性。
AI 中文摘要
作为工具使用型自主代理规划器的大型语言模型（LLMs）在多轮执行中引入了动态可靠性风险。单轮安全机制相对成熟，但长时间交互会暴露结构漏洞，初始对齐随时间退化。本文通过实证研究了多种先进LLMs中出现的两种失败模式：安全漂移，即声明的安全意图逐渐侵蚀，导致违反约束的行为；操作幻觉，即持续重复的工具调用，表明状态感知存在缺陷。通过对高风险道德困境、恶意请求和良性控制进行多轮评估，用声明-行动差距和活锁指标量化这些现象，证明其在直接执行协议下的跨模型普遍性。根本原因分析将不稳定性归因于当前代理循环中推理上下文与执行状态的解耦。我们提出了一个行动感知监督层，通过事后模拟表明该层可拦截违规行为且无良性案例误报。这项工作将重点从语言保障转移到可执行的架构机制，提升了代理可靠性。
英文摘要
Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution. While single-turn safety mechanisms are relatively mature, extended interactions reveal structural vulnerabilities where initial alignment degrades over time. This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift, the gradual erosion of declared safety intent leading to constraint-violating actions (e.g., textual refusal followed by reconnaissance and unsafe execution), and Operational Hallucination, persistent repetitive tool calls indicative of flawed state perception (e.g., livelocks even in legitimate tasks). Through controlled multi-turn evaluation on high-stakes ethical dilemmas, malicious requests, and benign controls, we quantify these phenomena using declaration-action gap and livelock metrics, demonstrating their cross-model prevalence under direct execution protocols. Root-cause analysis attributes the instabilities to the decoupling of reasoning context from execution state in current agent loops. We propose an Action-Aware Supervision Layer - a lightweight, plug-and-play architectural blueprint incorporating intent-action consistency checks, runtime state tracking, and forced termination primitives. Post-hoc simulation on captured failure trajectories shows the layer can intercept observed violations without false positives on benign cases. This work advances agent reliability by shifting focus from linguistic safeguards to enforceable architectural mechanisms for responsible agentic AI.
DOI：10.1109/ICAD69378.2026.11608655
