"Thinking with Trajectories" for Critical Failure Localization in Long-Horizon Agents

Large language model (LLM) agents tackle complex tasks through sequences of interdependent steps, where an early critical failure can derail subsequent decisions and ultimately lead to task failure. As agents advance toward increasingly long-horizon tasks, detecting such failures becomes more challenging: global analysis requires reasoning over lengthy trajectories with scattered evidence, while step-by-step inspection can be overwhelmed by local errors and incurs rapidly growing computational overhead. Effective auditing therefore requires allocating reasoning budget to informative trajectory segments while preserving a global view. We address this challenge by modeling critical failure localization as an agentic search problem. Specifically, we propose Dean, a framework that enables a “*Thinking with Trajectories*” paradigm: it first scans a compressed trajectory overview to identify promising regions, and then progressively zooms into selected segments for fine-grained analysis. This global-to-local reasoning loop iteratively narrows the search space until the critical failure is grounded in localized trajectory evidence. Experiments across diverse benchmarks demonstrate that Dean achieves state-of-the-art performance, with its advantage widening as trajectories grow longer while exhibiting substantially more favorable token scaling. We further show that localized critical failure evidence can be readily integrated into existing agent pipelines to support self-improvement, highlighting its potential as a source of effective feedback for long-horizon agents. These results demonstrate the effectiveness and scalability of the “*Thinking with Trajectories*” paradigm for long-horizon critical failure localization.