ArXiv

TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories

2026-08-06 · 17:51 UTC ·Yunjia Qi, Zehua Yin, Xintong Shi... ·1 min read
Authors
Yunjia Qi, Zehua Yin, Xintong Shi...
Categories
cs.AI
arXiv
https://arxiv.org/abs/2608.06346v1
PDF
https://arxiv.org/pdf/2608.06346v1

Brief

TrajDebug addresses cascading failures in long-horizon LLM-based agent trajectories by compressing multi-granularity history and performing evidence-based error identification to find the earliest critical steps and attribute each error's terminal impact. The paper evaluates the method on TrajErrBench (486 manually annotated failed trajectories from Tau2Bench and SWE-Bench Pro) and reports it outperforms prior baselines while producing actionable debugging guidance. Summary based on the abstract.

Why it matters

TrajDebug is an error-lifecycle tracing framework that uses multi-granularity history compression and evidence-based error identification to locate earliest critical error steps and trace each error's resolution status and terminal impact (paper published 2026-08-06 by Yunjia Qi et al.).

Key details

  • The authors introduce TrajErrBench, a new benchmark of 486 manually annotated failed trajectories drawn from Tau2Bench and SWE-Bench Pro, covering realistic tool-use and coding scenarios for evaluation.
  • Experiments across diverse agent benchmarks show TrajDebug achieves the best overall performance versus existing baselines, and application studies report its diagnoses provide actionable feedback to improve downstream agent success; code and data will be released.
Source evidence

Abstract

LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging. Critical error detection aims to locate the earliest error step in a failed trajectory that is responsible for the final failure. However, progress faces two main challenges. First, long trajectories make it difficult to identify individual errors, since the evidence for judging a step may be scattered across distant instructions, observations, and prior context. Second, failed trajectories often contain multiple local errors with different downstream effects, only some of which remain responsible for the final failure. In this work, we propose TrajDebug, an error-lifecycle tracing framework that addresses long-trajectory error discovery with multi-granularity history compression and evidence-based error identification, and supports critical attribution by tracing each error's resolution status and terminal impact. We further construct TrajErrBench, a benchmark of 486 manually annotated failed trajectories from Tau2Bench and SWE-Bench Pro, covering realistic tool-use and coding scenarios. Experiments across diverse agent benchmarks show that TrajDebug achieves the best overall performance over existing baselines, and application studies further demonstrate that its diagnoses provide actionable feedback for improving downstream agent success. We will release the codes and data to facilitate further research.