GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust
  Multi-Turn Deep Search
Authors: name: Heng Zhang, name: Yuling Shi, name: Xiaodong Gu, name: Haochen You, name: Zijian Zhang, name: Lubin Gan, name: Yilei Yuan, name: Jin Huang

Multi-agent systems powered by Large Language Models excel at complex tasks
through coordinated collaboration, yet they face high failure rates in
multi-turn deep search scenarios. Existing temporal attribution methods
struggle to accurately diagnose root causes, particularly when errors propagate
across multiple agents. Attempts to automate failure attribution by analyzing
action sequences remain ineffective due to their inability to account for
information dependencies that span agents. This paper identifies two core
challenges: (i) distinguishing symptoms from root causes in multi-agent
error propagation, and (ii) tracing information dependencies beyond
temporal order. To address these issues, we introduce GraphTracer, a
framework that redefines failure attribution through information flow analysis.
GraphTracer constructs Information Dependency Graphs (IDGs) to explicitly
capture how agents reference and build on prior outputs. It localizes root
causes by tracing through these dependency structures instead of relying on
temporal sequences. GraphTracer also uses graph-aware synthetic data generation
to target critical nodes, creating realistic failure scenarios. Evaluations on
the Who\&When benchmark and integration into production systems demonstrate
that GraphTracer-8B achieves up to 18.18\% higher attribution accuracy compared
to state-of-the-art models and enables 4.8\% to 14.2\% performance improvements
in deployed multi-agent frameworks, establishing a robust solution for
multi-agent system debugging.