DeFA: Dependency-Guided Failure Attribution for LLM Agents
Quick summary
arXiv:2610.01256v1 Announce Type: new Abstract: Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understanding both step content and step dependencies. We introduce DeFA, a dependency-guided framework for agent failure attribution. DeFA first combines protocol relations and semantic dependencies into an event dependency graph spanning the trajectory. It then identifies events that may violate task requirements and traces their sources and subsequent effects to construct a failure propagation graph. Fina
Key takeaways
- arXiv:2610.01256v1 Announce Type: new Abstract: Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understanding both step content and step dependencies.
- We introduce DeFA, a dependency-guided framework for agent failure attribution.
- DeFA first combines protocol relations and semantic dependencies into an event dependency graph spanning the trajectory.
Why it matters
“DeFA: Dependency-Guided Failure Attribution for LLM Agents” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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