SOURCE-LINKED INTELLIGENCE
DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems
Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile, frequently exhibiting reasoning and coordination errors that can lead to system-level failures. Failure attribution in such systems relies on tracing natural language interactions among agents to identify the decisive error, which refers to the earliest action whose correction can reverse system failure. There are two key challenges: 1) Shallow attribution: Existing methods often capture only minor deviations, such as incomplete retrievals or f
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-04T05:29:31.000Z
First collected: 2026-09-20T22:31:48.298Z. This is not the publication date.