CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures 事件

CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures arXiv:2605.25338v1 Announce Type: cross Abstract: Large language model (LLM) agents frequently fail on multi-step tasks involving reasoning, tool use, and environment interaction. While such failures are typically logged or retried heuristically, they contain structured signals about where execution broke down. We introduce CausalFlow, an interventional framework that converts failed agent traces into minimal counte