Problem
Research questions and friction points this paper is trying to address.
Agentic AI
Large Language Models
Failure Localization
Minimal Repair Families
Counterfactual Replay
Localizing Emergent Failures in Agentic AI: Recovering Minimal Repair Families via Counterfactual Replay
🤖 AI Summary
研究解决了多语言模型代理系统中故障定位问题，通过提出Graph-Constrained Joint Replay方法识别最小修复集，有效减少了恢复任务成功所需的重放次数。
📝 Abstract
Failures in agentic AI systems can arise from interactions among messages exchanged by multiple large language model (LLM) agents. Pointwise attribution cannot distinguish a jointly necessary repair from alternative singleton repairs. We formulate Minimal Repair Family Recovery (MRFR): recovering all inclusion-minimal event sets whose counterfactual replay restores task success within a declared size bound. We propose Graph-Constrained Joint Replay (GCJR), which slices failure-relevant events from an execution dependency graph, constructs graph-feasible singleton and pair candidates, and verifies them by replay with paired clean counterparts. For fixed replay outcomes, GCJR is exact within its declared graph domain. On 90 in-scope cases from a 120-DAG controlled benchmark, GCJR achieves 1.000 Family Exact Match while reducing mean replay calls from 56.3 to 25.3 (55.1%) relative to exhaustive search. On a 24-case, four-agent LLM pilot, it again achieves 1.000 Family Exact Match and reduces mean model calls from 21.0 to 10.0 (52.4%); single-event replay misses jointly necessary repairs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Minimal Repair Family Recovery
Graph-Constrained Joint Replay
Counterfactual Replay
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