Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Evaluation

Existing benchmarks and methods for MAS failure attribution largely assume a single deterministic root cause for each execution failure. We challenge this assumption, arguing that MAS failures often admit multiple plausible attributions depending on an observer’s analytical perspective. To address this, we propose multi-perspective failure attribution and introduce MP-BENCH, the first benchmark and evaluation protocol designed for this setting. Through extensive experiments, we show that the perceived limitations of LLMs in failure attribution stem not from insufficient model capability, but from evaluation assumptions that fail to account for the inherently multi-perspective nature of MAS failures.