MAADBench: The Refreshable Paradigm For Anomaly Detection In Multi-agent Systems

Recent studies report LLM-based multi-agent systems (MAS) fail at rates of 41%–87%, yet to our knowledge, no benchmark to date supports systematic anomaly detection (AD) for them. Building MAS AD benchmarks is hard because they must remain valid as LLM systems evolve: tasks may leak into training data and thus be memorized by LLMs, traces and anomaly patterns expire as backbones evolve, and labels must be provided reliably for each refresh. To address these challenges, we present **MAADBench** (**MA**: multi-agent; **AD**: anomaly detection), the first refreshable MAS AD benchmark designed for diverse evolving LLM backbones underlying the agents. MAADBench combines (1) sampled-and-coupled generative tasks over a $\approx 10^{37}$-task space to mitigate task leakage, (2) refreshable trace generation under configurable LLM backbones, and (3) automated provision of cost-free, deterministic step-level labels for fine-grained AD evaluation. Beyond offering the paradigm itself, we run MAADBench with five SOTA LLM backbones, and release the dataset **MAADBench-Full** with 5,200 step-labeled traces. Benchmarking 25 AD methods on MAADBench dataset reveals substantial limitations in current approaches: they rely heavily on supervision, struggle with subtle MAS-specific anomalies, and lack robustness across LLM backbones. These gaps point to a rich research agenda for MAS-specific anomaly detection, with MAADBench providing a systematic and refreshable testbed for method development and evaluation. We open-source MAADBench-Full at <https://huggingface.co/datasets/hww123/MAADBench-full>.