[2603.20260] ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics

Computer Science > Artificial Intelligence arXiv:2603.20260 (cs) [Submitted on 12 Mar 2026] Title:ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics Authors:Xinkui Zhao, Sai Liu, Yifan Zhang, Qingyu Ma, Guanjie Cheng, Naibo Wang, Chang Liu View a PDF of the paper titled ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics, by Xinkui Zhao and 6 other authors View PDF HTML (experimental) Abstract:The integration of Large Language Models into Multi-Agent Systems (MAS) has enabled the so-lution of complex, long-horizon tasks through collaborative reasoning. However, this collec-tive intelligence is inherently fragile, as a single logical fallacy can rapidly propagate and lead to system-wide failure. Most current research re-lies on post-hoc failure analysis, thereby hinder-ing real-time intervention. To address this, we propose PROMAS, a proactive framework utiliz-ing Markov transitions for predictive error anal-ysis. PROMAS extracts Causal Delta Features to capture semantic displacement, mapping them to a quantized Vector Markov Space to model reasoning as probabilistic transitions. By inte-grating a Proactive Prediction Head with Jump Detection, the method localizes errors via risk acceleration rather than static thresholds. On the Who&When benchmark, PROMAS achieves 22.97% step-level accuracy while processing only 27% of reasoning logs. This performance rivals reactive monitors like MASC while...