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

• URL: http://arxiv.org/abs/2603.20260v1
• Date: Thu, 12 Mar 2026 07:02:39 GMT
• Title: ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics
• Abstract summary: We propose PROMAS, a proactive framework utiliz-ing Markov transitions for predictive error anal-ysis.<n>On the Who&When benchmark, PROMAS achieves 22.97% step-level accuracy while processing only 27% of reasoning logs.<n>This performance rivals reactive monitors like MASC while reducing data overhead by 73%.
• Score: 11.144773070519868
• License: http://creativecommons.org/licenses/by/4.0/
• 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 reducing data overhead by 73%. Although this strategy entails an accuracy trade-off compared to post-hoc meth-ods, it significantly improves intervention latency, balancing diagnostic precision with the real-time demands of autonomous reasoning.

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