ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918400324796416 |
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| author | Zhao, Xinkui Liu, Sai Zhang, Yifan Ma, Qingyu Cheng, Guanjie Wang, Naibo Liu, Chang |
| author_facet | Zhao, Xinkui Liu, Sai Zhang, Yifan Ma, Qingyu Cheng, Guanjie Wang, Naibo Liu, Chang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20260 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics Zhao, Xinkui Liu, Sai Zhang, Yifan Ma, Qingyu Cheng, Guanjie Wang, Naibo Liu, Chang Artificial Intelligence 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. |
| title | ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.20260 |