ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics
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...
| Authors | Xinkui Zhao, Sai Liu, Yifan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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