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

TL;DR

PROMAS introduces a proactive error forecasting framework for multi-agent systems using Markov transition dynamics, enabling real-time error detection and intervention with reduced data overhead.

Contribution

It presents a novel proactive approach leveraging Markov models and causal features for early error prediction in multi-agent reasoning systems.

Findings

Achieves 22.97% step-level accuracy on the Who&When benchmark.

Reduces data processing by 73% compared to reactive methods.

Balances diagnostic accuracy with real-time intervention needs.

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.