Nash-Pruned CredMAS: Dynamic Panel Pruning for VLM-MAS using Nash-based Selection and Doubly-Robust Credits

摘要

Multi-round Vision-Language Model (VLM) Multi-Agent Systems (MAS) offer powerful reasoning capabilities but suffer from prohibitive costs due to static panel designs, where all N agents communicate at every T round. This approach is fundamentally inefficient, as it ignores the context-dependent and diminishing marginal utility of specific agents. To address this, we propose Nash-CredMAS, an economic framework that transforms agent selection into a dynamic resource allocation game. Unlike heuristic routing or one-time pruning, our method operates in two phases: (1) Offline Causal Value Learning, where we employ a doubly-robust (AIPW) estimator to train a context-aware value function from biased interaction logs, effectively learning the true marginal contribution of agents; and (2) Online Dynamic Auctions, where agents bid for communication slots based on their predicted utility. We formulate the inference-time selection as a submodular maximization problem under budget constraints, theoretically guaranteeing a (1 - 1/e)-approximation of the optimal coalition via a greedy strategy. Empirically, Nash-CredMAS achieves state-of-the-art results on challenging benchmarks, including MMMU and V*-Bench, while reducing token consumption by over 25% compared to static baselines. The system naturally converges to an economic equilibrium where agents actively remain silent when their marginal value does not justify the cost.