@inproceedings{fan-etal-2026-nash,
    title = "Nash-Pruned {C}red{MAS}: Dynamic Panel Pruning for {VLM}-{MAS} using Nash-based Selection and Doubly-Robust Credits",
    author = "Fan, Yijia  and
      Liu, Mingyu  and
      Yang, Jing  and
      Wang, Jian  and
      Wang, Keze  and
      Zhang, Jusheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1975/",
    pages = "39640--39650",
    ISBN = "979-8-89176-395-1",
    abstract = "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 \textit{context-dependent} and \textit{diminishing} marginal utility of specific agents. To address this, we propose \textbf{Nash-CredMAS}, an economic framework that transforms agent selection into a \textbf{dynamic resource allocation game}. Unlike heuristic routing or one-time pruning, our method operates in two phases: (1) \textbf{Offline Causal Value Learning}, where we employ a \textbf{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) \textbf{Online Dynamic Auctions}, where agents bid for communication slots based on their predicted utility. We formulate the inference-time selection as a \textbf{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."
}