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TOPIC · ENTITY #21399

multi-agent systems (MAS)

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RESEARCH · 1 SOURCE · arXiv cs.AI

MAMath-Bench: process-aware multimodal benchmark for multi-agent mathematical reasoning (arXiv:2610.26943v1)

MAMath-Bench is a process-aware benchmark for evaluating multimodal mathematical reasoning in multi-agent systems, released by Mira AI Lab (paper arXiv:2610.26943v1). It contains 1,003 problems across 11 categories and multiple modalities (text, images, audio), with fine-grained milestone annotations to measure solution correctness, reasoning-process reliability, and robustness to adversarial perturbations; evaluations show collaboration does not reliably boost final-answer accuracy, can increase intermediate-coverage while adding redundancy, and remains vulnerable to subtle adversarial reasoning errors. The dataset is available at the AgentMath GitHub repository.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

M-Plan: closed-loop multi-agent framework integrating long- and short-term memory for LLM agents

M-Plan is a closed-loop multi-agent system (MAS) framework that combines long-term and short-term memory via global-local and past-current task-specific guidance. The paper (arXiv:2610.27032v1) describes guidance generation during execution, local hints when agents are confused, post-task memory optimization for future retrieval, and reports improved accuracy and efficiency on multi-turn benchmarks including ALFWorld, WebShop, and PDDL compared to several baselines.

6.0