aiqbench analysis
CM2 introduces a novel multi-agent framework designed to bridge the gap between vertical STEM deduction and horizontal cultural reasoning in multimodal large language models. By integrating retrieval-augmented generation with networked reasoning, the system demonstrates improved cross-modal arbitration across various MLLM backbones. This advancement addresses a critical underexplored area where current models struggle with interdisciplinary interpretation beyond stable symbol systems.
CM2: Multimodal Cultural Reasoning via an Integrated Multi-Agent Framework
arXiv:2608.30498v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their horizontal, interdisciplinary cultural reasoning, however, remains underexplored.We propose CM2, a multi-agent framework grounded in the cognitive pathway of human cultural interpretation. CM2 integrates multimodal perception, retrieval-augmented generation, networked reasoning, gated fusion, and reward-driven feedback.Experiments on CM2D across multiple MLLM backbones show consistent gains over CoT and typical reasoning paradigms; ablations validate each module's contribution, and conflict analyses confirm genuine cross-modal arbitration.
Abstract:Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their horizontal, interdisciplinary cultural reasoning, however, remains this http URL propose CM2, a multi-agent framework grounded in the cognitive pathway of human cultural interpretation. CM2 integrates multimodal perception, retrieval-augmented generation, networked reasoning, gated fusion, and reward-driven this http URL on CM2D across multiple MLLM backbones show consistent gains over CoT and typical reasoning paradigms; ablations validate each module's contribution, and conflict analyses confirm genuine cross-modal arbitration.
Accepted to the 23rd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2026) as a short paper. 11 pages, 4 figures. Code and dataset are available at this https URL
Artificial Intelligence (cs.AI)
arXiv:2608.30498 [cs.AI]
(or arXiv:2608.30498v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.30498 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 140 words
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