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DeALOG framework uses decentralized agents for multimodal question answering · arXiv paper

Researchers have introduced DeALOG, a novel decentralized multi-agent framework designed for multimodal question answering. This framework utilizes specialized agents for tasks such as table analysis, context understanding, visual processing, summarization, and verification. These agents communicate and maintain a shared natural-language log, which serves as persistent memory, enabling collaborative error detection and verification without a central controller. Evaluations on several benchmark datasets demonstrate DeALOG's competitive performance and highlight the significance of its shared log, agent specialization, and verification mechanisms for accuracy. AI

IMPACT Introduces a novel framework for multimodal question answering, potentially improving the robustness and scalability of complex information integration.

RANK_REASON The cluster contains an academic paper detailing a new framework for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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DeALOG framework uses decentralized agents for multimodal question answering · arXiv paper

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The cluster contains an academic paper detailing a new framework for AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Abhijit Chakraborty, Ashish Raj Shekhar, Shiven Agarwal, Vivek Gupta ·

    DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework

    arXiv:2602.00996v3 Announce Type: replace-cross Abstract: Complex question answering across text, tables and images requires integrating diverse information sources. A framework supporting specialized processing with coordination and interpretability is needed. We introduce DeALO…