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arXiv AI By Emanuele Mezzi, Gertjan Burghouts, Fabio Massacci, Mengyuan Zhang

EDMA: Entropy-Driven Multimodal Answering

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EDMA: Entropy-Driven Multimodal Answering introduces a framework that formalises multimodal question answering as an iterative entropy‑reduction process. By quantifying each modality’s contribution through logical entropy, EDMA constructs measurable answering trajectories and employs entropy‑driven fusion and selection to minimise logical entropy. Across standard MMQA benchmarks, EDMA surpasses prompting baselines, achieving significant gains in F1 and LLM‑J scores, and demonstrates that higher entropy correlates with more false positives, validating entropy as a reliable performance proxy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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