✨Some Modalities are More Equal Than Others: Decoding and Architecting Multimodal Integration in MLLMs

📝 Summary:
MLLMs lack robustness to contradictory multimodal inputs. This work introduces MMA-Bench to analyze this brittleness and proposes a modality alignment tuning strategy. This strategy improves MLLMs robustness and cross-modal reasoning.

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22826
• PDF: https://arxiv.org/pdf/2511.22826
• Github: https://cskyl.github.io/MMA-Bench/

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#MLLMs #MultimodalAI #AIrobustness #CrossModalReasoning #MachineLearning

🔥 OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains

💡 The paper introduces a new dataset and method for improving audio-visual question answering systems. Current systems typically process videos in short clips and generate separate descriptions for audio and visual modalities, which can lead to inconsistent descriptions and a lack of cross-modal reasoning. To address this, the authors propose a two-part approach: entity-anchored video scripting, which transforms videos into structured scripts with summaries, main entity lists, and segment-wise audio-visual descriptions, and clue-guided QA generation, which prompts models to mine cross-segment clues from the script and generate QA pairs based on these clues.

The entity-anchored video scripting mechanism ensures cross-segment referential consistency and reconstructs audio-visual associations, while the clue-guided QA generation mechanism encourages models to generate questions that require long-term temporal connections and deep cross-modal reasoning. The authors use this pipeline to construct a new dataset called OmniVideo-100K, which consists of structured scripts and QA pairs, as well as a human-verified test set called OmniVideo-Test.

The results show that fine-tuning models on OmniVideo-100K yields significant performance gains, with improvements of up to 20.59% on the OmniVideo-Test set. The models also demonstrate strong generalization, with improvements of up to 12.64% on established benchmarks such as Daily-Omni and JointAVBench. Overall, the paper contributes a new dataset and method for improving audio-visual question answering systems, with a focus on cross-modal reasoning and temporal consistency.

📅 Published on Jun 12

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.14702
• PDF: https://arxiv.org/pdf/2606.14702
• Project Page: https://yzlmhzz.github.io/OmniVideo-100K/

📊 Datasets citing this paper:
• https://huggingface.co/datasets/MiG-NJU/OmniVideo-100K
• https://huggingface.co/datasets/MiG-NJU/OmniVideo-Test

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📢 By: https://xn--r1a.website/PaperNexus

#AudioVisualReasoning #MultimodalLearning #VideoUnderstanding #CrossModalReasoning #AudioVisualQuestionAnswering