✨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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For more data science resources:
✓ https://t.iss.one/DataScienceT
#MLLMs #MultimodalAI #AIrobustness #CrossModalReasoning #MachineLearning
📝 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/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#MLLMs #MultimodalAI #AIrobustness #CrossModalReasoning #MachineLearning
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