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The Alignment Illusion in Multimodal Large Language Models

Research Multimodal & Generative

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TL;DR - A study of 13 multimodal LLMs finds that common visual-text alignment scores can remain high even when visual tokens are corrupted and task accuracy collapses. It attributes this “alignment illusion” to shared language-model weights and proposes the principal-angle gap as a more reliable geometric diagnostic.

  • CKA, SVCCA, MIR, and leading principal-angle cosine often failed to distinguish genuine visual tokens from Gaussian noise.
  • Anisotropic MLP down-projections can pull visual and text representations toward shared output directions, creating largely one-dimensional, weight-induced similarity.
  • The proposed principal-angle gap measures the difference between the top two principal-angle cosines to separate this effect from multidirectional visual structure.
  • Under graded corruption, the gap tracked task accuracy more consistently, though irrelevant-image tests showed that internal geometry still need not correspond directly to task performance.

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The Alignment Illusion in Multimodal Large Language Models

arXiv cs.CV Hong-Han Wang, Yuntao Wang, Hu Ding 2026-09-24 arXiv:2609.30210
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Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-26 14:14:09.625989 UTC

TL;DR - A study of 13 multimodal LLMs finds that common visual-text alignment scores can remain high even when visual tokens are corrupted and task accuracy collapses. It attributes this “alignment illusion” to shared language-model weights and proposes the principal-angle gap as a more reliable geometric diagnostic.

  • CKA, SVCCA, MIR, and leading principal-angle cosine often failed to distinguish genuine visual tokens from Gaussian noise.
  • Anisotropic MLP down-projections can pull visual and text representations toward shared output directions, creating largely one-dimensional, weight-induced similarity.
  • The proposed principal-angle gap measures the difference between the top two principal-angle cosines to separate this effect from multidirectional visual structure.
  • Under graded corruption, the gap tracked task accuracy more consistently, though irrelevant-image tests showed that internal geometry still need not correspond directly to task performance.
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