Multimodal LLMs fail to express what they perceive from non-text inputs
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have identified a critical gap in multimodal LLMs: models successfully process non-textual inputs like vision and audio but struggle to translate that understanding into coherent responses. The study introduces Token-level Modality Alignment to measure how well different modality representations connect to generated text, revealing that non-textual signals receive systematically less attention during decoding. This finding exposes a fundamental architectural weakness in current omni-modal systems, suggesting that performance bottlenecks may stem not from perception but from the models' inability to route and prioritize cross-modal information during generation. The work has direct implications for practitioners building multimodal systems and points toward necessary refinements in how models integrate heterogeneous inputs.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
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The critical insight is that multimodal LLMs may possess strong cross-modal understanding but fail to use it. Token-level Modality Alignment measures not whether models perceive non-text inputs, but whether they actually weight those representations when generating output, revealing a decoding-phase failure rather than an encoding one.
This directly extends the pattern established in recent multimodal diagnostics. The spatial representation failures documented in the VLM evaluation framework (October 9) and the compositional misalignment in diffusion models (October 7) both pointed to representation space problems. This work adds a new layer: even when representations exist and align, the generation mechanism systematically deprioritizes non-textual signals. The retrieval-augmented clinical model (October 9) sidesteps this by using embeddings for retrieval rather than relying on end-to-end routing, suggesting practitioners may need similar architectural workarounds until the core decoding bottleneck is addressed.
If teams release ablations showing that simply increasing non-text token weights during beam search or reranking improves downstream task performance without retraining, that confirms the bottleneck is routing rather than representation quality. Conversely, if performance gains require full architectural retraining, the problem is deeper than attention allocation.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
VLMs fail at spatial reasoning despite strong benchmark scores
A new evaluation framework reveals a critical gap in how vision-language models encode spatial information compared to language-based concepts. Researchers introduced EICS and CASP-VL, benchmarks that stress-test whether VLMs maintain consistent internal representations under targeted perturbations. The finding that spatial reasoning degrades substantially below lexical performance, despite strong compositional benchmark scores, exposes a fundamental fragility…
MentionsOmni-modal LLMs · Token-level Modality Alignment
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Omni-Modal LLMs Know More Than They Say”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.