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ARXIV:2603.12545 · VISION-LANGUAGE MODELS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.12545VISION-LANGUAGE MODELSSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements.
Opportunity summary
Pain A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative…
Vision-language models (VLMs) have advanced rapidly, yet they still struggle with basic spatial reasoning. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative position, layout,…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results show consistent spatial performance gaps across models, and indicate that encoder objectives and positional structure shape spatial behavior, but do not fully…
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements.
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10.48550/arXiv.2603.12545A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements.
Abstract
Vision-language models (VLMs) have advanced rapidly, yet they still struggle with basic spatial reasoning. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative position, layout, and counting. We argue that this failure is not merely a data problem, but is closely tied to dominant design choices in current VLM pipelines: reliance on CLIP-style image encoders and the flattening of images into 1D token sequences with 1D positional encoding. We present a controlled diagnostic study within the LLaVA framework to isolate how these choices affect spatial grounding. We evaluate frontier models and LLaVA variants on a suite of spatial benchmarks, comparing CLIP-based encoders against alternatives trained with denser or generative objectives, as well as variants augmented with 2D positional encoding. Our results show consistent spatial performance gaps across models, and indicate that encoder objectives and positional structure shape spatial behavior, but do not fully resolve it.
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PROBLEM
A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative position, layout, a...
METHOD
Vision-language models (VLMs) have advanced rapidly, yet they still struggle with basic spatial reasoning. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative position, layout, and counting.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results show consistent spatial performance gaps across models, and indicate that encoder objectives and positional structure shape spatial behavior, but do not fully resolve it.
WHY NOW
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative position, layout, and counting.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-language models (VLMs) have advanced rapidly, yet they still struggle with basic spatial reasoning. Despite strong performance on general benchmarks, modern VLMs remain brittle at understanding 2D spatial relationships such as relative position, layout, and counting.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Our results show consistent spatial performance gaps across models, and indicate that encoder objectives and positional structure shape spatial behavior, but do not fully resolve it.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A study revealing the limitations of current vision-language models in spatial reasoning and proposing design improvements.
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Vision-Language Models
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