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ARXIV:2606.03988 · MULTIMODAL REASONING · SUBMITTED 03 JUN · 20:32 UTC · FRESHNESS FRESH
ARXIV:2606.03988MULTIMODAL REASONINGSUBMITTED 03 JUN · 20:32 UTCFRESHNESS FRESHMahtab Bigverdi · Lindsey Li · Weikai Huang · Yiming Liu · Jaemin Cho · Jieyu Zhang · +5 at arXiv
This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks.
Opportunity summary
Pain This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence verified
This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks. Many such problems require imaginative perception: inferring what would…
Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imaginative perception: inferring what would be seen from an…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Using the unified VLM BAGEL as the backbone, IPT supervision consistently improves spatial reasoning and often outperforms textual chain of thought training, even without…
Multimodal Reasoning moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks.
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10.48550/arXiv.2606.03988This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks.
Abstract
Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imaginative perception: inferring what would be seen from an unseen viewpoint, tracing paths through occluded spaces, or integrating partial observations into a coherent spatial representation. We introduce Imaginative Perception Tokens (IPT), intermediate perceptual representations that externalize what a VLM would perceive under alternative spatial configurations while remaining consistent with the observed input. To study this capability, we formulate three tasks, Perspective Taking (PET), Path Tracing (PT), and Multiview Counting (MVC), and construct datasets of approximately 20K examples with ground truth imaginations, answers, and evaluation benchmarks. Using the unified VLM BAGEL as the backbone, IPT supervision consistently improves spatial reasoning and often outperforms textual chain of thought training, even without generating images at inference time. On MVC, IPT improves accuracy by 3.4% and achieves competitive performance with strong closed-source models on PT. We further find that combining IPT and label-only supervision yields additional gains, whereas textual chain of thought can substantially degrade performance, suggesting a modality mismatch when spatial computation is forced through language. Overall, IPT provides a principled supervision signal for reasoning about unobserved spatial structure, improving generalization while producing interpretable intermediate representations.
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PROBLEM
This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks. Many such problems require imaginative perception: inferring what woul...
METHOD
Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imaginative perception: inferring what would be seen from an unseen viewpoint, tracing paths through occlu...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Using the unified VLM BAGEL as the backbone, IPT supervision consistently improves spatial reasoning and often outperforms textual chain of thought training, even without generating images at inference ti...
WHY NOW
Multimodal Reasoning moved forward this cycle; last verified June 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 29, "author": "Mahtab Bigverdi; Lindsey Li; Weikai Huang; Yiming Liu; Jaemin Cho; Jieyu Zhang; Tuhin Kundu; Chris Dangjoo Kim; Zelun Luo; Linda Shapiro; Ranjay Krishna"
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This research introduces a novel token-based approach to enhance spatial reasoning in vision-language models by externalizing imaginative perceptions, with demonstrated improvements on specific spatial tasks.
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Multimodal Reasoning
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