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ARXIV:2605.09719 · 3D VISION-LANGUAGE MODELS · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.097193D VISION-LANGUAGE MODELSSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHAlaa Asfour · Christopher Indris · Leihan Chen · Tejas Vyas · Guanghui Wang · arXiv
A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs.
Opportunity summary
Pain A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B teacher to a…
Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our approach achieves 8.7x lower inference latency and a 3x reduction in model size while retaining 54-72% of the teacher's performance. A public repository…
3D Vision-Language Models moved forward this cycle; last verified May 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.
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A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs.
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10.48550/arXiv.2605.09719A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs.
Abstract
Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B teacher to a 2.29B student model. Our approach achieves 8.7x lower inference latency and a 3x reduction in model size while retaining 54-72% of the teacher's performance. The framework utilizes VGGT as the vision encoder and a multi-task distillation pipeline with uncertainty-aware loss weighting. To improve reasoning without chain-of-thought (CoT) data, we introduce "Hidden CoT": learnable latent tokens that serve as an internal scratchpad before answer generation. This is the first use of latent scratchpad reasoning in distilled 3D VLMs. The student model jointly performs spatial description, depth estimation, and object detection. Experiments on ScanNet and 3D-FRONT show strong spatial understanding, reaching 68-72% accuracy on proximity and contact tasks. Our framework enables efficient 3D scene QA on resource-constrained platforms.
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PROBLEM
A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B teacher to a 2.29B student model.
METHOD
Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B teacher to a 2.29B student model.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our approach achieves 8.7x lower inference latency and a 3x reduction in model size while retaining 54-72% of the teacher's performance. A public repository is linked, so build verification can inspect im...
WHY NOW
3D Vision-Language Models moved forward this cycle; last verified May 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B teacher to a 2.29B student model.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs. We propose a knowledge distillation framework that transfers spatial reasoning from a 7B teacher to a 2.29B student model.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our approach achieves 8.7x lower inference latency and a 3x reduction in model size while retaining 54-72% of the teacher's performance. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
3D Vision-Language Models moved forward this cycle; last verified May 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A knowledge distillation framework creates a lightweight vision-language model for efficient 3D spatial reasoning with reduced computational costs.
Segment
3D Vision-Language Models
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Commercial read
5.0/10 public viability
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