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ARXIV:2603.26639 · VISION-LANGUAGE MODELS · SUBMITTED 30 MAR · 21:51 UTC · FRESHNESS STALE
ARXIV:2603.26639VISION-LANGUAGE MODELSSUBMITTED 30 MAR · 21:51 UTCFRESHNESS STALEShihua Zhang · Qiuhong Shen · Shizun Wang · Tianbo Pan · Xinchao Wang · arXiv
A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods.
Opportunity summary
Pain A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods.
Evidence 68 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models…
Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes…
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods.
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10.48550/arXiv.2603.26639A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods.
Abstract
Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models into VLMs. Nevertheless, we observe that naive token fusion followed by standard fine-tuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues. In this paper, we propose GeoSR, a framework designed to make geometry matter by encouraging VLMs to actively reason with geometry tokens. GeoSR introduces two key components: (1) Geometry-Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non-geometric shortcuts and force the model to consult geometry tokens for spatial reasoning; and (2) Geometry-Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical. Together, these designs unleash the potential of geometry tokens for spatial reasoning tasks. Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state-of-the-art performance by effectively leveraging geometric information. The project page is available at https://suhzhang.github.io/GeoSR/.
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Proof status
unverified68 refs; 3 sources; 50% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models into VLMs.
METHOD
Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle this limitation by injecting geom...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remai...
WHY NOW
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state-of-the-art performance by effectively leveraging geometric information.
The abstract explicitly states this, and the results tables show GeoSR achieving higher scores than baselines on multiple benchmarks.
partial
Nevertheless, we observe that naive token fusion followed by standard fine-tuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues.
This is a core observation presented in the abstract that motivates the development of GeoSR.
partial
GeoSR introduces two key components: (1) Geometry-Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non-geometric shortcuts and force the model to consult geometry tokens for spatial reasoning;
This is a direct description of one of the two key components of the proposed GeoSR framework as stated in the abstract.
partial
(2) Geometry-Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical.
This is a direct description of the second key component of the GeoSR framework as stated in the abstract.
partial
GeoSR also introduces a negligible runtime increase, showing that the proposed design is efficient and does
The analysis section mentions efficiency and negligible runtime increase, supported by the text discussing computational cost.
partial
This framework can be productized by integrating with existing vision-language systems to enhance their ability to interpret and execute tasks based on spatial understanding.
The 'product_angle' section explicitly discusses productization and integration with existing systems.
partial
There is a risk that the integration of geometry tokens may not generalize across all types of VLMs or systems without extensive re-training.
The 'caveats' section explicitly mentions this as a risk associated with the integration of geometry tokens.
partial
we observe that naive token fusion followed by standard fine-tuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues.
This is a core observation stated in the abstract that motivates the proposed framework.
partial
Geometry-Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non-geometric shortcuts and force the model to consult geometry tokens for spatial reasoning
This is a direct description of one of the two key components of the proposed GeoSR framework, as stated in the abstract.
partial
Geometry-Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical.
This is a direct description of the second key component of the proposed GeoSR framework, as stated in the abstract.
partial
Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state-of-the-art performance by effectively leveraging geometric information.
The abstract explicitly states this achievement, and the results tables in the paper provide supporting evidence.
partial
GeoSR (Ours) 68.3 38.7 57.4 62.3 48.7 44.4 35.6 59.5 51.9
This claim is directly supported by the 'Spatial Reasoning Models' table, showing specific performance metrics.
partial
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A framework that forces vision-language models to actively use geometric information for improved spatial reasoning, outperforming existing methods.
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Vision-Language Models
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68 references, 3 sources, 50% evidence coverage.
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