analiza aiqbench
This arXiv paper proposes transferring intermediate features from a video generation model into a vision-language model to improve dynamic spatial reasoning, using separate raw and temporal-delta branches fused by task-conditioned gates. The reported 67.2% on DSR-Bench is a benchmark result from a single preprint, so it needs independent replication before it counts as a settled advance. For readers tracking multimodal models, the useful signal is that generative video models may supply temporal representations that understanding-focused VLMs lack.
GenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning
arXiv:2610.41314v1 Announce Type: cross Abstract: Dynamic spatial reasoning is essential for connecting visual intelligence to thephysical world, yet remains challenging for vision-language models (VLMs), which must preserve scene state while tracking how spatial relations evolve over time. Meanwhile, video generation models (VGMs), trained to synthesize temporally coherent world dynamics, offer a rich source of spatiotemporal representations. We present GenDSR, a framework that transfers intermediate representations from VGM into VLM for dynamic spatial reasoning. Rather than treating a VGM feature as a monolithic prior, GenDSR constructs two complementary branches: a raw branch preserving holistic generative context and a delta branch emphasizing temporal variation. Task-conditioned tokenwise gates then fuse both signals and inject the result into the native visual representation. On DSR-Bench, GenDSR achieves 67.2%, surpassing the previous best result and establishing a new state of the art.
Abstract:Dynamic spatial reasoning is essential for connecting visual intelligence to thephysical world, yet remains challenging for vision-language models (VLMs), which must preserve scene state while tracking how spatial relations evolve over time. Meanwhile, video generation models (VGMs), trained to synthesize temporally coherent world dynamics, offer a rich source of spatiotemporal representations. We present GenDSR, a framework that transfers intermediate representations from VGM into VLM for dynamic spatial reasoning. Rather than treating a VGM feature as a monolithic prior, GenDSR constructs two complementary branches: a raw branch preserving holistic generative context and a delta branch emphasizing temporal variation. Task-conditioned tokenwise gates then fuse both signals and inject the result into the native visual representation. On DSR-Bench, GenDSR achieves 67.2%, surpassing the previous best result and establishing a new state of the art.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.41314 [cs.CV]
(or arXiv:2610.41314v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.41314 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 231 cuvinte
Subiecte conexe
ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density
arXiv · 5 aug. 2026
Beyond the Encoder: Object-Centric Representations Emerging from Segmentation Foundation Models
arXiv · 14 oct. 2026
Looking Beyond the Scale: Do Surgical Skill Models Learn Transferable Representations Across Assessment Rubrics?
arXiv · 20 aug. 2026
From Symmetry to Invariance: Learning Galois Equivalent Representations in Finite Fields
arXiv · 25 aug. 2026