All papers

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper presents DVLA-RL++, combining complementary semantic purification and reinforcement learning gating to reduce context contamination in few-shot learning.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

Recasts referring expression segmentation as a closed-loop policy iteratively refining explicit boundary contours via visuomotor actions.

Presents an agent harness that pairs frozen multimodal language models with modular navigation skills for general robot navigation tasks.

Uses diffusion inpainting to erase lesions from mammograms, creating synthetic healthy counterfactuals to augment breast cancer classification training.

Uses diffusion inpainting to erase lesions from mammograms, creating synthetic healthy counterfactuals to augment breast cancer classification training.

Proposes a training framework reconnecting backward gradient paths across autoregressive chunks to improve long video generation consistency.

Proposes a training framework reconnecting backward gradient paths across autoregressive chunks to improve long video generation consistency.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Presents a semi-automatic stride-independent image patching method guided by user input to train deep learning segmentation models.

Presents a semi-automatic stride-independent image patching method guided by user input to train deep learning segmentation models.

Proposes a set-associative spatial memory retrofit for streaming 3D reconstruction to preserve distinct viewpoints at shared spatial locations.

Proposes a set-associative spatial memory retrofit for streaming 3D reconstruction to preserve distinct viewpoints at shared spatial locations.

Investigates extracting spatial, attribute, and relational modifier grounding directly from frozen multimodal large language model representations using optimal transport.

Introduces an adaptive tokenization method allocating variable compute budgets to image diffusion based on visual complexity and spatial layout.

Introduces an adaptive tokenization method allocating variable compute budgets to image diffusion based on visual complexity and spatial layout.

Develops a framework incorporating character animation principles like exaggeration into generative motion models via adaptation and guidance.

Develops a framework incorporating character animation principles like exaggeration into generative motion models via adaptation and guidance.

Introduces a multi-dimensional benchmark evaluating reasoning-informed visual editing across temporal, causal, spatial, and logical operations.

Proposes representing robot policies entirely in deterministic stateful code instead of querying vision-language models during run-time execution loops.

Proposes representing robot policies entirely in deterministic stateful code instead of querying vision-language models during run-time execution loops.

Presents a framework linking game engines with a neural renderer to simulate interactive virtual environments for training embodied agents.

Presents a framework linking game engines with a neural renderer to simulate interactive virtual environments for training embodied agents.

Proposes a decoupled 4D reward framework separating static scenes from dynamic objects to improve consistency in generative video models.

Proposes a decoupled 4D reward framework separating static scenes from dynamic objects to improve consistency in generative video models.

Presents a test-time alignment framework that grounds generative 3D foundation models in observed geometry and appearance without model retraining.

Presents a test-time alignment framework that grounds generative 3D foundation models in observed geometry and appearance without model retraining.

Proposes a distributed multiplayer world model architecture where individual clients generate synchronized video views by exchanging player state fields.

Proposes a distributed multiplayer world model architecture where individual clients generate synchronized video views by exchanging player state fields.

Introduces a unified multimodal agent using evidence graphs to supervise external retrieval and reasoning across images and videos.

Presents a dense correspondence framework combining generative and semantic representations to handle non-rigid image editing and generation.

Presents a dense correspondence framework combining generative and semantic representations to handle non-rigid image editing and generation.

Presents a dense correspondence framework combining generative and semantic representations to handle non-rigid image editing and generation.

Presents a dense correspondence framework combining generative and semantic representations to handle non-rigid image editing and generation.

Presents a dense correspondence framework combining generative and semantic representations to handle non-rigid image editing and generation.

Introduces a benchmark evaluating metric point-to-point distance estimation in 3D foundation models using physical ground-truth measurements.

Introduces a benchmark evaluating metric point-to-point distance estimation in 3D foundation models using physical ground-truth measurements.

Introduces a lifting-free transformer model that directly synthesizes egocentric video from exocentric recordings without explicit 3D reconstruction.

Designs a vision transformer using a single recurrent block with depth-programmed experts to reduce stored parameters without accuracy loss.

Designs a vision transformer using a single recurrent block with depth-programmed experts to reduce stored parameters without accuracy loss.

Designs a vision transformer using a single recurrent block with depth-programmed experts to reduce stored parameters without accuracy loss.

Designs a vision transformer using a single recurrent block with depth-programmed experts to reduce stored parameters without accuracy loss.

Presents a previsualization workflow combining physical video capture and generative reshooting to evaluate and manage visual hallucination risk.

Introduces an audio-visual caption benchmark using atomic evaluation units to evaluate multimodal large language models reliably.

Presents a closed-loop goal-directed video world model that iteratively plans and realizes atomic steps for procedural tasks.

Generates seamlessly looping 3D cinemagraphs from static 3D Gaussian splats using periodic deformation fields guided by video models.

Studies how scaling to 30,000 hours of egocentric video affects world model fidelity for agents versus object interactions.

Proposes a self-evolving image editing method using rubric-guided contrastive verification without external reward models or human annotations.