All papers

The paper presents a framework to learn dexterous robotic hand control directly from human monocular video demonstrations without teleoperation.

This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts.

This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors.

This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation.

This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings.

Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining.

Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications.

The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts.

The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks.

The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification.

The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers.

The paper introduces a post-training quantization framework for autoregressive visual generation models to retain image quality under low precision.

The paper presents StimFlow, using conditional flow matching to synthesize fMRI brain activity directly in voxel space from visual stimuli.

The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints.

This study analyzes a decade of crowdsourced disaster photos to evaluate vision-language models against human geographic reasoning for location estimation.

The paper proposes a training-free method establishing entity identity before final rendering to maintain visual consistency across long storyboards.

The paper presents a dataset and framework mapping sketches and text to executable parametric computer-aided design code.

The paper presents a hierarchical agent combining vision-language models for task planning with deterministic control programs for micromanipulation.

The paper proposes a reasoning framework that discretizes visual states into tokens to jointly optimize visual computation and chain-of-thought steps.

The paper explores transferring frozen video representations across heterogeneous sensor subsets to classify breathing activity and pose.

The paper proposes an image-to-video framework combining three coordinate spaces to enable camera-controllable video generation.

This paper analyzes why omni-modal language models perceive non-textual inputs correctly yet fail to mention them in text outputs.

This paper introduces Unravel, a training-free framework that converts a single image into an editable, multi-object 3D scene.

This paper presents EventLens, a reinforcement learning framework that trains multimodal models to understand video through explicit event structure recovery.

This paper presents a cross-attention method with histological priors to improve training efficiency and convergence in spatial transcriptomics.

Develops an agentic text-to-image framework allowing agents to revisit and select earlier image states for iterative compositional generation.

Presents an activation quantization method that groups tokens via pre-scan features to improve low-bit post-training quantization for Mamba super-resolution models.

Presents a breast cancer risk prediction framework that explicitly aligns longitudinal examinations across multiple mammography views.

Introduces a training-free Bayesian state-update framework to reduce error accumulation and representational drift in streaming 3D reconstruction models.

Proposes a world-centric rotary positional embedding using 3D Gaussian primitives to improve cross-view attention in multi-view vision tasks.

Applies a causal inference framework to evaluate the impact of robotic social navigation signals in corridor interactions.

Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence.

Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving.

Accelerates video diffusion models by using closed-loop calibrated caching instead of fixed heuristic thresholds across denoising steps.

Uses a learned scene-coordinate localization module to maintain consistent 3D spatial context for image-goal robot navigation.

Introduces a diagnostic evaluation framework to systematically test generalization and perturbation robustness in vision-language-action robotic models.

Generates prompt-adapted adversarial images that block unauthorized visual questions on vision-language models while still answering legitimate prompts correctly.

Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation.

The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning.

The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing.

The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation.

The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors.

The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs.

The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems.

The paper investigates how observing partner states, sharing knowledge, and predicting partner actions affect coordination in embodied multi-agent teams.

The paper proposes a system for physical AI that continuously monitors environments to identify intervention needs and execute timely actions.

The paper introduces an in-context learning method that selects demonstration examples by mining a vision-language model's own past prediction mistakes.

The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency.

The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure.

Introduces a visual document retrieval benchmark focused on multi-page and cross-document evidence configurations in long documents.

Introduces a benchmark and agent framework for embodied navigation tasks requiring physical environment interactions like opening doors.

Designs an explicit digital-twin world model where robotic agents inspect, simulate, and calibrate physical parameters before acting.

Presents a compliant teleoperation control framework that reduces excessive contact forces during dexterous manipulation data collection.

Discovers robot capabilities using a generative flow network that creates symbolic task skeletons grounded in physical robot affordances.

Presents a multimodal benchmark testing whether models can explain observed chemical effects by linking structural features to reaction mechanisms.

The paper presents a benchmark of synthetic paired comparisons to evaluate video reward models across specific visual and motion dimensions.

The paper introduces phase-conditioned affordance fields to improve equivariant visuomotor policy learning for robotic manipulation.

The paper introduces a distribution-level evaluation metric for medical image segmentation to assess prediction sets without requiring paired ground-truth masks.

The authors analyze compatibility across multi-schema latent spaces in motion models, showing shared representations require explicit paired data to align.

The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals.