We propose MV-RoboBench, a benchmark for evaluating the multi-view spatial reasoning capabilities of VLMs in robotic manipulation.

We survey how to turn abundant human videos into actionable supervision for VLA models, organizing methods into four categories and outlining key challenges for reliable real-world transfer.

We propose AD-MIR, a two-stage framework that decodes advertising intent by first constructing a structured multimodal database via semantic retrieval and keyword matching, then reasoning over it with an iterative, evidence-grounded inquiry loop that mimics marketing expertise, achieving state-of-the-art on AdsQA.

We propose DLR, an information-theoretic multi-pattern RL framework that generates diverse, high-success manipulation trajectories for scalable VLA pretraining, improving transfer and showing better data-scaling than standard single-pattern RL.

We introduce WorldReasonBench, a human-aligned benchmark that stress-tests video generators as future world-state predictors across four dimensions—World Knowledge, Human Centric, Logic Reasoning, and Information-Based reasoning—covering diverse tasks from physical dynamics and cultural understanding to spatial geometry and quantitative math.

We propose a diffusion policy optimization method that uses RL to autonomously generate smooth, low-variance long-horizon manipulation trajectories, enabling VLA training that outperforms models trained on human or Gaussian RL demonstrations on LIBERO.

We propose a hierarchical framework that decomposes 3D spatial understanding in VLMs into four progressive levels, and build an automated pipeline over ~5M images to generate 3D spatial VQA data for fine-tuning, achieving state-of-the-art on multiple spatial reasoning benchmarks.

We propose VITRA, a novel approach for pretraining Vision-Language-Action (VLA) models for robotic manipulation using large-scale, unscripted, real-world videos of human hand activities.

My contribution: I focus on the data processing pipeline, leveraging HaWoR to reconstruct 3D hand poses from human-centric videos.

HSSBench is a multilingual (UN six languages) benchmark of 13,000+ expert-curated samples designed to evaluate MLLMs’ cross-disciplinary, concept-to-vision reasoning in Humanities and Social Sciences, revealing clear gaps even in state-of-the-art models.

We introduce MIRA, a medical time-series foundation model pretrained on 454B+ time points that handles irregular sampling and missingness to deliver stronger zero-shot and fine-tuned forecasting across datasets and tasks.

We propose TransDiff, a diffusion-based single-view RGB-D depth completion method for accurate grasping of transparent objects.

We integrate evidential deep learning into DCRNN to provide sampling-free uncertainty quantification for spatiotemporal traffic forecasting, achieving improved predictive intervals measured by MIS.