6 accepted papers
The application of vision-language models in medicine is critically hampered by the scarcity of high-quality, expert-annotated data. Supervised fine-tuning on existing datasets often leads to poor generalization on unseen modalities and tasks, while reinforcement learning, a promising alternative, i
This paper presents RynnVLA-001, a vision-language-action (VLA) model built upon large-scale video generative pretraining from human demonstrations. We propose a novel two-stage pretraining methodology. The first stage, Ego-Centric Video Generative Pretraining, trains an Image-to-Video model to pred…
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream m
Research ideation is crucial for scientific progress, but the exponential increase in scientific literature makes it challenging to stay updated and identify impactful directions. Recent developments in large language models(LLMs) offer a promising avenue to automate this process. However, existing
Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing techniques that disregard layout information and are prone to errors, while recent parsing-free visual methods often stru…
Temporal knowledge graph (TKG) serves as an essential tool in modeling complex event relations among real-world entities. A temporal knowledge graph can be viewed as a collection of knowledge graph snapshots ordered by time. Reasoning over such graphs remains nontrivial as temporal causal dependenci…