6 accepted papers
Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robust spatial mental model. We identify three core cognitive challenges hindering this transition: spatial reasoning, long-…
Masked Diffusion Models (MDMs) offer greater flexibility in decoding order than autoregressive models but require careful path planning to achieve high-quality generation. While existing samplers typically greedily select positions with the lowest uncertainty, we identify their fundamental limitatio…
Hand-object interaction (HOI) reconstruction and synthesis are becoming central to embodied AI and AR/VR. Yet, despite rapid progress, existing HOI generation research remains fragmented across three disjoint tracks: (1) pose-only synthesis that predicts MANO trajectories without producing pixels; (
Diffusion Transformers (DiTs) have emerged as a powerful backbone for image generation, offering superior scalability over U-Nets. However, their practical deployment is hindered by significant computational costs. While Quantization-Aware Training (QAT) shows promise, its application to DiTs is cha…
Vision-Language-Action (VLA) models for autonomous driving show promise but falter in unstructured corner case scenarios, largely due to a scarcity of targeted benchmarks. To address this, we introduce Impromptu VLA. Our core contribution is the Impromptu VLA Dataset: over 80,000 meticulously curate…
In knowledge-intensive domains like scientific research, effective decisions rely on organizing and retrieving intricate data. Knowledge graphs (KGs) help by structuring entities, relations, and contextual dependencies, but building KGs in such domains is challenging due to inherent complexity, manu…