14 citations · 14 across the 1 of their papers we have counts for
6 papers
LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction
Jin Lou, Zhiyuan Jing, Xupeng Wang +21
Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: actions are exposed, but their explanato…
DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents
Yu Liu, Zhilin Liu, Zhiwei Yang +7
As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception,…
Making Every Tool Call Count: Necessary Tool-Evidence Path Rewards for Agentic Vision-Language Models
Xingming Long, Yu Liu, Zhiwei Yang +7
Modern vision-language models (VLMs) can directly answer many image-grounded questions, yet they often struggle with complex queries requiring fine-grained visual details or extern…
ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation
Zeyuan He, Bowen Yang, Zhirui Fang +9
Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing actions from current observati…
InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
Junhao Cai, Zetao Cai, Jiafei Cao +39
Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, b…
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
Jin Ye, Junlong Cheng, Jianpin Chen +12
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massiv…