12 citations · 14 across the 10 of their papers we have counts for
10 papers
ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts
Mingxin Wang, Bin Hu, Bin Qian +12
World Action Models (WAMs) have emerged as a promising paradigm by jointly modeling robot actions and future visual dynamics. However, their reliance on pixel-generative future sup…
Orientation Reading by Production Vision-Language Models on Optotype Charts: A Controlled Multi-Model Evaluation Across Reasoning Modes, Prompts, and Access Modalities
Shahryar Wasif, Avneek Sandhu, Bin Hu
OBJECTIVES: Vision-language models are increasingly used to interpret medical and everyday images through consumer chat interfaces, yet their ability to read orientation - the sing…
ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
Ronghan Chen, Yandan Yang, Zuojin Tang +18
Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typically reactive and lack expl…
PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
Bin Hu, Yanwen Ma, Jiehui Huang +14
Recent game world models can synthesize visually plausible, action-conditioned rollouts. However, their interaction behaviors often remain limited to exploratory or wandering traje…
PROMPT: A Pre-registered Randomized Protocol for Component-Level Evaluation of Clinical AI Prompts
Bin Hu, Avneek Sandhu, Shahryar Wasif
BACKGROUND:Prompt engineering shapes medical AI outcomes, but prompt components are rarely tested as clinical interventions. We developed PROMPT (Pre-registered Randomized Outcome…
Dynamic Computerized Tumbling-E Testing for Temporal Reliability of Human Sequential Perceptual Decisions
Avneek Sandhu, Bin Hu
OBJECTIVES: Visual acuity and tumbling-E tasks are often treated as static threshold measures, yet sequential perceptual decisions unfold over time. A computerized tumbling-E task…
CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs
Chengwen Liu, Hao Peng, Jisheng Dang +3
In multimodal video reasoning, reinforcement learning-based methods typically rely on simplistic and inflexible reasoning-length control strategies that fail to adapt to the model'…
EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution
Shiyu He, Minchi Kuang, Mengxian Wang +2
Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence. In particular, long-horizon simulations…
Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
Zihao Zhou, Bin Hu, Chenyang Zhao +2
Recent studies have uncovered the potential of Large Language Models (LLMs) in addressing complex sequential decision-making tasks through the provision of high-level instructions.…
Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
Bin Hu, Chenyang Zhao, Pu Zhang +4
Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an embodied agent in…