1 citations · 1 across the 9 of their papers we have counts for
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X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning
Yi Wang, Zhenlong Yuan, Yuchan Guo +9
Chest X-ray (CXR) interpretation often requires integrating visual findings with task-specific evidence rather than relying on direct visual recognition alone. While tool-augmented…
VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction
Qiutong Chen, Yuchan Guo, Zhenlong Yuan +10
Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarization and text-centric priors of…
IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection
Rongbin Tan*, Rongbin Tan, Fangfang Lin* +16
Small defects in unfamiliar product categories can be difficult to distinguish from benign appearance variations in a global view. Additional reasoning over the same view may leave…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Learning Physics from Pretrained Video Models: A Multimodal Continuous and Sequential World Interaction Models for Robotic Manipulation
Zijian Song, Qichang Li, Sihan Qin +4
The scarcity of large-scale robotic data has motivated the repurposing of foundation models from other modalities for policy learning. In this work, we introduce PhysGen (Learning…
Robotic Manipulation is Vision-to-Geometry Mapping: Vision-Geometry Backbones over Language and Video Models
Zijian Song, Qichang Li, Jiawei Zhou +4
At its core, robotic manipulation is a problem of vision-to-geometry mapping (). Physical actions are fundamentally defined by geometric properties like 3D posi…
RADAR: Benchmarking Vision-Language-Action Generalization via Real-World Dynamics, Spatial-Physical Intelligence, and Autonomous Evaluation
Yuhao Chen, Zhihao Zhan, Xiaoxin Lin +11
VLA models have achieved remarkable progress in embodied intelligence; however, their evaluation remains largely confined to simulations or highly constrained real-world settings.…