8 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
MiniMax, :, Aili Chen +219
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…
IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
Rongbin Tan, Fangfang Lin, Zhenlong Yuan +10
Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot understanding across diverse indus…
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…
SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks
Zijian Song, Xiaoxin Lin, Qiuming Huang +3
Large Language Models (LLMs) have undergone rapid progress, largely attributed to reinforcement learning on complex reasoning tasks. In contrast, while spatial intelligence is fund…
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.…