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cs.CV2026

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Shilin Yan, Jintao Tong, Hongwei Xue +6

The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit…

cs.CV2026

GigaWorld-Policy: An Efficient Action-Centered World--Action Model

Angen Ye, Boyuan Wang, Chaojun Ni +21

World-Action Models (WAM) initialized from pre-trained video generation backbones have demonstrated remarkable potential for robot policy learning. However, existing approaches fac…

cs.CV2026

GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning

GigaBrain Team, Boyuan Wang, Bohan Li +23

Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and we…

cs.CV2025

SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead

Chaojun Ni, Cheng Chen, Xiaofeng Wang +12

Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To…

cs.CV2025

GigaWorld-0: World Models as Data Engine to Empower Embodied AI

GigaWorld Team, Angen Ye, Boyuan Wang +22

World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explic…