6 papers
ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
Brain Team, Ziyang Gong, Haoming Gu +28
Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve f…
WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents
Zhixiang Guo, Siyuan Liang, Shi Fu +4
Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated automated evaluation methods. A k…
ACE-Brain-0: Spatial Intelligence as a Shared Scaffold for Universal Embodiments
Ziyang Gong, Zehang Luo, Anke Tang +21
Universal embodied intelligence demands robust generalization across heterogeneous embodiments, such as autonomous driving, robotics, and unmanned aerial vehicles (UAVs). However,…
Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models
Shi Fu, Yingjie Wang, Shengchao Hu +2
Self-Rewarding Language Models (SRLMs) achieve notable success in iteratively improving alignment without external feedback. Yet, despite their striking empirical progress, the cor…
HRP: High-Rank Preheating for Superior LoRA Initialization
Yuzhu Chen, Yingjie Wang, Shi Fu +4
This paper studies the crucial impact of initialization in Low-Rank Adaptation (LoRA). Through theoretical analysis, we demonstrate that the fine-tuned result of LoRA is highly sen…
A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops
Shi Fu, Yingjie Wang, Yuzhu Chen +2
High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted. Consequently, models increasin…