Publications (12)
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Guibin Zhang, Hejia Geng, Xiaohang Yu +22
The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LL…
ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction
Xiaohang Yu, Haoxiang Wang, Yuqi Han +3
This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolatio…
opML: Optimistic Machine Learning on Blockchain
KD Conway, Cathie So, Xiaohang Yu +1
The integration of machine learning with blockchain technology has witnessed increasing interest, driven by the vision of decentralized, secure, and transparent AI services. In thi…
SUDP: Secret-Use Delegation Protocol for Agentic Systems
Xiaohang Yu, Hejia Geng, Xinmeng Zeng +1
Agentic systems increasingly act with user secrets for APIs, messaging platforms, and cloud services. Today's agent runtimes typically implement authorization by exposure: enabling…
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience
Xiaohang Yu, Zhengxian Yang, Shi Pan +8
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for vario…
opp/ai: Optimistic Privacy-Preserving AI on Blockchain
Cathie So, KD Conway, Xiaohang Yu +2
The convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain pl…
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
Yuqi Han, Tao Yu, Xiaohang Yu +2
The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on th…
LOCARD: An Agentic Framework for Blockchain Forensics
Xiaohang Yu, William Knottenbelt
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a pa…
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning
Zelin Tan, Hejia Geng, Xiaohang Yu +14
While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement learning (RL) post-training remains largel…
FMPose3D: monocular 3D pose estimation via flow matching
Ti Wang, Xiaohang Yu, Mackenzie Weygandt Mathis
Monocular 3D pose estimation is fundamentally ill-posed due to depth ambiguity and occlusions, thereby motivating probabilistic methods that generate multiple plausible 3D pose hyp…
PRIMA: Boosting Animal Mesh Recovery with Biological Priors and Test-Time Adaptation
Xiaohang Yu, Ti Wang, Mackenzie Weygandt Mathis
We present PRIMA (*PRI*ors for *M*esh *A*daptation), a framework for robust 3D quadruped mesh recovery under severe species and pose imbalance. Existing animal reconstruction metho…
On the Periodic Orbits of the Dual Logarithmic Derivative Operator
Xiaohang Yu, William Knottenbelt
We study the periodic behaviour of the dual logarithmic derivative operator in a complex analytic setting. We show that $\mathcal{A…