10 citations · 19 across the 29 of their papers we have counts for
29 papers
Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft
Zhou Ziheng, Huacong Tang, Jinyuan Zhang +9
Discovering causal regularities and applying them to build functional systems--the discovery-to-application loop--is a hallmark of general intelligence, yet evaluating this capacit…
CREATE: Cross-Layer Resilience Characterization and Optimization for Efficient yet Reliable Embodied AI Systems
Tong Xie, Yijiahao Qi, Jinqi Wen +9
Embodied Artificial Intelligence (AI) has recently attracted significant attention as it bridges AI with the physical world. Modern embodied AI systems often combine a Large Langua…
SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents
Shaofei Cai, Yulei Qin, Haojia Lin +10
Agentic reinforcement learning (RL) holds great promise for the development of autonomous agents under complex GUI tasks, but its scalability remains severely hampered by the verif…
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
Kaichen He, Zihao Wang, Muyao Li +2
The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models. However, existing agents are typically confined to static, predefined actio…
UniCode: Augmenting Evaluation for Code Reasoning
Xinyue Zheng, Haowei Lin, Shaofei Cai +3
Current coding benchmarks often inflate Large Language Model (LLM) capabilities due to static paradigms and data contamination, enabling models to exploit statistical shortcuts rat…
OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
Zihao Wang, Muyao Li, Kaichen He +4
The choice of action spaces is a critical yet unresolved challenge in developing capable, end-to-end trainable agents. This paper first presents a large-scale, systematic compariso…
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents
Shaofei Cai, Zhancun Mu, Haiwen Xia +3
While Reinforcement Learning (RL) has achieved remarkable success in language modeling, its triumph hasn't yet fully translated to visuomotor agents. A primary challenge in RL mode…
Can Language Models Discover Scaling Laws?
Haowei Lin, Haotian Ye, Wenzheng Feng +8
Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To invest…
Inference-time Scaling of Diffusion Models through Classical Search
Xiangcheng Zhang, Haowei Lin, Haotian Ye +4
Classical search algorithms have long underpinned modern artificial intelligence. In this work, we tackle the challenge of inference-time control in diffusion models -- adapting ge…
LoopNav: Benchmarking Spatial Consistency in World Models
Kewei Lian, Shaofei Cai, Yitao Liang +1
The ability to simulate the world in a spatially consistent manner is a crucial requirement for effective world models. Such a model enables high-quality visual generation, and als…
Generative Evaluation of Complex Reasoning in Large Language Models
Haowei Lin, Xiangyu Wang, Ruilin Yan +7
With powerful large language models (LLMs) demonstrating superhuman reasoning capabilities, a critical question arises: Do LLMs genuinely reason, or do they merely recall answers f…
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse
Muyao Li, Zihao Wang, Kaichen He +2
Recently, action-based decision-making in open-world environments has gained significant attention. Visual Language Action (VLA) models, pretrained on large-scale web datasets, hav…
Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling
Shuqi Lu, Haowei Lin, Lin Yao +6
3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D…
Open-World Skill Discovery from Unsegmented Demonstrations
Jingwen Deng, Zihao Wang, Shaofei Cai +2
Learning skills in open-world environments is essential for developing agents capable of handling a variety of tasks by combining basic skills. Online demonstration videos are typi…
A Neural Symbolic Model for Space Physics
Jie Ying, Haowei Lin, Chao Yue +7
In this study, we unveil a new AI model, termed PhyE2E, to discover physical formulas through symbolic regression. PhyE2E simplifies symbolic regression by decomposing it into sub-…
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment
Shaofei Cai, Zhancun Mu, Anji Liu +1
We aim to develop a goal specification method that is semantically clear, spatially sensitive, domain-agnostic, and intuitive for human users to guide agent interactions in 3D envi…
TFG-Flow: Training-free Guidance in Multimodal Generative Flow
Haowei Lin, Shanda Li, Haotian Ye +4
Given an unconditional generative model and a predictor for a target property (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target…
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents
Shaofei Cai, Bowei Zhang, Zihao Wang +4
Developing agents that can follow multimodal instructions remains a fundamental challenge in robotics and AI. Although large-scale pre-training on unlabeled datasets (no language i…
MineStudio: A Streamlined Package for Minecraft AI Agent Development
Shaofei Cai, Zhancun Mu, Kaichen He +4
Minecraft's complexity and diversity as an open world make it a perfect environment to test if agents can learn, adapt, and tackle a variety of unscripted tasks. However, the devel…
Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies
Guangyu Zhao, Kewei Lian, Haoxuan Ru +8
Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performance is often highly sensitive to the cho…
ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting
Shaofei Cai, Zihao Wang, Kewei Lian +4
Vision-language models (VLMs) have excelled in multimodal tasks, but adapting them to embodied decision-making in open-world environments presents challenges. One critical issue is…
TFG: Unified Training-Free Guidance for Diffusion Models
Haotian Ye, Haowei Lin, Jiaqi Han +6
Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desira…
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
Zihao Wang, Shaofei Cai, Zhancun Mu +7
This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textua…
CLoG: Benchmarking Continual Learning of Image Generation Models
Haotian Zhang, Junting Zhou, Haowei Lin +6
Continual Learning (CL) poses a significant challenge in Artificial Intelligence, aiming to mirror the human ability to incrementally acquire knowledge and skills. While extensive…