21 citations · 74 across the 16 of their papers we have counts for
17 papers
UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience
Zichuan Lin, Feiyu Liu, Yijun Yang +9
Autonomous mobile GUI agents have attracted increasing attention along with the advancement of Multimodal Large Language Models (MLLMs). However, existing methods still suffer from…
ProAct: Agentic Lookahead in Interactive Environments
Yangbin Yu, Mingyu Yang, Junyou Li +9
Existing Large Language Model (LLM) agents struggle in interactive environments requiring long-horizon planning, primarily due to compounding errors when simulating future states.…
IC-World: In-Context Generation for Shared World Modeling
Fan Wu, Jiacheng Wei, Ruibo Li +4
Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared worl…
Yan: Foundational Interactive Video Generation
Deheng Ye, Fangyun Zhou, Jiacheng Lv +15
We present Yan, a foundational framework for interactive video generation, covering the entire pipeline from simulation and generation to editing. Specifically, Yan comprises three…
Playable Game Generation
Mingyu Yang, Junyou Li, Zhongbin Fang +5
In recent years, Artificial Intelligence Generated Content (AIGC) has advanced from text-to-image generation to text-to-video and multimodal video synthesis. However, generating pl…
Nuance Matters: Probing Epistemic Consistency in Causal Reasoning
Shaobo Cui, Junyou Li, Luca Mouchel +2
To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating inte…
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models
Fuxiang Zhang, Junyou Li, Yi-Chen Li +3
Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge…
ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining
Kexin Chen, Yuyang Du, Junyou Li +7
The development of AI-assisted chemical synthesis tools requires comprehensive datasets covering diverse reaction types, yet current high-throughput experimental (HTE) approaches a…
More Agents Is All You Need
Junyou Li, Qin Zhang, Yangbin Yu +2
We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method, termed as A…
Affordable Generative Agents
Yangbin Yu, Qin Zhang, Junyou Li +2
The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged…
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Kexin Chen, Jiamin Lu, Junyou Li +12
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a comprehensive AI agent that automates the…
Pretraining in Deep Reinforcement Learning: A Survey
Zhihui Xie, Zichuan Lin, Junyou Li +2
The past few years have seen rapid progress in combining reinforcement learning (RL) with deep learning. Various breakthroughs ranging from games to robotics have spurred the inter…
Revisiting Discrete Soft Actor-Critic
Haibin Zhou, Tong Wei, Zichuan Lin +6
We study the adaption of Soft Actor-Critic (SAC), which is considered as a state-of-the-art reinforcement learning (RL) algorithm, from continuous action space to discrete action s…
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails
Kexin Chen, Guangyong Chen, Junyou Li +2
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amoun…
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19
Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning
Zichuan Lin, Junyou Li, Jianing Shi +3
Learning rational behaviors in open-world games like Minecraft remains to be challenging for Reinforcement Learning (RL) research due to the compound challenge of partial observabi…
Neural Entity Summarization with Joint Encoding and Weak Supervision
Junyou Li, Gong Cheng, Qingxia Liu +4
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions…