activity
20232025
most citedJARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

6 citations · 13 across the 6 of their papers we have counts for

collaborators

6 papers

cs.MA2025

Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation

Chenxu Wang, Yonggang Jin, Cheng Hu +7

Adapting a single agent to a new multi-agent system brings challenges, necessitating adjustments across various tasks, environments, and interactions with unknown teammates and opp…

cs.AI2024★ 1 cited

GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models

Leyan Wang, Yonggang Jin, Tianhao Shen +9

As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their pe…

cs.CL2024★ 5 cited

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

Yuelin Bai, Xinrun Du, Yiming Liang +19

Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instructi…

cs.AI2024

Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction

Yonggang Jin, Ge Zhang, Hao Zhao +7

Developing a generalist agent is a longstanding objective in artificial intelligence. Previous efforts utilizing extensive offline datasets from various tasks demonstrate remarkabl…

cs.AI2023★ 6 cited

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Zihao Wang, Shaofei Cai, Anji Liu +9

Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle cer…

cs.LG2023★ 1 cited

Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork

Yonggang Jin, Chenxu Wang, Tianyu Zheng +5

Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-ma…