3 citations · 3 across the 4 of their papers we have counts for
5 papers
Text-Centric Post-Training for Omni-Modal Reasoning
Ziyang Cheng, Yuhao Wang, Hongcheng Liu +5
Improving joint audio-visual reasoning in Omni Large Language Models typically incurs substantial data construction and training costs. Our diagnostics reveal multi-hop reasoning d…
AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios
Kaiyuan Chen, Qimin Wu, Taiyu Hou +42
The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow, demonstrating exceptional performance in coding, deep research, and c…
X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs
Rui Ye, Xiangrui Liu, Qimin Wu +4
LLM-based multi-agent systems (MAS) extend the capabilities of single LLMs by enabling cooperation among multiple specialized agents. However, most existing MAS frameworks rely on…
MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems
Rui Ye, Keduan Huang, Qimin Wu +17
LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications. Despite conside…
GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets
Qiming Wu, Zichen Chen, Will Corcoran +2
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP), demonstrating significant capabilities in processing and understanding text data…