1 citations · 1 across the 3 of their papers we have counts for
4 papers · 1 filter
RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation
Qi Cheng, Shengyu Chen, Wei Cheng +4
Agentic workflows enable large language models (LLMs) to solve complex tasks by coordinating reasoning, tool use, and verification. However, a workflow optimized for an entire task…
OOPMAS: Object-Oriented Multi-Agent Systems for Query-Level Workflow Generation
Qi Cheng, Shengyu Chen, Wei Cheng +4
Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question answering. However, existing m…
ST-Bench: A Spatial-Temporal Benchmark for Multi-Agent System Generation on Scientific Research Tasks
Qi Cheng, Rongchao Dong, Shengyu Chen +8
The rapid progress of LLM-based multi-agent systems (MAS) has shown that they largely outperform single agents on coding, math, and QA tasks, where executable tests provide a binar…
LLM-based Evaluation Policy Extraction for Ecological Modeling
Qi Cheng, Licheng Liu, Qing Zhu +4
Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, capturing carbon-nitrog…