2 papers
cs.AI2026
AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters
Hanjun Luo, Zhimu Huang, Sylvia Chung +6
Text-to-image (T2I) systems increasingly rely on upstream prompters, either humans or multimodal large language models (MLLMs), to translate user intent into detailed prompts. Yet…
cs.SE2026
CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding
Hanjun Luo, Chiming Ni, Jiaheng Wen +9
LLM-powered coding agents are reshaping the development paradigm. However, existing evaluation systems, neither traditional tests for humans nor benchmarks for LLMs, fail to captur…