1.3k citations · 1.9k across the 22 of their papers we have counts for
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CME-SpectrumBench: Can LLMs Analyze Condensed Matter Spectral Data?
Jin Gene Wong, Anjney Midha, Joseph Mathew Tennyson +3
Large Language Models (LLMs) are presently well assessed by expert-level mathematics and physics benchmarks which focus on theoretical, symbolic reasoning. However, we lack experim…
Interaction-as-a-Judge
Wayne Chi, I-Hung Hsu, Li Chen +6
Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess these applications interactive…
Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards
Yuanhao Ban, I-Hung Hsu, Anastasios Angelopoulos +3
Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal capture…
Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist
Yuanhao Ban, Tong Xie, Sohyun An +6
Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness ben…
DualEval: Joint Model-Item Calibration for Unified LLM Evaluation
Aaron J. Li, Hao Huang, Youngmin Park +6
Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style preference data that better r…