Longxuan Yu — accepted AI-conference papers

2026

Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models

Luhan Tang, Longxuan Yu, Shaorong Zhang, Greg Ver Steeg

ICML 2026poster

Discrete diffusion language models (dLLMs) offer a fast and flexible alternative to autoregressive models (ARMs) for discrete sequence generation by performing iterative denoising with parallel updates. Despite these advantages, dLLMs are commonly evaluated using metrics developed for ARMs. Such eva…

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2025

CausalEval: Towards Better Causal Reasoning in Language Models

Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen +2

NAACL 2025long

Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While language models (LMs) can generate rationales for their outputs, their ability to reliably perform causal reasoning remains uncertain, often falling short in…

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