Rubing Chen — accepted AI-conference papers

2026

OptScale: Probabilistic Optimality for Inference-time Scaling

Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei

AAAI 2026technical

Inference-time scaling has emerged as a powerful technique for enhancing the reasoning performance of Large Language Models (LLMs). However, existing approaches often rely on heuristic strategies for parallel sampling, lacking a principled foundation. To address this gap, we propose a probabilistic

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2025

Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond

Rubing Chen, Jiaxin Wu, Jian Wang, Xulu Zhang, Wenqi Fan, Chenghua Lin +2

EMNLP 2025

The increasing demand for domain-specific evaluation of large language models (LLMs) has led to the development of numerous benchmarks. These efforts often adhere to the principle of data scaling, relying on large corpora or extensive question-answer (QA) sets to ensure broad coverage. However, the

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