Kaijian Zou — accepted AI-conference papers

2026

LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?

Kaijian Zou, Feiyang Xiong, Yunxiang Zhang, Xinliang Frederick Zhang, Yueqi Ren, Shitanshu Bhushan +3

ICML 2026poster

Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity and ease of verification. Yet, current coding benchmarks face limitations such as lack of exceptionally challenging problems, insufficient test case cove…

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2025

On Many-Shot In-Context Learning for Long-Context Evaluation

Kaijian Zou, Muhammad Khalifa, Lu Wang

ACL 2025long

Many-shot in-context learning (ICL) has emerged as a unique setup to both utilize and test the ability of large language models to handle long context. This paper delves into long-context language model (LCLM) evaluation through many-shot ICL. We first ask: what types of ICL tasks benefit from addit…

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2025

SYNC: A Synthetic Long-Context Understanding Benchmark for Controlled Comparisons of Model Capabilities

Shuyang Cao, Kaijian Zou, Lu Wang

EMNLP 2025

Recently, researchers have turned to synthetic tasks for evaluation of large language models’ long-context capabilities, as they offer more flexibility than realistic benchmarks in scaling both input length and dataset size. However, existing synthetic tasks typically target narrow skill sets such a

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2023

All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

Yujian Liu, Xinliang Frederick Zhang, Kaijian Zou, Ruihong Huang, Nicholas Beauchamp, Lu Wang

EMNLP 2023long main

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the m…

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2023

Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting

Kaijian Zou, Xinliang Frederick Zhang, Winston Wu, Nicholas Beauchamp, Lu Wang

EMNLP 2023short findings

News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via linguistic style and word usage. In this paper, we s…

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