Author

Yu-Han Wu

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#artificial intelligence Jun 2025

Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design

This study reveals that the Deepseek-R1-Distill series models are subject to significant fluctuations caused by various factors, making their claimed performance improvements difficult to reproduce reliably.

Yong-Fu Zhu, Lin Sun, Jinzhu Wu et al. · arXiv.org · 4 citations · ⚡2

#artificial intelligence Preprint Aug 2026

ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions

Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an olympiad-style benchmark from thirteen public science competitions in physics, chemistry, and biology, including IPhO and IChO 2025--2026, IBO 2023, USAPhO 2026, and USN...

Guangxiang Zhao, Qi-Long Shi, Xusen Xiao et al. · 0 citations

Review Aug 2026

Intern-S2-Preview: Scientific Agentic Foundation Model

Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.

Lei Bai, Jiaqi Cao, Chiyu Chen et al. · 3 citations