activity
20242026
most citedSuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

4 citations · 6 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL2026

RealCLI: Toward a Unified Evaluation of Human-Agent Collaboration for CLI Agents

Yunqi Liu, Peiyu Wang, Bingli Wang +21

Command-line interface (CLI) agents increasingly execute longer-horizon tasks, yet existing benchmarks either assume that user instructions remain fixed throughout execution or eva…

cs.LG2026

Intern-S2-Preview: Scientific Agentic Foundation Model

Lei Bai, Jiaqi Cao, Chiyu Chen +121

Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…

cs.CV2026

COHERENCE: Benchmarking Fine-Grained Image-Text Alignment in Interleaved Multimodal Contexts

Bingli Wang, Huanze Tang, Haijun Lv +5

In recent years, Multimodal Large Language Models (MLLMs) have achieved remarkable progress on a wide range of multimodal benchmarks. Despite these advances, most existing benchmar…

cs.CL2025★ 2 cited

A Survey on Large Language Model Benchmarks

Shiwen Ni, Guhong Chen, Shuaimin Li +11

In recent years, with the rapid development of the depth and breadth of large language models' capabilities, various corresponding evaluation benchmarks have been emerging in incre…

cs.CL2025

KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation

Jiajun Shi, Jian Yang, Jiaheng Liu +26

Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchm…

cs.CL2025★ 4 cited

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

P Team, Xinrun Du, Yifan Yao +94

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledg…

cs.CL2024

Can MLLMs Understand the Deep Implication Behind Chinese Images?

Chenhao Zhang, Xi Feng, Yuelin Bai +18

As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…