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20242026
most citedMM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Code as Representation: A Compilable Parsing Paradigm for Academic Documents

Rihui Jin, Jun Wang, chengyuan zhu +11

Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine use. For M…

cs.AI2026

EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization

Xinbang Dai, Zheyu Xin, Huikang Hu +7

Large Reasoning Models (LRMs) often suffer from overthinking due to redundant verification steps. Existing approaches for mitigating overthinking, such as fast-slow thinking switch…

cs.AI2025

Can LLMs Solve ASP Problems? Insights from a Benchmarking Study (Extended Version)

Lin Ren, Guohui Xiao, Guilin Qi +2

Answer Set Programming (ASP) is a powerful paradigm for non-monotonic reasoning. Recently, large language models (LLMs) have demonstrated promising capabilities in logical reasonin…

cs.CL2025

OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases

Yongrui Chen, Zhiqiang Liu, Jing Yu +21

Large Language Models (LLMs) have demonstrated substantial progress on reasoning tasks involving unstructured text, yet their capabilities significantly deteriorate when reasoning…

cs.CV2024★ 3 cited

MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities

Weihao Yu, Zhengyuan Yang, Lingfeng Ren +7

MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluati…