most citedMRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

VeriSkill: A Self-Evolution Framework for Program Verification Skills

Changguo Jia, Tianqi Zhao, Zhiyou Xiao +2

Automating program verification with LLM agents requires generating specifications, annotations, auxiliary lemmas, and tool invocations, all of which depend on reusable skills. A n…

cs.CV2026

OpenWorldLib: A Unified Codebase and Definition of Advanced World Models

DataFlow Team, Bohan Zeng, Daili Hua +39

World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we…

cs.LG2026

Towards Automated Kernel Generation in the Era of LLMs

Yang Yu, Peiyu Zang, Chi Hsu Tsai +11

The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level ha…

cs.IR2025

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation

Zhiyou Xiao, Qinhan Yu, Binghui Li +3

Current research on Multimodal Retrieval-Augmented Generation (MRAG) enables diverse multimodal inputs but remains limited to single-modality outputs, restricting expressive capaci…

cs.LG2025★ 1 cited

MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

Qinhan Yu, Zhiyou Xiao, Binghui Li +3

Recent advances in Retrieval-Augmented Generation (RAG) have significantly improved response accuracy and relevance by incorporating external knowledge into Large Language Models (…