2 citations · 3 across the 13 of their papers we have counts for
4 papers · 1 filter
MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs
Feilong Chen, Yijiang Liu, Yi Huang +5
We propose MindVL, a multimodal large language model (MLLMs) trained on Ascend NPUs. The training of state-of-the-art MLLMs is often confined to a limited set of hardware platforms…
MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents
Xijia Tao, Yihua Teng, Xinxing Su +7
Existing multimodal browsing benchmarks often fail to require genuine multimodal reasoning, as many tasks can be solved with text-only heuristics without vision-in-the-loop verific…
VisuRiddles: Fine-grained Perception is a Primary Bottleneck for Multimodal Large Language Models in Abstract Visual Reasoning
Hao Yan, Xingchen Liu, Hao Wang +11
Recent strides in multimodal large language models (MLLMs) have significantly advanced their performance in many reasoning tasks. However, Abstract Visual Reasoning (AVR) remains a…
DocPuzzle: A Process-Aware Benchmark for Evaluating Realistic Long-Context Reasoning Capabilities
Tianyi Zhuang, Chuqiao Kuang, Xiaoguang Li +4
We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-lev…