Mingrui Wu

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20242026

most citedLLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding

1 citations · 1 across the 8 of their papers we have counts for

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cs.CV2025

From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs

Mingrui Wu, Zhaozhi Wang, Fangjinhua Wang +3

While Multimodal Large Language Models (MLLMs) have achieved impressive performance on semantic tasks, their spatial intelligence--crucial for robust and grounded AI systems--remai…

cs.CL2025

Param $Δ$ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost

Sheng Cao, Mingrui Wu, Karthik Prasad +2

The post-training phase of large language models is essential for enhancing capabilities such as instruction-following, reasoning, and alignment with human preferences. However, it…

From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs

ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost

Param $Δ$ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost