1 citations · 1 across the 8 of their papers we have counts for
8 papers
Test-Time Computing for Referring Multimodal Large Language Models
Mingrui Wu, Hao Chen, Jiayi Ji +5
We propose ControlMLLM++, a novel test-time adaptation framework that injects learnable visual prompts into frozen multimodal large language models (MLLMs) to enable fine-grained r…
MICON-Bench: Benchmarking and Enhancing Multi-Image Context Image Generation in Unified Multimodal Models
Mingrui Wu, Hang Liu, Jiayi Ji +2
Recent advancements in Unified Multimodal Models (UMMs) have enabled remarkable image understanding and generation capabilities. However, while models like Gemini-2.5-Flash-Image s…
From Atoms to Trees: Building a Structured Feature Forest with Hierarchical Sparse Autoencoders
Yifan Luo, Yang Zhan, Jiedong Jiang +4
Sparse autoencoders (SAEs) have proven effective for extracting monosemantic features from large language models (LLMs), yet these features are typically identified in isolation. H…
Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs
Chi Zhang, Wenxuan Ding, Jiale Liu +3
Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks. However, their robustness against textual misinform…
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…
Param 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…
Vision Calorimeter for Anti-neutron Reconstruction: A Baseline
Hongtian Yu, Yangu Li, Mingrui Wu +8
In high-energy physics, anti-neutrons () are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties pr…
LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding
Mingrui Wu, Sheng Cao
Recently embedding-based retrieval or dense retrieval have shown state of the art results, compared with traditional sparse or bag-of-words based approaches. This paper introduces…