activity
20242026
collaborators

9 papers

cs.CV2026

Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation

Boyuan Xiao, Bohong Chen, Yumeng Li +3

In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs & VLAs) are powerful tools wi…

cs.CV2026

FreeFuse: Multi-Subject LoRA Fusion via Adaptive Token-Level Routing at Test Time

Yaoli Liu, Yao-Xiang Ding, Kun Zhou

This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies…

cs.CV2025

Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models

Jingming Liu, Yumeng Li, Boyuan Xiao +5

Under pure textual modality, Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning tasks by decomposing them into simpler sub-problems. However, Mu…

cs.LG2025

Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models

Yifei Peng, Yaoli Liu, Enbo Xia +5

We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves bot…

cs.CV2025

ROVI: A VLM-LLM Re-Captioned Dataset for Open-Vocabulary Instance-Grounded Text-to-Image Generation

Cihang Peng, Qiming Hou, Zhong Ren +1

We present ROVI, a high-quality synthetic dataset for instance-grounded text-to-image generation, created by labeling 1M curated web images. Our key innovation is a strategy called…

cs.IR2025

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation

Jingming Liu, Yumeng Li, Wei Shi +3

Recent studies have proposed leveraging Large Language Models (LLMs) as information retrievers through query rewriting. However, for challenging corpora, we argue that enhancing qu…