2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
ETCHR: Editing To Clarify and Harness Reasoning
Beichen Zhang, Yuhong Liu, Jinsong Li +3
Multimodal Large Language Models have advanced visual reasoning, yet a purely textual chain of thought remains a bottleneck for questions that require fine-grained focus or view tr…
Think Visually, Reason Textually: Vision-Language Synergy in ARC
Beichen Zhang, Yuhang Zang, Xiaoyi Dong +4
Abstract reasoning from minimal examples remains a core unsolved problem for frontier foundation models such as GPT-5 and Grok 4. These models still fail to infer structured transf…
Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning
Yuhong Liu, Beichen Zhang, Yuhang Zang +6
Spatial understanding remains a weakness of Large Vision-Language Models (LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement learning with verifiable rewards (R…
Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach
Beichen Zhang, Xiaoxing Wang, Xiaohan Qin +1
Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the fin…
Long-CLIP: Unlocking the Long-Text Capability of CLIP
Beichen Zhang, Pan Zhang, Xiaoyi Dong +2
Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text mod…