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cs.CV2025
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…
cs.CV2025
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…
cs.CL2025
BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
Beichen Zhang, Yuhong Liu, Xiaoyi Dong +6
Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed…