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Yinghui Li

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TangramPuzzle: Evaluating Multimodal Large Language Models with Compositional Spatial Reasoning

TangramPuzzle is introduced, a benchmark comprising 668 validated configurations and 1,336 instances for evaluating compositional spatial reasoning under geometric constraints, and the Tangram Construction Expression is formulated to encode tangram configurations with machine-verifiable geometric specifications.

Daixian Liu, Jiayi Kuang, Ying-Hui Li et al. · 4 citations
Jul 2026

Thinking in Video: Can Video Generators Really Reason About the Real World?

The Causal-Generative Dual-Judge (CGDJ) is introduced, auditing World Model Consistency from two perspectives, and analysis exposes audio-visual misalignment, where models verbalize correct causal logic more reliably than they render it, challenging the"world simulator"narrative.

Yongheng Zhang, Guang Yang, Ruihan Hou et al. · 1 citation
#computer vision Preprint Aug 2026

Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning

Latent-OPD is proposed, which augments OPD with trajectory-level latent distillation and introduces a progressive teacher-lookahead strategy, which aligns middle-to-late student layers with increasingly deeper teacher layers, establishing Latent-OPD as a highly effective approach to frame-efficient video reasoning.

Aoni Shen, Yongheng Zhang, Ying-Hui Li et al. · 2 citations
#natural language process... Preprint Aug 2026

INSPIRE: An Internalize-Then-Improve Approach for Example-Driven Mathematical Reasoning

INSPIRE is an Internalize-Then-Improve approach combining Reference-Guided Student Internalization (RGSI), which produces high-quality preference candidates under the policy model's own distribution, with a stage-wise rubric preference training strategy that decomposes learning into method-oriented and correctness-orie...

Shuai Wang, Jiayi Kuang, Ying-Hui Li et al. · 0 citations
Preprint Aug 2026

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs'Agentic Mathematical Capabilities

Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.

Jiayi Kuang, Ying-Hui Li, Yun-Ze Song et al. · 0 citations

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