Papers by Runqi Yang

3 papers
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)

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Challenge: Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization.
Approach: They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation.
Outcome: The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts.
V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

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Challenge: Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach: They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome: The proposed framework provides quantitative analyses and superior deciphering capability.
Simple and Effective Text Matching with Richer Alignment Features (P19-1)

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Challenge: Existing models only use a single inter-sequence alignment layer to make full use of this process.
Approach: They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets .
Outcome: The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models.

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