Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Pre-trained on extensive text and image corpora, current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks. However, their performance is still lacking in physical domains that require understanding diagrams with complex physical structures and quantitative analysis based on multi-modal information. To address this, we develop a new framework, named **M**ulti-Modal Scientific Re**A**soning with **P**hysics Perception and **S**imulation (**MAPS**) based on an MLLM. MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator. The PPM module is obtained by fine-tuning a visual language model using carefully designed synthetic data with paired physical diagrams and corresponding simulation language descriptions. At the inference stage, MAPS integrates the simulation language description of the input diagram provided by PPM and results obtained through a Chain-of-Simulation process with MLLM to derive the underlying rationale and the final answer. Validated using our collected college-level circuit analysis problems, MAPS significantly improves reasoning accuracy of MLLM and outperforms all existing models. The results confirm MAPS offers a promising direction for enhancing multi-modal scientific reasoning ability of MLLMs. We will release our code, model and dataset used for our experiments upon publishing of this paper.

As the mechanism of the effective traditional Chinese medical science treatment remains confounding, researchers seek to analyze the therapies using traditional Chinese medicine (TCM) with advanced techniques. In this paper we take advantage of the technique of data mining with the software SPSS (Statistical Product and Service Solutions), in particular the Apriori algorithm for association rules mining (ARM), which discovers the following relationship by iteratively analyzing large datasets of TCM medical records. After analyzing the database, we are likely to reveal the mysterious interrelationships among TCM syndromes, clinical symptoms and curative Chinese herbal medicine. Here we list the principals, steps and pseudo-codes of Apriori algorithm, and then apply Apriori algorithm to analyze Chinese traditional medical cases of headache. Through verification, the Apriori algorithm proves effective in helping traditional Chinese doctors decide what to prescribe when facing various patients with headache. Finally we offer some improvement of Apriori algorithm for ARM to boom its effectiveness.