4 citations · 7 across the 7 of their papers we have counts for
8 papers
From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning
Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou +4
Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally hand…
Localize-Then-Decide Guarantees for LLM Judgments
Xinyu Li, Yi Zhou, Guanqun Cao +3
Large language models (LLMs) are increasingly used as evaluators to assess output quality and preference alignment, yet providing reliable guarantees of agreement with human judgme…
GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
Zhiyuan Ye, Xiangyu Zhou, Ji Qi +2
Adversarial Training (AT) improves neural network robustness, but most methods train a fixed parameter space from the start. This paper asks whether the order in which parameters b…
RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack
Weichen Dai, Zijie Dai, Zhijie Huang +6
While current large language models (LLMs) demonstrate remarkable linguistic capabilities through training on massive unstructured text corpora, they remain inadequate in leveragin…
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry
Weichen Dai, Yezeng Chen, Zijie Dai +9
Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building a…
Exploring The Neural Burden In Pruned Models: An Insight Inspired By Neuroscience
Zeyu Wang, Weichen Dai, Xiangyu Zhou +2
Vision Transformer and its variants have been adopted in many visual tasks due to their powerful capabilities, which also bring significant challenges in computation and storage. C…
COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs
Xinhe Li, Zhuoying Feng, Yezeng Chen +4
To reduce the experimental validation workload for chemical researchers and accelerate the design and optimization of high-energy-density lithium metal batteries, we aim to leverag…
MInD: Improving Multimodal Sentiment Analysis via Multimodal Information Disentanglement
Weichen Dai, Xingyu Li, Zeyu Wang +4
Learning effective joint representations has been a central task in multi-modal sentiment analysis. Previous works addressing this task focus on exploring sophisticated fusion tech…