5 papers
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
Weixin Chen, Antonio Vergari, Han Zhao
Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper…
Causal Neural Probabilistic Circuits
Weixin Chen, Han Zhao
Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting the class label from the concept predi…
Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering
Boyuan Liu, Feng Ji, Jiayan Nan +4
This paper introduces Omne-R1, a novel approach designed to enhance multi-hop question answering capabilities on schema-free knowledge graphs by integrating advanced reasoning mode…
MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization
Xingxuan Li, Yao Xiao, Dianwen Ng +15
Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains,…
Long Term Memory: The Foundation of AI Self-Evolution
Xun Jiang, Feng Li, Han Zhao +12
Large language models (LLMs) like GPTs, trained on vast datasets, have demonstrated impressive capabilities in language understanding, reasoning, and planning, achieving human-leve…