5 papers
Thinking with Patterns: Breaking the Perceptual Bottleneck in Visual Planning via Pattern Induction
Yichang Jian, Boyuan Xiao, Zhenyuan Huang +2
Planning from raw visual input remains a significant challenge for current Vision-Language Models (VLMs), when the complexity of input is beyond their one-step perception capabilit…
Can AI Understand the Language of Origami?
Naaisha Agarwal, Yihan Wu, Xin Guan +10
Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason about the generative mechanisms and constr…
Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models
Yifei Peng, Yaoli Liu, Enbo Xia +5
We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves bot…
Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning
Yu Jin, Jingming Liu, Zhexu Luo +5
Visual generative abductive learning studies jointly training symbol-grounded neural visual generator and inducing logic rules from data, such that after learning, the visual gener…
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Yifei Peng, Zijie Zha, Yu Jin +5
Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abducti…