4 papers
Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2
Yu-Ning Qiu, Lin-Feng Zou, Jiong-Da Wang +2
In high-complexity abstract reasoning, a system must infer a latent rule from a few examples or structured observations and apply it to unseen instances. LLMs can express such rule…
ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection
Runzhi Deng, Yundi Hu, Xinshuang Zhang +5
Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-lan…
Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models
Xiao-Wen Yang, Jie-Jing Shao, Lan-Zhe Guo +5
Large Language Models (LLMs) have shown promising results across various tasks, yet their reasoning capabilities remain a fundamental challenge. Developing AI systems with strong r…
Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection
Wen-Chao Hu, Wang-Zhou Dai, Yuan Jiang +1
Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with sym…