A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Zitian Gao, Lynx Chen, Yihao Xiao, et al.
The paper proposes the Universal Reasoning Model (URM), which enhances universal transformers with short convolution and truncated backpropagation, achieving state-of-the-art results on ARC-AGI benchmarks.
Matthew Ho, Chenglei Si, Zhaoxiang Feng, et al.
This paper introduces concept-level memory for LLMs, storing reusable abstract reasoning patterns distilled from solution traces, and demonstrates improved performance on ARC-AGI with test-time continual learning.
Beichen Zhang, Yuhang Zang, Xiao-wen Dong, et al.
Proposes Vision-Language Synergy Reasoning (VLSR) and Modality-Switch Self-Correction (MSSC) to improve abstract reasoning on ARC-AGI by combining visual abstraction with linguistic reasoning.