A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Stephen J. Mellor, Marc Balcer, Ivar Jacoboson
This book defines Executable UML, a profile of UML that enables developers to model subject matters at a higher abstraction level for execution and compilation into complete systems.
Kangsan Kim, Min-Bong Kang, Taeil Kim, et al.
This paper introduces Memory Transfer Learning (MTL), showing that cross-domain memory pools improve coding agent performance by 3.7% through transfer of meta-knowledge, with abstraction level dictating transferability.
Zhiyu Li, Shichao Song, Hanyu Wang, et al.
MemOS is a memory operating system for LLMs that elevates memory to a first-class resource, unifying parametric, activation, and plaintext memory via the MemCube abstraction for lifecycle management and cross-modal integration.
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The paper discusses the shift from traditional storage systems to in situ AI that provides multi-level abstractions for accessing information content from raw bits to learned functions capturing distributions.
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This paper proposes a theoretical foundation for mechanistic interpretability, centering causality as the core principle underlying interpretability methods.
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ONELIFE learns symbolic world models from a single unguided exploration episode using programmatic representations and stochastic abstractions.
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This paper introduces variable-length latent world models that improve long-horizon planning by enabling flexible temporal abstraction in latent space.
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.
Tom Dietterich
Introduces the MAXQ decomposition for hierarchical reinforcement learning, enabling faster convergence via state abstractions and recursively optimal policies.