A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Cheng Qian, Wenting Zhao, Liangwei Yang, et al.
This paper introduces test-time strong-to-weak capability transfer via inference-time harnesses, nearly doubling target model performance on Theory-of-Mind benchmarks without parameter updates.
Tiantian Guo, Tongpo Zhang, Eng Gee Lim, et al.
This review traces wavelet theory from construction to applications, highlighting rational wavelet transform advantages and the evolution of Wavelet Neural Networks.
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This review provides a practical introduction to neural operators for scientific computing, covering theory, applications, and their role as surrogates in forward modeling.
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This paper describes policy iteration for multi-agent reinforcement learning, focusing on an algorithm called Interconnected Learning Automata for Markov Games (MG-ILA).
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A survey organizing mechanistic interpretability across three dimensions, outlining how it bridges theory and practice for understanding AI systems.
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This paper systematically examines the integration of game theory and multi-agent systems, presenting a comprehensive review of development status and future prospects for modeling strategic interactions among autonomous agents.
Shaoguang Mao, Yuzhe Cai, Yan Xia, et al.
This paper introduces ALYMPICS, a framework for evaluating LLM agents' strategic decision-making in game-theoretic scenarios, revealing their capabilities and limitations in human-like interactions.
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This paper argues that AI agents will evolve into modular research infrastructure that reliably connects databases, theory, experiments, and feedback, rather than remaining hype-driven autonomous systems.
James B. Simon, D. Kunin, Alexander Atanasov, et al.
This paper argues that a scientific theory of deep learning is emerging, characterized by five growing bodies of work that form a 'learning mechanics' perspective.
Adam Kostka, J. Chudziak
This paper introduces a novel multi-agent architecture integrating Theory of Mind, BDI-style internal beliefs, and symbolic solvers to improve collaborative decision-making in LLM-based systems.
James B. Grace, Donald R. Schoolmaster, Glenn R. Guntenspergen, et al.
This paper presents guidelines for a third-generation structural equation modeling framework based on graph theory, causal graphs, and probabilistic reasoning.
Stanislav Ivanov, Mohammad Soliman, Aarni Tuomi, et al.
This paper applies the Theory of Planned Behaviour to model how perceived benefits, risks, and TPB factors drive generative AI adoption among lecturers and students in higher education.