A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Leslie Lamport
Introduces the Temporal Logic of Actions (TLA), a simple yet powerful logic for specifying and reasoning about concurrent systems, where systems and properties are expressed in the same logic.
Cecilia Ka Yuk Chan
Proposes an AI Ecological Education Policy Framework with pedagogical, governance, and operational dimensions based on surveys of 457 students and 180 teachers in Hong Kong universities.
Unknown
This paper examines how to benchmark biomedical foundation models and questions their epistemological value in biomedical research.
M. Buyl, Alexander Rogiers, Sander Noels, et al.
This paper reveals that LLMs' ideological portrayals of politically relevant figures reflect the worldviews of their creators, varying across geopolitical regions and languages.
Saman Marandi, Yu-Shu Hu, Mohammad Modarres
This paper presents a framework for automated construction of Dynamic Master Logic models as knowledge graphs from technical documentation using retrieval-augmented large language models, validated on a nuclear reactor system.
Obed Junias, Maria Leonor Pacheco
Introduces a framework that decomposes compound answer options into atomic judgments and uses an operator-constrained ILP to compose calibrated scores, improving Macro-F1 on logical reasoning benchmarks.
Unknown
This paper explores optimal training strategies for protein language models, providing guidance on compute-efficient scaling in biological research.
Marina Yusoff, Toto Haryanto, Heru Suhartanto, et al.
This structured review analyzes deep learning methods, especially CNNs and hybrids, for histopathological breast cancer image classification, highlighting challenges like overfitting and data imbalance.
Divneet Mandair, Jorge S. Reis-Filho, Alan Ashworth
This review examines recent deep learning applications in breast cancer histology that yield biological insights and novel biomarkers, spanning genomic extraction to stroma-based recurrence prediction.
Unknown
This paper introduces a method for learning symbolic world models from real-world observations to enable long-horizon planning in unseen environments.
Hongming Chen, Ola Engkvist, Yinhai Wang, et al.
This paper reviews the first wave of deep learning applications in drug discovery, covering bioactivity prediction, de novo molecular design, synthesis prediction, and biological image analysis.
Pulkit Verma, Ngoc La, Anthony Favier, et al.
PDDL-Instruct instruction tuning framework enhances LLMs' symbolic planning via logical chain-of-thought reasoning, achieving 94% planning accuracy.