A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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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.
Shigeru Kondo, Takashi Miura
This review explains the reaction-diffusion model as a framework for biological pattern formation, highlighting its relevance and applications in developmental biology.
Gunnar Carlsson
This paper introduces topological data analysis (TDA) as a framework for extracting qualitative, large-scale structure from high-dimensional, noisy point clouds using geometry and topology.
Richard C. Mohs, Nigel H. Greig
This paper outlines the drug discovery and development process to help basic scientists frame their research for effective translation to clinical use.
Dolev Bluvstein, Simon J. Evered, Alexandra A. Geim, et al.
A programmable logical quantum processor using reconfigurable neutral-atom arrays demonstrates error-corrected qubits with up to 280 physical qubits and improved algorithmic performance.
DM Anisuzzaman, Jeffrey G. Malins, Paul A. Friedman, et al.
This review outlines major methodological approaches and steps for fine-tuning LLMs for specialized use cases, with examples from medical subspecialties.
Jundong Xu, Hao Fei, Liangming Pan, et al.
Proposes SymbCoT, a fully LLM-based framework integrating symbolic expressions and logic rules with Chain-of-Thought prompting to enhance logical reasoning.
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Introduces GSM-Symbolic, a benchmark using symbolic templates to reveal LLMs rely on pattern recognition rather than genuine logical reasoning.
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Logic-RL uses rule-based RL on synthetic logic puzzles to teach LLMs advanced reasoning that generalizes to math benchmarks.
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RAFT trains LLMs to ignore irrelevant documents, cite relevant passages, and reason logically for open-book QA.