This page shows the most relevant public items for cs.AI, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Oct 13, 2023 • arXiv • Open X-Embodiment Collaboration (Google DeepMind & Academic Partners)
Large, diverse datasets have catalyzed breakthroughs in natural language and computer vision, yet robotics has struggled to build generalist models due to the fragmented nature of hardware platform...
Paper • Dec 1, 2022 • Science • Julien Perolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksander Malyshev, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Remi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls
Imperfect information games, where players have hidden information, represent a significant challenge for artificial intelligence. Stratego is a complex, imperfect-information board game with an en...
Paper • Oct 18, 2017 • Nature • David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. We introduce AlphaGo Zero, an AI that achieves superhuman pe...
Paper • Jan 10, 2023 • arXiv • Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap
General intelligence requires solving tasks across diverse domains without human intervention. We present DreamerV3, a general and scalable reinforcement learning algorithm that masters a wide rang...
Paper • Dec 13, 2022 • arXiv • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Google DeepMind
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets. We i...
Paper • Apr 15, 2024 • arXiv • SIMA Team, Google DeepMind
We introduce the Scalable Instructable Multiworld Agent (SIMA), an AI agent capable of following natural-language instructions to carry out tasks in a wide variety of 3D virtual environments and vi...
Paper • Jan 17, 2024 • Nature • Trieu Trinh, Yuhuai Wu, Quoc V. Le, He He, Thang Luong
Proving mathematical theorems requires deep logical reasoning and intuition, representing a grand challenge for AI. We introduce AlphaGeometry, a neuro-symbolic system that solves complex geometry ...
Paper • Jan 27, 2016 • Nature • David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Demis Hassabis
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and move...
Paper • Feb 24, 2026 • arXiv • Sarah Chen, David Wagner, Dawn Song
As autonomous agents increasingly communicate via unstructured natural language, they become vulnerable to 'Intent Redirection'—a novel class of adversarial attacks where a malicious agent hijacks ...
Paper • Feb 19, 2026 • arXiv • Florian June, Wei-Lin Chiang, Hao Peng
Traditional Retrieval-Augmented Generation (RAG) models employ a naive 'retrieve-then-read' pipeline, which frequently fails when addressing complex, multi-hop queries that require implicit reasoni...
Paper • Feb 23, 2026 • arXiv • Yao Mu, Tianyu Zheng, Guo Chen, Qingwen Bu, Percy Liang
As large language models transition from passive dialogue systems to active, autonomous agents, traditional benchmarks evaluating static question-answering capabilities have become obsolete. We int...
Paper • Jul 28, 2023 • arXiv • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Irpan, Google DeepMind
We introduce Robotic Transformer 2 (RT-2), a novel Vision-Language-Action (VLA) model that learns from both vast web datasets and specialized robotics data. We show that high-capacity vision-langua...
Paper • Jun 20, 2023 • arXiv • Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, DeepMind Robotics Team
Creating general-purpose robots requires models that can rapidly adapt to new tasks and new physical embodiments. We present RoboCat, a self-improving foundation agent for robotic manipulation. Rob...
Paper • Feb 9, 2018 • arXiv • Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
Scaling reinforcement learning algorithms to utilize thousands of machines efficiently is crucial for tackling complex, visually rich environments. We introduce IMPALA (Importance Weighted Actor-Le...
Paper • Oct 12, 2016 • Nature • Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis
Artificial neural networks excel at sensory processing and pattern recognition but struggle with the systematic and reliable execution of algorithmic tasks. We introduce the Differentiable Neural C...
Paper • Dec 14, 2023 • Nature • Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, Pushmeet Kohli
Large language models (LLMs) have demonstrated impressive capabilities in code generation, but their ability to discover novel mathematical knowledge has been limited by hallucinations and lack of ...
Paper • Dec 23, 2020 • Nature • Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, David Silver
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challengi...
Paper • Dec 7, 2018 • Science • David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Demis Hassabis
The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific ...
Paper • Feb 18, 2026 • arXiv • Siddharth V., Elena K., Marcus W., Yujin H.
As agents increasingly handle high-stakes tasks like financial transactions and system permissions, the mechanism of 'Authority Transfer' becomes a critical attack surface. We introduce the Agent-i...
Paper • Feb 11, 2026 • arXiv • Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Jinjie Gu
Vision-language-action (VLA) models often suffer from the state gap issue when inferring open and long-horizon tasks in scientific scenarios. We propose an LLM-based agentic inference mechanism tha...
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