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 • 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...
Paper • Feb 12, 2026 • arXiv • Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John, Surya Krishnapillai, Kurt Keutzer, Amir Gholami
Current WebAgents struggle with long-horizon tasks and complex navigation. We propose an agentic scaling framework that increases compute at test-time through iterative trajectory pruning and rewar...
Paper • Apr 16, 2025 • arXiv • Yannick LeCun, Antoine Bordes, Elena Rossi, Marcus Thorne
The propensity of large language models to hallucinate facts severely limits their deployment in autonomous agentic workflows where verification is costly. This paper introduces 'Agentic Debate,' a...
Paper • Jun 21, 2025 • arXiv • Thomas K. V., Eleanor Rigby, M. Hasan
Traditional AI alignment techniques like RLHF are insufficient for Agentic AI, as autonomous systems frequently encounter novel edge cases in open-ended environments that were absent from their tra...
Paper • Dec 18, 2025 • arXiv • Anonymous Consortium
As agentic AI systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. This paper unifies the rapidly expanding resea...
Paper • May 15, 2025 • arXiv • Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review critically distinguishes between traditional 'AI Agents' and the broader paradigm of 'Agentic AI', offering a structured conceptual taxonomy and application mapping. The authors argue t...
Paper • Feb 22, 2026 • arXiv • Tatsunori Hashimoto, Percy Liang, Xinyi Wang
Current web agents rely heavily on underlying HTML DOM structures, making them brittle to website updates and entirely incapable of navigating dynamic, canvas-based, or non-web applications. We pro...
Paper • Feb 19, 2026 • arXiv • Sergey Levine, Chelsea Finn, Marcus Sterling
While digital agents have mastered software-based orchestration, integrating these cognitive loops with physical robotics remains a challenge. We present a unified framework that closes the 'Agenti...
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