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 • Apr 18, 2025 • arXiv • Maciej Besta, Nils Blach, Torsten Hoefler
While Chain-of-Thought and Tree-of-Thoughts prompting greatly enhance LLM reasoning, they strictly enforce linear or hierarchical cognitive paths. We introduce Graph of Thoughts (GoT), a novel cogn...
Paper • Sep 10, 2025 • arXiv • Yao Mu, Tianyu Zheng, Percy Liang, Dan Hendrycks
As multi-agent swarms are deployed in open-ended web environments, standard static benchmarks fail to capture their vulnerability to dynamic threats and deceptive actors. We introduce AgentBench-20...
Paper • Jul 15, 2025 • arXiv • Kevin Zhu, Lara Croft, Julian Bao
Evaluating the long-horizon planning and adaptability of Agentic AI in the real world is fraught with safety and cost limitations. We establish Minecraft as the premier sandbox for open-ended agent...
Paper • Aug 8, 2025 • arXiv • Jian Lu, Percy Liang, Chelsea Finn
While scaling laws for single-model LLMs are well established, the relationship between the number of collaborating agents and overall system performance remains poorly understood. This paper inves...
Paper • Mar 21, 2025 • arXiv • Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Kuhu Syal
Automated theorem proving requires a blend of creative intuition and rigorous logical deduction, a combination that eludes pure deep learning models. We propose a Neurosymbolic Agentic AI framework...
Paper • Oct 29, 2025 • arXiv • Mohamad Abou Ali, Fadi Dornaika
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbol...
Paper • Dec 22, 2025 • arXiv • Percy Liang, Thomas K. V., Eleanor Rigby
Aligning multi-agent systems via traditional human feedback is intractable due to the sheer volume and speed of agent-to-agent interactions. We introduce a novel alignment framework utilizing Inver...
Paper • Feb 20, 2026 • arXiv • Sethuraman et al., AAMAS 2026 Main Track
We propose Mixture-of-Reasoning Agents (MiRA), a zero-shot multimodal framework that decomposes reasoning across three specialized agents: Visual Analyzing, Text Comprehending, and Judge. By consol...
Paper • Feb 19, 2026 • arXiv • Google DeepMind Team
We introduce Gemini 3.1 Pro, an enhanced version of the Gemini 3 series optimized for rigorous logic and long-horizon problem solving. Built on a hybrid architecture that fuses linear attention wit...
Paper • Jun 21, 2016 • arXiv • Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané
Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper, we discuss one such poten...
Paper • May 9, 2023 • OpenAI • Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, William Saunders
Understanding the internal mechanisms of massive language models is a critical bottleneck for AI safety and alignment. Given the billions of parameters in modern models, manual human inspection of ...
Paper • Dec 16, 2021 • arXiv • Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman
We introduce a method for fine-tuning language models to interact with a text-based web browser to answer open-ended questions. This model, WebGPT, searches the web, navigates through links, and sy...
Paper • Dec 13, 2019 • arXiv • Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Ilya Sutskever, et al.
We present OpenAI Five, a system of five neural networks that learned to play the highly complex, imperfect-information esports game Dota 2 entirely through self-play. Dota 2 involves long time hor...
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 ...
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