We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle in reinforcement learning. CoinRun strikes a desirable balance in complexity: the environment is simpler than traditional platformer games like Sonic the Hedgehog but still poses a worthy generalization challenge for state of the art algorithms.
arXiv:2604. 02721v2 Announce Type: replace Abstract: Competitive programming remains one of the last few human strongholds in coding against AI.
By DeepReinforce Team, Xiaoya Li, Guoyin Wang, Songqiao Su, Chris Shum, Jiwei Li
arXiv:2609.09094v1 Announce Type: new
Abstract: Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever...
By Raphael Boige, Amine Boumaza, Bruno Scherrer
arXiv:2510. 11503v2 Announce Type: replace-cross Abstract: Games have long been a microcosm for studying planning and reasoning in both natural and artificial intelligence (AI), often focusing on expert-level or even super-human play.
By Katherine M. Collins, Cedegao E. Zhang, Lionel Wong, Mauricio Barba da Costa, Graham Todd, Adrian Weller, Samuel J. Cheyette, Thomas L. Griffiths, Joshua B. Tenenbaum
UCLA Professor Ernest Ryu and GPT-5 solved a key question in optimization theory, showcasing AI’s role in accelerating mathematical discovery.
arXiv:2608. 09128v1 Announce Type: cross Abstract: LLM agents are increasingly deployed in multi-agent social settings where they must cooperate, negotiate, and adapt to other agents.
By Keyu He, Xuhui Zhou, Maarten Sap
arXiv:2606. 17847v1 Announce Type: new Abstract: WallGo is a recently introduced strategic board game popularized by the 2025 Netflix series The Devil's Plan.
By Hsing-Yu Chen, J\'er\^ome Arjonilla, I-Chen Wu, Ti-Rong Wu
The study investigates how algorithmic advice influences human behavior in a Cournot quantity competition. Participants receiving individualized equilibrium recommendations converged quickly to the stable equilibrium, whereas those given strategically biased, downward‑biased advice experienced persistent underproduction and higher profits, resembling tacit collusion. The results show that algorithmic signals can shape coordination without explicit communication, highlighting the importance of careful design and oversight in competitive markets.
By Tobias R. Rebholz, Maxwell Uphoff, Christian H. R. Bernges, Florian Scholten
arXiv:2606. 18119v1 Announce Type: new Abstract: To assess the ability of current AI systems to correctly solve research-level mathematics problems, we tested several AI systems on a set of ten problems in a broad range of mathematical fields; these problems arose naturally in the research process of the contributors.
By Mohammed Abouzaid, Nikhil Srivastava, Rachel Ward, Lauren Williams
arXiv:2508. 11874v2 Announce Type: replace-cross Abstract: Designing polynomial-time algorithms for approximate Nash equilibria (ANE) with provable worst-case guarantees is a fundamental open problem in algorithmic game theory.
By Hanyu Li, Dongchen Li, Xiaotie Deng
arXiv:2609.06816v1 Announce Type: new
Abstract: Decision-time search in perfect and imperfect information games with enumerable belief states are effective methods for game AI. Collectible card games...
By Dustin Rubin