arXiv:2508. 13213v4 Announce Type: replace Abstract: Strategic decision-making requires balancing immediate opportunities against long-term objectives: a tension fundamental to competitive environments.
arXiv:2607. 00190v1 Announce Type: cross Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games.
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.
arXiv:2605. 22748v2 Announce Type: replace-cross Abstract: Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces.
The paper presents a systematic mapping of recent chess research involving humans, engines, neural and reinforcement‑learning systems, large language models (LLMs), and hybrid approaches. It identifies 84 core study families and classifies them by agent type, strategic‑reasoning stages, and evaluation dimensions, highlighting a strong focus on situation assessment, evaluation, and action selection while noting gaps in planning, explanation, metacognition, and human–AI collaboration. The study also distinguishes hybrid systems by integration timing and cautions that improved human performance in evaluations does not automatically imply human–AI synergy.
SpeedrunBench is a new benchmark that tests large language model agents on their ability to develop and refine strategies for completing nine different video games as quickly as possible. The benchmark requires agents to repeatedly improve, reflect, and reason over long action horizons to beat both themselves and other players. Experiments show that while agents can approach human records on simple platformers, they lag behind humans on longer, more complex games within realistic resource limits.
arXiv:2606. 15503v1 Announce Type: new Abstract: In this paper, we introduce the concept of synthetic counteradaptation, a process where human and AI systems co-evolve by adapting to each other's strategies and behaviors.
arXiv:2605.09965v3 Announce Type: replace Abstract: The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from...
arXiv:2505. 16388v2 Announce Type: replace Abstract: The serious games between humans and AI have only just begun.
arXiv:2610.31726v1 Announce Type: new Abstract: Effective human-machine teaming requires agents that coordinate with unpredictable human partners. Reinforcement learning from live human feedback or l...
The paper proposes a developmental framework for autonomous artificial agents that emphasizes learning social norms and alignment through direct interaction with dynamic environments. It argues that intrinsic motivations such as curiosity and competence can guide exploration, but also complicate alignment with human goals. By drawing parallels to child development, the authors suggest that regulatory sandboxes serve as pedagogical spaces where agents gradually acquire moral agency and adapt their behaviors through experience and cooperation.
arXiv:2606. 26267v1 Announce Type: new Abstract: Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess.