arXiv AI August 11, 2026 By Yingying Guo, Zhuoxuan Ju, Ruibo Ming, Ruicheng Feng, Jinjin Gu

Experience-Sensitive Game Learning: A Behavioral Study of Humans and Language Agents

arXiv:2608. 07490v1 Announce Type: cross Abstract: Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from repeated interaction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

3d ago

STRATA: Self-Learning Through Role-Aligned Tiered Agents for Real-Time Strategy Games

arXiv:2609.38881v1 Announce Type: new Abstract: Real-time strategy (RTS) games require agents to coordinate economic development, production and construction, base defense, unit organization, and att...

By Xinhe Tian, Xiaoyue Zhang, Ziyou Zhang, Jiacheng Li, Xiaoqiang Jin, Qianchuan Zhao, Gaochen Cui

llms agents efficiency

arXiv Machine Learning

Jul 31

Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games

arXiv:2607. 27536v1 Announce Type: cross Abstract: Learning a strategic task changes more than what is directly taught: fine-tuning on one game can either enhance or degrade an agent's ability to reason in another.

By Joshua Caiata, Sreepriya Pulyassary, Xiang Li, Kate Larson

llms rag agents fine-tuning

arXiv Machine Learning

Aug 4

Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

arXiv:2608. 01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma.

By Phu Hoa Pham, Duy Minh Dao Sy, Trung Kiet Huynh, Phu Quy Nguyen Lam, Chi Nguyen Tran, Minh Trung Le, Phong Hao Le, Dinh Nam Nguyen, Thien Ky Nguyen Dong, Elias Fernandez Domingos, Le Hong Trang, The Anh Han

llms agents benchmarks safety

arXiv AI

Jul 14

People use fast and flat simulation to reason about new 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.

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

arXiv Machine Learning

Jul 31

Policy Gradient Steering: Interventions from Behavioral Objectives

arXiv:2607. 27574v1 Announce Type: new Abstract: Activation steering has emerged in large language models as a lightweight alternative for dynamically changing a model's behavior at inference time.

By Yoann Poupart, Aur\'elie Beynier, Nicolas Maudet

llms reinforcement-learning

arXiv AI

Jul 2

Large language models replicate and predict human cooperation across experiments in game theory

arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.

By Andrea Cera Palatsi, Samuel Martin-Gutierrez, Ana S. Cardenal, Max Pellert

llms agents safety

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STRATA: Self-Learning Through Role-Aligned Tiered Agents for Real-Time Strategy Games

Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games

Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

People use fast and flat simulation to reason about new games

Policy Gradient Steering: Interventions from Behavioral Objectives

Large language models replicate and predict human cooperation across experiments in game theory