The paper "The Geometry of Empowerment" investigates how empowerment—an agent’s capacity to control its environment—relates to structurally central states that afford broad future access. By linking empowerment maximization with skill‑learning techniques, the authors develop new geometric frameworks for interpreting and analyzing empowerment. Their analyses resolve long‑standing questions about the relationship between empowerment and structural centrality, and they uncover differences between information‑based and reward‑based geometries, offering theoretical insights for scalable empowerment‑maximization methods.
By Catherine Ji, Vivek Myers, Sergey Levine, Benjamin Eysenbach
arXiv:2604.21155v2 Announce Type: replace
Abstract: Intrinsic motivations are receiving increasing attention, i.e. behavioral incentives that are not engineered, but emerge from the interaction of an...
By Tristan Shah, Ilya Nemenman, Daniel Polani, Stas Tiomkin
The paper surveys the evolving field of agent skill evaluation and evolution, emphasizing the shift from isolated skill creation to automated, evaluation-driven development. It categorizes skill evolution into four paradigms—execution feedback, trajectory distillation, compression, and reinforcement learning—and analyzes six skill-centric benchmark categories to highlight gaps and trade-offs. The authors outline open research directions for building generalizable, efficient, and verifiably safe skill ecosystems.
By Kexin Ding, Yang Zhou, Can Jin, Feng Tong, Mu Zhou, Dimitris N. Metaxas
arXiv:2511. 04177v2 Announce Type: replace Abstract: Personal AI agents are increasingly deployed in shared environments, where their actions affect not just the primary user they are assisting, but bystanders who never consented to being affected by the system.
By Claire Yang, Claire Jie Zhang, Maya Cakmak, Max Kleiman-Weiner
The paper introduces Agentic University (AU), a Human‑Participatory Multi‑Agent Organization that lets learners act as organization members and collaborate with specialized agents to build a revisable Capability Program. This program links personal goals, multimodal learning activities, and layered assessment, using learner feedback and performance to guide future support. Evaluation shows that AU improves program organization, assessment design, and revision quality, yielding higher mean assessment gains and stronger cognitive support compared to baseline.
By Jingru Fan, Tianle Zhou, Yunjie Zhou, Zihao He, Yixuan Zhou, Kezhao Wu, Xinyu Zheng, Xiyuan Wang, Yuheng Wang, Zhi Li, Songhua Liu, Weinan E, Chen Qian
arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.
By Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby
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.
By Marica Notte, Ludovica Marinucci, Vieri Giuliano Santucci
arXiv:2609.40137v1 Announce Type: cross
Abstract: We present Game-Guided Skill Discovery (GGSD), a framework that uses self-play in games to discover motor skills that are directly playable by humans...
By Seungeun Rho, Jeonghwan Kim, Xue Bin Peng, Sehoon Ha
arXiv:2606. 03692v1 Announce Type: new Abstract: Recent AI agents can flexibly invoke skills to solve complex tasks, but their long-term improvement is fundamentally constrained by a lack of systematic skill construction, accumulation, and transfer.
By Yuan Xiong, Ziqi Miao, Qian Chen, Lijun Li, Yequan Wang, Shizhu He, Jun Zhao, Kang Liu
arXiv:2604. 08377v2 Announce Type: replace Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment.
By Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang, Yiming Hu, Tongwen Huang, Xiangxiang Chu
Agentic University (AU) is a human‑participatory multi‑agent system that uses large language models to provide personalized tutoring, content generation, and adaptive learning paths. Learners act as organization members and collaborate with specialized agents to create and revise a Capability Program that links personal goals, learning activities, and layered assessment. Evaluation shows that AU improves program organization, assessment design, and revision quality, yielding higher assessment gains and stronger cognitive support compared to baseline approaches.
The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.
By Mia Lassiter, Brinnae Bent