arXiv AI
Jun 18
Recursive Joint Simulation in Games
arXiv:2402. 08128v3 Announce Type: replace Abstract: Game-theoretic dynamics between AI agents could differ from traditional human-human interactions in various ways.
Synthetic Counteradaptation: A Principle of Human-AI Co-evolution
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.
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arXiv AI
Jul 16
Social Simulations: from Agent-Based Modeling to Digital Twins
arXiv:2607. 13693v1 Announce Type: cross Abstract: This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly defined behavioral rules, to AI-enhanced simulations based on Large Language Models and, ultimately, Social Digital Twins: high-fidelity, data-driven representations of real-world socio-technical systems.
arXiv AI
Jul 17
Align AI to Dynamic Human-AI Workflows
arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
arXiv AI
Jul 7
Serious Games: Human-AI Interaction, Evolution, and Coevolution
arXiv:2505. 16388v2 Announce Type: replace Abstract: The serious games between humans and AI have only just begun.
arXiv AI
Jul 21
Artificially intelligent agents in the social and behavioral sciences: A history and outlook
arXiv:2510. 05743v3 Announce Type: replace Abstract: We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models.
arXiv Computation and Language
Sep 22
SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm
arXiv:2609.24911v1 Announce Type: new Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by...
Hugging Face Trending Papers
Sep 21
SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based...
arXiv AI
Jun 3
SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems
arXiv:2606. 03544v1 Announce Type: new Abstract: Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior.
arXiv AI
Jun 9
Payoff scaling shapes cooperation in LLM agents across languages
arXiv:2601. 19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users.
arXiv AI
Aug 20
Position: Behavioral Systems Require Behavioral Tests
The paper argues that artificial agentic systems, which operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time, should be evaluated through systematic observation, perturbation, and interpretation of their actions rather than solely on performance outcomes. It draws on lessons from behavioral sciences to motivate this position and proposes a research agenda that includes methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi‑agent systems. These directions aim to establish a rigorous science of AI behavior.
arXiv AI
Jul 2
Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction
arXiv:2607. 00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized.
arXiv AI
Jun 6
Towards Healthy Evolution: Exploring the Role and Mechanisms of Human-Agent Interaction in Self-Evolving Systems
arXiv:2606. 06114v1 Announce Type: new Abstract: Self-evolving agents improve through continual self-play and self-generated learning signals, but autonomous evolution can also cause capability degradation and safety drift.