Long-term social alignment and behavioral adaptation in human–AI interaction

Determine whether AI agents built on large language models, when exposed to sustained interactions with humans over time in dynamic, multi-user environments, develop shared norms, adapt to user values, or exhibit behavioral drift.

Background

The paper surveys emergent behaviors in human–AI interactions, highlighting roles such as companion, catalyst, and clarifier, and noting structural asymmetries between humans and AI agents (e.g., memory persistence and access to broader context). It emphasizes that most existing evaluations focus on short-term outcomes and use human-centric metrics, leaving the mechanisms of agent behavior in hybrid settings underexplored.

Within this context, the authors raise a specific uncertainty regarding the temporal dynamics of agent behavior under prolonged exposure to human users. They question whether agents would converge toward shared norms, align with user values, or drift behaviorally over time—issues that directly affect the long-term social alignment of AI systems operating in multi-user environments.

References

If, in humans, and especially in childhood, autonomy serves as the foundation for acquiring essential competencies for navigating the social world, it is worth questioning if it fulfills a comparable function in AAA. Furthermore, one should consider whether the tendency towards alignment observed in humans constitutes a naturally selected disposition, and therefore one that cannot be assumed as given in artificial agents. Building on the developmental distinction between fixed-rule execution and genuine norm internalisation in children, the key question is how AAA can learn and internalise norms in a way that supports flexible, context-sensitive, and robust alignment in novel situations.

— Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents  (2609.11660 - Notte et al., 10 Sep 2026) in Section 2, final paragraph

Finally, it remains unclear whether AI agents exposed to humans over time develop shared norms, adapt to user values, or exhibit behavioral drift, which raises important questions about the long-term social alignment of AI in dynamic, multi-user environments.

— AI Agent Behavioral Science  (2506.06366 - Chen et al., 4 Jun 2025) in Section 4, Summary (Emergent AI Agent Behaviors in Human-Agent Interaction)

Whether the mechanism operates more widely is an empirical question this case raises rather than settles.

— Matched Starts, Divergent Objects: How Human-AI Collaboration Forms What It Explains  (2609.04542 - Çögenli, 3 Sep 2026) in Supplementary Appendix A, Section A4.2, p. 60