arXiv:2607.07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents. As AI agents, including large language models (LLMs), become embedded in daily life, they increasingly participate in such interactions and reshape social interaction structures. Yet they often fail to coordinate with humans in an effective, considerate, and natural manner. We hypothesize that this gap arises because existing approaches align model behavior with h
The increasing integration of AI, especially large language models, into daily human interactions highlights current deficiencies in human-AI coordination, making research into social norms critical.
Achieving seamless human-AI coordination through learned social norms is essential for AI adoption, ethical development, and the operationalization of AI agents in complex social settings.
AI systems will evolve beyond task-specific functionalities to become more integrated and 'socially intelligent' agents capable of nuanced human-like interaction and coordination.
- · AI developers focused on social intelligence
- · Companies deploying AI agents in customer-facing roles
- · Users of AI systems
- · Ethical AI frameworks
- · AI systems failing to adapt to human social dynamics
- · Developers prioritizing purely technical metrics over social integration
- · Industries requiring complex human-AI collaboration without socially aware AI
AI agents become more effective and accepted partners in human collaborative environments.
New standards and benchmarks for socio-ethical AI performance emerge, influencing AI development pipelines.
The definition of 'intelligence' in AI shifts to include social and emotional competence, driving research into cognitive architectures.
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