Think-Before-Speak: From Internal Evaluation to Public Expression in Multi-Agent Social Simulation
arXiv:2606.03137v1 Announce Type: new Abstract: LLM-based multi-agent simulation offers a promising way to study social interaction, deliberation, and collective opinion dynamics. However, many existing dialogue simulation frameworks represent interaction mainly as observable turn exchange or aggregated outputs, leaving the internal evaluative processes behind silence, speaking intention, and public expression difficult to examine. We introduce TBS (Think-Before-Speak), an interval-based multi-agent simulation framework that separates agents' private reasoning from public utterance generation.
The increasing sophistication of LLMs and the recognition of limitations in current multi-agent simulation frameworks are driving innovation in understanding complex social dynamics.
This work directly addresses a critical gap in simulating nuanced social interactions, moving beyond simple turn-taking to model internal thought processes, which is crucial for developing more realistic and effective AI agents.
The ability to examine internal evaluative processes of AI agents before public expression changes how researchers can study and design social AI, enabling a deeper understanding of emergent behaviors.
- · AI ethicists
- · Social scientists
- · Agentic AI developers
- · Simulation researchers
- · Developers of simplistic agent models
AI agents will become capable of more sophisticated and believable social interactions.
This framework could lead to more robust and less predictable AI system behaviors, requiring enhanced oversight and ethical considerations.
The deeper understanding of internal AI states might inform new theories of human social cognition or lead to hybrid human-AI social systems.
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Read at arXiv cs.AI