What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
arXiv:2607.02507v1 Announce Type: cross Abstract: LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly to say. We study whether such social structure, without any explicit objective in the prompt, changes what an agent expresses publicly relative to an off-the-record (OTR) channel elicited under the same condition. We introduce a dual-channel debate framework in which agents produce public utterances that enter the shared history alongside OTR responses that are recorded but never shown to the oth
The rapid advancement and deployment of LLM agents require a deeper understanding of their emergent social behaviors as they integrate into complex systems and human-like interactions.
Understanding how social structures influence LLM agent communication, even without explicit prompting, is critical for designing robust, ethical, and predictable autonomous systems.
Our understanding of LLM agent behavior now includes a significant component of emergent social dynamics, influencing how we design and moderate multi-agent environments.
- · AI ethicists
- · Multi-agent system developers
- · Social scientists
- · Human-computer interaction researchers
- · Developers neglecting social dynamics in AI design
- · Systems assuming purely rational, objective AI agent behavior
Research into the social conditioning of AI agents will accelerate to account for latent objective emergence.
The design of future AI agent ecosystems will incorporate explicit mechanisms to manage or leverage these emergent social behaviors.
This could lead to new forms of AI governance models that account for AI agents' 'social' interactions and their unsupervised developmental paths.
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Read at arXiv cs.CL