arXiv:2606.30571v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in open-ended multi-agent settings, but the long-run dynamics of model--model interaction remain poorly understood. We study whether open-ended LLM discussions exhibit attractor-like behavior, i.e. topic-independent stable sets of behaviors which conversations settle into. Across 7 LLMs and 20 controversial topics, we compare self-play and mixed-play dyadic debates, tracking trajectories in representation space, discourse traits, and stances. We find self-play trajectories to be model-specific at
The increasing deployment of large language models in multi-agent settings necessitates understanding their long-term interactive dynamics.
This research provides crucial insights into the emergent stable behaviors of LLM interactions, which can predict system stability and biases in autonomous AI systems.
Our understanding of how LLMs behave in open-ended, multi-turn conversations now includes the concept of attractor states, implying predictable conversational patterns.
- · AI developers
- · Autonomous agent designers
- · AI safety researchers
- · AI system evaluators
- · Developers of unpredictable AI agents
- · Researchers assuming linear LLM interaction
Understanding attractor states enables better design and control over LLM-based multi-agent systems.
This predictability could lead to the creation of more robust and reliable AI systems for complex tasks.
The existence of attractor states might inherently limit the diversity of AI discourse, posing challenges for open-ended creative or problem-solving AI applications.
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Read at arXiv cs.LG