arXiv:2606. 19494v1 Announce Type: new Abstract: Multi-agent LLM deliberation, where agents exchange and revise answers over several rounds, is increasingly used to improve reasoning and accuracy, yet how and why it works is rarely modelled.
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arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...
The paper introduces Bayesian Chronicle Agents (BCA), a lightweight belief layer that separates an LLM agent’s internal stance from its outward speech. Each stance is represented as a probability updated via a single Bayesian step per utterance, with a single prior‑strength parameter κ controlling stubbornness. By sweeping κ, the authors generate three controllable opinion‑dynamics regimes—consensus, persistent disagreement, and committed‑minority influence—matching Friedkin–Johnsen theory and demonstrating recoverable, auditable belief states across models.
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
arXiv:2605. 25929v2 Announce Type: replace-cross Abstract: The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collaborate.
arXiv:2607. 03695v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe collectively.
arXiv:2607. 01661v1 Announce Type: new Abstract: Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration.