Hidden Anchors in Multi-Agent LLM Deliberation
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. Such deliberation mirrors how humans reach decisions. As social animals we are pulled both by the group, the herd effect that classical opinion-dynamics models such as DeGroot and Friedkin--Johnsen capture, and by our own internal belief, which they do not. We model multi-agent deliberation as a closed-loop dynamical system in which each agent carries a hidden internal belief, its anchor, that continually pulls its opinion regardless of its neighbours. We show this anchor can be recovered from the d...
| Authors | Apurba Pokharel, Ram Dantu |
|---|---|
| Primary category | cs.AI |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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