ArXiv preprint shows LLMs benefit from deliberation across four tasks
The arXiv preprint arXiv:2609.22497v1 adapts a three-stage human deliberation paradigm to multiple large language model families and tests it across four domains (visual numerical estimation, ML paper peer review, detecting hidden malicious AI behavior, and sports forecasting). Deliberating LLM groups produced lower collective error than passive aggregation, individual model judgments improved after deliberation, and the advantage depended on model diversity (clones of a single model did not benefit).