Publications

We present Meta Council, a multi-agent LLM framework in which N expert agents—each with a unique professional persona and analytical framework—analyze queries in parallel, then a weighted synthesis step produces structured decision documents with confidence scores, dissent preservation, and risk matrices. Evaluated across 750+ benchmark runs spanning six domains and five models (3B to frontier-class), we find that weighted synthesis outperforms single-best selection by 29–58% on freetext tasks (p<0.0001, d=2.16), that synthesis amplifies model quality non-linearly, and that the optimal aggregation method is domain-dependent.

When positive-class training data is a biased subset of the true positive population—common in hiring, medical screening, and credit scoring—standard cross-validation selects models that overfit to the observed cluster. We propose Stability Bonus (SB) regularization, which rewards hyperparameter configurations with small train–validation gaps, favoring wider decision boundaries that generalize to unseen positive subgroups. On hard synthetic benchmarks with controlled distribution shift, SB improves test AUC by +6.9% (p<0.0001, d=3.48) and unseen-subgroup AUC by +7.1%. Class weighting, the standard remedy, is the worst performer under shift.