Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift
TL;DR
An experimentally verified formal law is provided for calculating the uplift that diversity of thought provides in Large Language Model (LLM) ensembles through an exact decomposition of LLM ensemble lift into rescue and damage masses, which yields a compact heuristic for calculating uplift.
Abstract
This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM) ensembles. From first principles, we derive an exact decomposition of LLM ensemble lift into rescue and damage masses, which yields a compact heuristic for calculating uplift. From this we extract the metrics which predict ensemble performance: an accuracy-adjusted correctness correlation, $\phi_{\mathrm{adj}}$, together with the accuracy gap and collective accuracy of the pair. We test the law on 767,520 inferences from ten open-weight models over two graduate-level science benchmarks, together with a novel agentic cybersecurity benchmark in which each model conducts digital-forensics investigations by multi-turn tool use in a network-isolated sandbox (23,520 graded trials including abstentions); all votes are released openly. Calibrated once on SuperGPQA at a 40:60 vote split, the heuristic predicts lift on the calibration set with Spearman's $\rho=0.84$ and, with its coefficients frozen, transfers to two datasets never used in calibration ($\rho=0.51$ on GPQA Diamond and $0.84$ on the forensic tasks), whilst the measured swap mass tracks realised lift with $R^2\ge 0.96$ throughout. Raw $\phi$ has almost no predictive power ($R^2\le 0.09$ throughout); the accuracy-adjusted $\phi_{\mathrm{adj}}$ is markedly superior ($R^2=0.67$ on SuperGPQA), and the heuristic combining these metrics is the most stable pre-pooling predictor across the three datasets.