Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift
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, $φ_{\mathrm{adj}}$, 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 w...
| Authors | Junade Ali |
|---|---|
| Primary category | cs.AI |
| Published | 19 July 2026 |
| Last updated at source | 30 September 2026 |
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