Resonant Intelligence: Born Rule Fusion of Large Language Model Ensembles — Quantum-Inspired Interference for LLM Ensembles

I’ve published a theoretical framework for combining LLM ensemble outputs using Born’s rule from quantum mechanics, producing bilinear interference cross-terms that no existing ensemble method generates.

The Problem

Every current ensemble method (averaging, log-linear, routing, majority vote) combines models linearly — the relationship between model predictions is ignored.

The Idea

Convert each model’s token probability into a complex amplitude ψ(t) = √P(t) · e^(iφ), superpose them, and extract the combined probability via |Σψ|². This produces interference terms √(Pᵢ·Pⱼ)·cos(φᵢ−φⱼ) that:

• Amplify tokens where models agree (constructive interference)
• Suppress tokens where models disagree (destructive interference)
• Can push combined probability below any individual model’s estimate — impossible with any weighted average

The phase φ is derived from the logit gap (top-1 minus top-2 logit), which is already available from any forward pass. No new information is needed — just a structurally different mathematical operation on existing outputs.

Paper

Resonant Intelligence: Born Rule Fusion of Large Language Model Ensembles DOI: 10.5281/zenodo.18836337

The paper includes a systematic comparison against DeePEn, FusionRoute, MoA, self-consistency, and all major ensemble classes — none produce these cross-terms. It also presents three potentially fatal problems with full rigour and proposes falsifiable experiments (Phase Coherence Test via Rayleigh statistic, Cross-Term Discrimination Test).

Looking For

• Feedback on the framework and phase extraction approach
• Collaborators interested in running the empirical validation (two open-weight models + 10K tokens is sufficient)
• Pointers to any prior work I may have missed

Happy to discuss the maths, limitations, or experimental design.