Molecular Memory allows MoE systems to recover previously learned domain expertise 9-11x faster by utilizing cost-penalized fitness metrics that preserve dormant experts.
April 2, 2026
Original Paper
Cost-Penalized Fitness in FMA-Orchestrated Mixture of Experts: Experimental Evidence for Molecular Memory in Domain Adaptation
arXiv · 2604.00812
The Takeaway
This solves the problem of catastrophic forgetting in production MoE models under domain shift. It significantly reduces the compute needed for domain adaptation, potentially saving millions in training costs for large-scale providers.
From the abstract
We present experimental results from seven controlled runs of nanoFMT, a Free-Market Algorithm (FMA) orchestrated transformer with dynamic Mixture-of-Experts (MoE) management. The experiments address a fundamental question for advanced LLM development: how should an MoE system manage its expert pool when operating at full capacity under changing data distributions? We demonstrate that cost-penalized fitness metrics, combined with a linear grace period for newborn experts, produce a system that a