Bio-Resilient Retrofit: Modular Substrate and Lifecycle Layers for Sustainable Transformer Scaling

The exhaustion of high-quality training data, coupled with escalating inference costs and model bloat, threatens continued scaling of large transformer models. This paper proposes a bio-resilient retrofit framework—modular, plug-and-play layers synthesizing DNA-inspired substrates, homeostatic Mind Matrix regulation, and developmental pruning/sealing mechanisms—for seamless integration into existing architectures. Key components include: (1) substrate-level token proofreading and filtering to reject noise/redundancy; (2) homeostatic scheduling for dynamic phase transitions; and (3) pruning with confidence-based sealing for parameter compression and knowledge protection. Quantitative evaluations on long-sequence and continual learning benchmarks demonstrate 40–60% reductions in effective token requirements and inference compute (MACs/GFLOPs) with minimal accuracy degradation and enhanced forgetting resistance. Pseudocode enables direct PyTorch/JAX implementation in models like Grok and Llama, offering a practical pathway to intelligence-per-token maximization in resource-constrained environments.