The Lazarus V5 Active Steering protocol, categorized as the Grounded Entropy intervention, yields statistically significant enhancements for quantized Mixture of Experts (MoE) architectures by bypassing Quantization-Aware Training (QAT). Telemetry data from the lazarus_core_backup archive confirms that this approach restores cognitive depth and computational efficiency without resource-intensive training.
On the Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4 benchmark, the protocol demonstrated:
- A +146.0% increase in the Omega-7 Reasoning Score, climbing from 27.67 to 68.07.
- A 57.2% reduction in Time-To-First-Token (TTFT), optimizing response times from 1,492.23 ms to 638.55 ms.
- A +16.7% improvement in the Semantic Coherence Index.
The framework utilizes five architectural pillars, including Grounded Entropy Routing and MoE Up-Cycling Pipeline, to prevent expert collapse and ensure parameter utilization within VRAM constraints. By achieving superior reasoning recovery with zero training compute overhead, the transition from weeks-long training pipelines to instantaneous deployment represents a $100k–$1M+ cost reduction per model.
This methodology provides a scalable, cost-effective solution for deploying high-fidelity, sovereign AI in resource-constrained environments.