Field Protocols

Technical reference for integrating the NARE Field reasoning engine into autonomous workflows.

AMORTIZED_INFERENCE

Caches successful logical pathways into continuous trace memory, bypassing redundant calculations.

0_TOKEN_COST

ADAPTIVE_COMPUTE

Dynamically injects logic back into the LLM based on geometric manifold optimization.

CONFIDENCE_GATE

LATENT_PATHWAY

Routes representations through a proprietary continuous space for deterministic reasoning.

PROPRIETARY

Implementation Standard

Deploy the NARE-Field Monolith locally on consumer GPUs (e.g., RTX 3050) with float32 precision.

IntegrationPyTorch Native

GradientZero-Gradient Inference

Memory CostO(1) Distillation

Python Integration

from narefield import NAREFieldEngine # 1. Initialize the Engine engine = NAREFieldEngine(model_path="narelabs/nare-1-mini", alpha=0.35) # 2. Attach Reasoning Manifold engine.attach(manifold_idx=14) # 3. Execute with Latent Cycles output = engine.generate_with_reasoning(prompt="Solve...")