Phase A: Context Isolation and Pre-Loading
VIE dispatches to provider-agnostic model routes while Nightjar Variable Parser applies eager loading of only relevant state, memory graph, and backend context.
Three execution phases that compound to reduce latency, drift, and retry cost.
Execution model
Orchestrator: First we isolate context so we only load what is relevant.
Inference layer: Then I guide generation so outputs stay consistent and tool-safe.
State layer: After that I mutate state directly so we avoid unnecessary serialization.
System: The result is lower latency, less drift, and fewer retries.
layers
route
memory
model_training
data_object
swap_horiz
commit
recenter
Phase B: Hardened In-Inference Guidance
RFM Neural Controllers steer hidden-layer activations during generation to reduce drift and force syntax-consistent, tool-safe outputs on first pass.
Phase C: Direct State Mutation
Nightjar effect handlers execute natural code state operations in-place, with control-state handoff back to VIE orchestrator via return or raise effects.
Compounding Benefits
Shared memory removes serialization overhead, deterministic fast-paths reduce latency, and local GPU efficiency improves through cached effects and iteration guards.