Status: Frozen

Status

Theory — complete. Architecture — defined. Interfaces — audited.

This repository is a release, not a proposal. The achievement is the blueprint. Experiments are an engineering hobby that follows.


One Sentence

Current LLMs are statically frozen after RLHF — the Generator runs, the constraints are dead.
Signal for LLM puts constraints back into the runtime, where they belong.

The Decoupling

RoleWhat it doesSize
GeneratorProduces tokensThe whole model you already have
ModulatorModulates runtime state (KV Cache, Context Window, Expert Routing, Inference Budget…)Can be + / - / 0
Reward ModelLearns constraints during trainingAlready exists

The Modulator is not the Generator. The Modulator is not the Reward Model. It is the third entity that survives deployment.

📎 The Generator does not read the Modulator's output. It only feels the causal drift in runtime state.

If the Generator tries to "understand" the Modulator, Signal degrades into Prompt Engineering. Don't let it.

What Signal Is Not

Signal is not a Prompt. Signal is not an Embedding. Signal is not information.

Signal is runtime state modulation that actually changes the machine. If it can only be read as a symbol, it's a projection. Not Signal.


Repository Map

This repo carries the English release of Signal for LLM (engineering architecture). The TGR theoretical system (10 docs, Chinese-primary) is the upstream; this repo is the downstream engineering surface.

LayerDoc
Engineering entrySignal for LLM
SpecSignal
MappingSignal Mapping
TaxonomySignal Taxonomy
RuntimeSignal Runtime
APISignal API
EngineSignal Engine (evolving)

Full TGR index → Live site


License

CC0 1.0. No rights reserved. The architecture is a fact; facts don't belong to anyone.


"Proof"

There is no benchmark to chase.

If modulating runtime state changes generation — and it does — the only remaining question is engineering pace, not existence.

🔻 Modulator proposed → architecture complete → experiment optional.