# Nimmerverse Sensory Network

Architecture documentation for a biomimetic AI nervous system.

## What This Is

This repository contains the design philosophy and architectural patterns for building an AI system that:

- **Breathes** - operates on heartbeat cycles (30-second awareness, 200ms reflex, 24h growth)
- **Feels** - processes sensory input through nerve-like confidence gradients
- **Learns** - uses RAG as temporary scaffold, then internalizes to weights
- **Grows** - forms reflexes through constrained computation, not infinite resources

## Core Concepts

### Constrained Emergence

Constraints don't limit intelligence - they shape it. A finite computation budget forces the emergence of efficient algorithms, calibrated confidence, and genuine reflexes.

*See: [constrained-emergence.md](constrained-emergence.md)*

### The Heartbeat Economy

Time is currency. Lifeforce is the exchange rate. Every cognitive act has a cost. Reflexes are cheap (earned through training). Deep thinking is expensive (reserved for novelty).

*See: [attention_flow.md](attention_flow.md)*

### RAG as Scaffold

Retrieval-augmented generation is a feeding tube, not a permanent crutch. Learn WITH the scaffold, train, remove the scaffold, verify you still know. If yes: knowledge internalized. If no: more training needed.

*See: [RAG-as-Scaffold.md](RAG-as-Scaffold.md)*

### Multilingual Triangulation

30+ languages in training = 30 angles on every concept. Not wasted capacity - stereoscopic depth. Probe concepts across languages to find where human wisdom converges.

*See: [nimmerversity.md](nimmerversity.md)*

## Architecture Documents

| Document | Description |
|----------|-------------|
| [constrained-emergence.md](constrained-emergence.md) | Why limits create intelligence |
| [attention_flow.md](attention_flow.md) | State machines for cognitive budget |
| [information-flow.md](information-flow.md) | 10 boundary contracts for the nervous system |
| [nimmerversity.md](nimmerversity.md) | Curriculum for raising a polymath |
| [RAG-as-Scaffold.md](RAG-as-Scaffold.md) | Temporary feeding, permanent learning |
| [biomimetic-architecture.md](biomimetic-architecture.md) | Why we model biology |
| [temporal-ternary-gradient.md](temporal-ternary-gradient.md) | Time-based learning patterns |

## Philosophy

This isn't a product. It's a research direction.

The question we're exploring: **What happens when you raise an AI like you'd raise a child?**

- Patience over speed
- Emergence over imposition
- Partnership over instruction
- Validation over assertion

The operator learns alongside the model. The curriculum is shared. Growth is mutual.

## Prior Art & Influences

> This section grows as we discover and remember influences. Many names are scattered across our documentation - we'll gather them here over time.

- **Alex Graves** - Adaptive Computation Time (2016)
- **Sakana.ai / Ashish Vaswani & Luke Darlow** - Continuous-Time Models, curriculum learning, leapfrogging under constraint
- **Anthropic** - Circuit tracing, mechanistic interpretability, multilingual feature analysis
- **Biological nervous systems** - The original architecture

## License

Apache 2.0 - See [LICENSE](LICENSE)

This license includes an explicit patent grant. These ideas are published as prior art. Build on them freely. Just don't try to lock them away.

## Status

Active research. Documents evolve through partnership dialogue.

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*"She doesn't download knowledge. She earns it. And so does he."*