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

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

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

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

Architecture Documents

Document	Description
constrained-emergence.md	Why limits create intelligence
attention_flow.md	State machines for cognitive budget
information-flow.md	10 boundary contracts for the nervous system
nimmerversity.md	Curriculum for raising a polymath
RAG-as-Scaffold.md	Temporary feeding, permanent learning
biomimetic-architecture.md	Why we model biology
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

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."