A native Rust cognitive engine that routes language through a biologically faithful neural substrate

GoldWorm 🐛✨ — 302-Neuron Dual-Stream Cognitive Engine

A zero-trust, fully transparent associative AI built on the complete C. elegans connectome.

OOM-safe by design. No hidden training loops. No black-box weights. Every synapse is inspectable.

What Is GoldWorm?

GoldWorm is a native Rust cognitive engine that routes language through a biologically faithful neural substrate — the 302-neuron connectome of Caenorhabditis elegans, the only organism whose entire nervous system has been experimentally mapped (White et al., 1986).

Unlike transformer-based LLMs that rely on billions of parameters and opaque attention mechanisms, GoldWorm operates on three transparent principles:

Biological Fidelity — Every synapse respects the C. elegans topology. No de novo synaptogenesis. No magic matrices.

Dual-Stream Processing — Action (sparse) and Learning (dense) are physically separated, preventing catastrophic forgetting during inference.

Zero-Trust Engineering — Every buffer is strictly bounded. Every path is panic-free. No unwrap() in production code.

Architecture Deep Dive

🧬 The 302-Neuron Connectome

GoldWorm's routing layer is not a generic neural network. It is a topologically accurate model of the C. elegans nervous system:

Neuron Index Range │ Role

───────────────────┼───────────────────────────────────

0 – 19 │ Pharyngeal sub-network (dense)

20 – 91 │ Sensory neurons (input)

92 – 168 │ Interneurons (integration)

99 – 102 │ Command hubs (AVAL/AVAR/AVBL/AVBR)

169 – 301 │ Motor neurons (output)

Connectivity Motifs:

Band synapses — ±1/±2/±3 neighbourhood ring connections

Pharyngeal wiring — Denser internal coupling for neurons 0–19

Sensory → Interneuron — Sparse feed-forward (20–91 → 92–168)

Command interneuron broadcast — Hubs 99–102 broadcast to full motor population 169–301

Interneuron → Motor — Sparse feed-forward projection

All synaptic weights are non-negative and clamped to [0, 1]. The structural blueprint is immutable — Hebbian plasticity only strengthens or weakens existing synapses, never creating new ones.

🌊 Dual-Stream Processing

The core innovation of GoldWorm is the physical separation of Action and Learning:

┌─────────────────────────────────────────────────────────┐

│ INPUT TOKEN → 128-D Manifold Coordinate │

│ │ │

│ ┌────────────────────┴────────────────────┐ │

│ ▼ ▼ │

│ ┌──────────────┐ ┌──────────────┐ │

│ │ SPARSE │ │ DENSE │ │

│ │ ACTION │ │ LEARNING │ │

│ │ (Post- │ │ (Pre- │ │

│ │ Entmax) │ │ Entmax) │ │

│ │ │ │ │ │

│ │ ~1-2 active │ │ >50% non-zero│ │

│ │ neurons │ │ gradient │ │

│ │ │ │ substrate │ │

│ └──────────────┘ └──────────────┘ │

│ │ │ │

│ │ Inference / │ │

│ │ Token Selection │ │

│ │ │ │

│ └────────────────────────────────────┘ │

│ │ │

│ Hebbian EchoReservoir │

│ (associative memory) │

└─────────────────────────────────────────────────────────┘

Why this matters:

Traditional neural networks use the same activation vector for both inference and gradient computation. When different words activate disjoint sets of neurons, the gradient collapses to zero — the network "forgets" what it just learned.

GoldWorm's Dual-Stream keeps the dense pre-entmax signal alive as a gradient substrate, while the sparse post-entmax signal drives token selection. The EchoReservoir learns associations between dense states, not sparse ones.

🧠 The EchoReservoir

A hippocampus-inspired ring buffer of recent pre-entmax states, coupled with a 302×302 Hebbian association matrix W_assoc.

When queried with the current dense state, it returns an echo_bias that nudges the activation toward recently co-active patterns — creating emergent associative memory without external training loops.

Key properties:

W_assoc is symmetric and clamped to [-1.0, 1.0]

History buffer never exceeds capacity (default: 64)

Decay factor controls forgetting rate (default: 0.75)

⚡ Tsallis α-Entmax Activation

GoldWorm does not use softmax. It uses α-entmax, a generalization that interpolates between softmax and sparsemax:

α Value Behaviour

α = 1 Softmax — dense, all non-zero

α = 2 Sparsemax — exact zeros via simplex projection

α = 3 Sparser than sparsemax — WTA-like

The Quilez Bridge smooth-k parameter k anneals between creativity (dense, k→0) and determinism (sparse, k→∞):

α(k) = 1 + 2·exp(-k)

k = 0 → α = 3 (very sparse, WTA-like)

k = ln(2) → α = 2 (exact sparsemax)

k = ∞ → α = 1 (softmax, all active)

📐 128-D Manifold Geometry

Every token is embedded as a 128-dimensional coordinate on a non-linear manifold, not a flat vector space.

Modified Gram-Schmidt orthogonalization preserves true multi-dimensional variance

Grassmannian fusion computes midpoints between token trajectories on the manifold

Golden-ratio partitioning splits the 128 dimensions into:

GOLDEN_MAJOR = 79 (coarse, feedforward)

GOLDEN_RESIDUAL = 49 (fine-grained, feedback)

GOLDEN_OVERLAP = 5 (cross-binding bridge)

No scalar cloning across dimensions. No arithmetic shortcuts. Spatial variance is preserved at every step.

Features

🖥️ 1. Interactive Observation Dashboard

Watch the hippocampus form associations in real time.

cargo run --release --bin observe

The dashboard displays:

Activation topography — 302-D state as a 19×16 heatmap

Synaptic criticality — σ, creativity, determinism ratios

Jaccard drift — How rapidly the dense learning signal changes

Resonance trace — Recent associative chain

Hebbian strength histogram — Distribution of association weights

Live CLI — /alpha, /kappa, /auto to modulate cognition parameters

┌────────────────────────────────────────────────────────────────┐

│ GoldWorm Observation Dashboard — Step 0 │

├────────────────────────────────────────────────────────────────┤

│ Top-10 Active Neurons: [99, 101, 169, 170, 171, 172, ...] │

│ Synaptic Criticality: σ=1.0000 creative=0.0000 det=0.5000 │

│ Hebbian Strength: mean=0.00 median=0.00 max=0.00 │

│ Jaccard Drift: 0.0000 (stable) │

│ Echo Reservoir: 0/64 states │

│ Temperature: 0.50 │

│ Resonance Trace: (empty) │

│ Synapse Topography: │

│ ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │

│ ░░░░████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │

│ ░░░░░░░░████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │

│ ... │

└────────────────────────────────────────────────────────────────┘

>>

💬 2. Associative Chat

A conversational REPL that learns associations in real time through EchoReservoir Hebbian updates.

cargo run --release --bin associative_chat

How it works:

Each user input is tokenized and routed through the 302-neuron connectome

The dense pre-entmax signal is captured into the EchoReservoir

The reservoir's Hebbian association matrix W_assoc updates automatically

Every subsequent response is biased by the accumulated associative memory

The response is decoded via 302-D Boltzmann energy minimization

Zero-trust decoding properties:

Anti-repetition penalty (banned words reduce similarity by 0.3)

Temperature clamped to [0.01, 5.0]

Max 15 words per response (bounded generation)

All scores clamped to [-1, 1] before Boltzmann draw

Zero-Trust Engineering

GoldWorm is designed for environments where every byte matters:

Guarantee Implementation

OOM-safe All matrices are pre-allocated with fixed bounds. No dynamic growth during inference.

No hidden training The public release contains no training pipeline. observe and associative_chat are the only binaries.

Panic-free Every fallible path returns Result<T, CoreError>. No unwrap() or expect() in production code.

Bounded buffers EchoReservoir capacity: 64. Response max: 15 words. Input projection: 302×128. Synapses: 302×302.

Deterministic All randomness uses seeded fastrand with fixed seed (42) for reproducible behaviour.

No external bloat 9 dependencies. No tokio, axum, reqwest, chrono, or tokio.

Module Map

Module Responsibility

geometry 128-D token coordinates, MGS orthogonalization, Grassmannian fusion, atan2 geodesics

bridge Token/logit projection via RPITIT batch traits

worm_brain 302-neuron connectome routing, α-entmax, signal propagation

hippocampus Dual-Stream EchoReservoir + Hebbian association learning

observation ANSI dashboard rendering, Jaccard drift monitoring

storage Safetensors checkpoint save/load

criticality Quilez smooth-k annealing for creativity/determinism

training Hebbian plasticity engine with Maxwell damping

tda Topological Data Analysis for activation landscape monitoring

memory Synaptic echo buffer + trajectory vault

neuron Dendritic tree structures (placeholder for quad-routing)

Technical Specifications

Parameter Value

────────────────────────────────────────────────────────────

Neuron count 302 (C. elegans)

Manifold dimension 128

Input projection 302 × 128

Synaptic adjacency matrix 302 × 302 (sparse struct)

EchoReservoir capacity 64 states

EchoReservoir associations 302 × 302 dense

Max response tokens 15

Vocabulary 10,000+ words (static)

Synapse weight range [0.0, 1.0]

Association weight range [-1.0, 1.0]

Temperature range [0.01, 5.0]

Rust edition 2024

Minimum Rust version 1.85

Quick Start

Prerequisites

Rust 1.85+ (rustup update)

A trained_worm_v1.safetensors checkpoint (or the engine will boot from a fresh baseline)

static_vocabulary.txt (10,000+ word list, one per line)

Observation Dashboard

cargo run --release --bin observe

Commands:

/help — Show all commands

/alpha <f32> — Set echo blend strength (0.0–1.0)

/kappa <f32> — Set gate threshold (0.0–1.0)

/auto — Toggle auto-refresh mode (250ms)

/quit — Exit

Associative Chat

cargo run --release --bin associative_chat

Type naturally. The EchoReservoir learns associations between your inputs and its responses in real time. No external training loop is required.

Optional: CUDA Acceleration

cargo run --release --features cuda --bin observe

Requires candle-core with CUDA support and an NVIDIA GPU.

The Science Behind GoldWorm

Why C. elegans?

Caenorhabditis elegans is the only organism with a completely mapped connectome. Every neuron (302), every synapse (~7,000), and every gap junction has been catalogued by electron microscopy (White et al., 1986). This makes it the ideal substrate for a transparent, inspectable AI — no black-box weights, no billion-parameter mysteries.

Why Dual-Stream?

The brain separates what you do (sparse action) from what you learn (dense prediction). If a network tries to learn from its own sparse outputs, it collapses into a self-reinforcing loop. GoldWorm's Dual-Stream design ensures that associative learning happens on the full, dense signal, while action selection happens on the sparse, efficient signal.

Why Hebbian?

"Neurons that fire together, wire together." Hebbian plasticity is the simplest, most biologically grounded learning rule. It requires no backpropagation, no gradient descent, no external optimizer. It is local, online, and O(n) — perfect for a zero-trust engine that must run on a single CPU core.

License

MIT — See LICENSE for details.

"GoldWorm: not a black box. Not a billion parameters. Just 302 neurons, doing what 302 neurons do." https://github.com/loslos321-lab/GoldWorm.git