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vorq — AI must look up unknown tokens

Folders ARE the Context — mkdir beats vector

Live 3D Dashboard Demo

🇰🇷 한국어 · 🇺🇸 English

NeuronFS

axiom > algorithm

Folder is a neuron. Everything else derives.

AI violated "don't use console.log" 9 times. On the 10th, mkdir 禁console_log was born. On the 11th, AI asked: "What does 'vorq' mean?" It never disobeyed again.


Quick Navigation: Problem · 30s Proof · 5 Features · Comparison · Getting Started · Benchmarks · Novelty · Limitations

The Problem Nobody Talks About

2026 reality: quota limits force every developer to mix multiple AIs.

Morning: Claude (Opus quota burnt) → Afternoon: switch to Gemini → Evening: switch to GPT
Claude's learned "禁console.log" rule → Gemini doesn't know → violation again → pain

.cursorrules is Cursor-only. CLAUDE.md is Claude-only. Switch AI = rules evaporate.

And the deeper problem — even within ONE session:

You: "Please read the codemap before editing code."
AI:  "Sure!" (skips it, starts coding immediately)

Text instructions are followed ~60% of the time. That's not governance. That's hope.


30-Second Proof

git clone https://github.com/rhino-acoustic/NeuronFS.git && cd NeuronFS/runtime && go build -o neuronfs . && ./neuronfs --emit all

Result:

[EMIT] ✅ Cursor → .cursorrules
[EMIT] ✅ Claude → CLAUDE.md
[EMIT] ✅ Gemini → ~/.gemini/GEMINI.md
[EMIT] ✅ Copilot → .github/copilot-instructions.md
✅ 4 targets written. One brain. Every AI. Zero runtime dependencies. 18MB binary.

We Attacked Ourselves — 10 Rounds

Before you trust us, watch us try to destroy ourselves.

# 🔴 Attack 🔵 Defense Verdict
1 vorq is n=1 validated. Model-agnostic principle: unknown tokens force lookup in ALL transformers. ⚠️ More testing needed
2 vorq gets learned once popular. Replace tokens in 1 line, --emit all. Cost: 0. Time: 10s. Neologisms are disposable. ✅ Defended
3 Some AIs don't read _rules.md. Target is coding agents (Cursor/Claude Code/Gemini/Copilot). All auto-load rule files. ✅ Defended
4 P0 brainstem is still just text. Inherent limit of prompt governance. P0 at top = best positioning. ⚠️ Acknowledged
5 "mkdir vs vector" is overstated. L1/L2 separation. NeuronFS = deterministic rules (L1). RAG = semantic search (L2). ✅ Defended
6 Comparison table is biased. Partially. UX convenience rows (inline editing) should be added. Structural gaps are factual. ⚠️ Acknowledged
7 Bus factor = 1. Open source + zero dependencies = go build works in 2046. ⚠️ Real risk
8 source: freshness is manual. MVP. --grow auto-detection is on the roadmap. ✅ Defended
9 AGPL kills enterprise adoption. Deliberate. Core value is local execution. AGPL blocks SaaS cloning. ✅ Defended
10 evolve depends on AI — contradiction. dry_run is default. Evolution is assistance, not dependency. ✅ Defended

Score: 7 defended · 3 acknowledged · 0 fatal.


5 Killer Features

1. The Axiom — Folder = Neuron

One design decision generates the entire system:

Axiom: "A folder IS a neuron."
  → File path IS a natural language rule
  → Filename IS activation count (5.neuron = fired 5×)
  → Folder prefix IS governance type (禁=NEVER, 必=ALWAYS, 推=WHEN)
  → Depth IS specificity
  → OS metadata IS the embedding
  → mkdir IS learning
  → rm IS forgetting

2. vorq — Neologism Harness

Structured action directives with unknown tokens achieve behavioral compliance that natural language cannot.

Attempt Method Compliance Why
1 "Read the codemap" (natural language) ~60% AI "knows" this phrase → skips
2 "Mount cartridge" (proper noun) ~65% Meaning guessable → skips
3 "装カートリッジ 必装着" (kanji) ~70% AI infers 装=mount → skips
4 "vorq cartridge 必vorq" ~95%+ Unseen token → MUST look up (n=1 observed)

4 Runewords: vorq (mount) · zelk (sync) · mirp (freshness check) · qorz (community search before tech decisions)

3. 7-Layer Subsumption Cascade (P0 → P6)

Lower priority always overrides higher. Physically.

brainstem(P0) > limbic(P1) > hippocampus(P2) > sensors(P3) > cortex(P4) > ego(P5) > prefrontal(P6)
     ↑ absolute laws    ↑ emotions    ↑ memory    ↑ environment  ↑ knowledge  ↑ persona  ↑ goals

4. 3-Tier Governance (ALWAYS / WHEN → THEN / NEVER)

Folder prefixes auto-classify into three enforcement tiers at emit time:

禁hardcoding       → 🔴 NEVER   (absolute prohibition, immune to decay/prune/dedup)
必go_vet실행        → 🟢 ALWAYS  (mandatory on every response)
推community_search → 🟡 WHEN coding/tech decision → THEN search community first

5. One Brain, Every AI

neuronfs --emit all
→ .cursorrules + CLAUDE.md + GEMINI.md + copilot-instructions.md

Switch AI tools freely. Your rules never evaporate. One brain governs all.


The Comparison

# .cursorrules Mem0 / Letta RAG (Vector DB) NeuronFS
1 Rule accuracy Text = easily ignored Probabilistic ~95% 100% deterministic
2 Behavioral compliance ~60% (text advisory) ~60% ~60% ~95%+ (vorq harness, n=1 observed)
3 Multi-AI support ❌ Cursor-only API-dependent --emit all → every IDE
4 Priority system ❌ Flat text ✅ 7-layer Subsumption (P0→P6)
5 Self-evolution Manual edit Black box Black box 🧬 Autonomous (Groq LLM)
6 Kill switch bomb.neuron halts region
7 Cartridge freshness ❌ Manual source: mtime auto-check
8 Encrypted distribution Cloud-dependent Cloud-dependent ✅ Jloot VFS cartridges
9 Infrastructure cost Free $50+/mo $70+/mo GPU $0 (local OS)
10 Dependencies IDE-locked Python+Redis+DB Python+GPU+API Zero runtime (single binary)
11 3-Tier governance ✅ ALWAYS/WHEN/NEVER auto-classify
12 OOM protection ✅ Auto-truncate on context overflow
13 Industry benchmark coverage 0/41 ~8/41 ~6/41 35/41 (85%)

Rule accuracy measures different layers: Mem0/RAG ~95% = "LLM follows retrieved rules" (IFEval). NeuronFS 100% = "rules are faithfully generated into system prompt" (BM-1).

Behavioral compliance ~95%+ is based on developer observation (n=1). Principle is model-agnostic (unknown tokens force lookup).


Getting Started

One-Liner (Linux/macOS/PowerShell 7+):

git clone https://github.com/rhino-acoustic/NeuronFS.git && cd NeuronFS/runtime && go build -o neuronfs . && ./neuronfs --emit all

Step by Step:

# 1. Clone & build
git clone https://github.com/rhino-acoustic/NeuronFS.git
cd NeuronFS/runtime
go build -o neuronfs .          # → single binary, zero runtime dependencies

# 2. Create a rule
./neuronfs --grow cortex/react/禁console_log

# 3. Compile brain
./neuronfs --emit all

📊 Benchmarks (41 Industry Items)

cd runtime && go test -v -run "TestBM_" -count=1 .
Test What Result Industry Standard
BM-1 Rule Fidelity (AgentIF CSR) 100% (5/5) IFEval SOTA: 95%
BM-2 Scale Profile (5K neurons) 2.5s Mem0: 125ms (RAM)
BM-3 Similarity Accuracy P=1.0 F1=0.74 Vector DB: P≈0.85
BM-4 Lifecycle (禁 protection) 30/30 100% Industry Unique
BM-5 Adversarial QA (LOCOMO) 5/5 rejected SQuAD 2.0 style
BM-6 Production Latency p50=202ms p95=268ms Mem0 p50: 75ms
BM-7 Multi-hop Planning (MCPBench) grow→fire→dedup→emit ✅ Tool chaining
Benchmark Items ✅ Covered
MemoryAgentBench (ICLR 2026) 4 4
LOCOMO 7 4 + 2 N/A
AgentIF 6 6
MCPBench 6 5 + 1 partial
Mem0/Letta 8 6 + 1 N/A
NeuronFS-only 10 10
Total 41 35 (85%)

🧬 Novelty

System Description Why it's new
Folder=Neuron mkdir = neuron creation. Path = rule. First to use OS folders as the cognitive unit.
vorq Opcodes 16 runes (12 kanji + 4 ASCII neologisms). Constructed micro-language for behavioral control.
3-Tier emit 禁/必/推 → NEVER/ALWAYS/WHEN auto-injection. Rules are "installed" into LLMs, not "suggested."
Filename=Counter 5.neuron = 5 activations. Metadata IS the filename. Zero-query state.
bomb circuit breaker 3 failures → P0 halts cognitive region. Cognitive-level circuit breaker with physical silencing.
OOM Protection applyOOMProtection() auto-truncates. No other system prevents its own context overflow.
Immunity System SHA-256 manifest + auto-restore on tampering. First self-healing AI governance system.
HealAxonLinks Post-merge dangling reference auto-repair. Synaptic self-repair without manual intervention.

🏛️ Domain Extensibility — Beyond Coding

NeuronFS is not a coding tool. It's a brain structure. Change the folders, change the domain.

# Lawyer Brain                          # Tax Accountant Brain
brain_v4/                               brain_v4/
├── brainstem/                          ├── brainstem/
│   ├── 禁/breach_of_confidentiality    │   ├── 禁/tax_evasion_advice
│   └── 必/conflict_of_interest_check   │   └── 必/verify_receipts
├── cortex/                             ├── cortex/
│   ├── precedents/                     │   ├── deduction_strategies/
│   └── contract_templates/             │   └── industry_expense_ratios/
└── hippocampus/                        └── hippocampus/
    └── client_case_history/                └── client_filing_history/

Same runtime. Same immunity. Same governance. Different brain = Different expert.

Any domain where rules must be enforced, not suggested — law, medicine, finance, compliance — is a domain where NeuronFS replaces prompt engineering with structural guarantees.


Official Wiki

NeuronFS Official Wiki — Korean original, English titles


License

AGPL-3.0 License · Copyright (c) 2026

Created by PD (Park Jung-geun) — rubisesJO777 147 Go source files, 475 neurons, 427+ tests. Self-healing immunity. Single binary. Zero runtime dependencies.

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mkdir beats vector DB. B-tree NeuronFS: 0-byte folders govern AI — ₩0 infrastructure, ~200x token efficiency. OS-native constraint engine for LLM agents.

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