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README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- mixture-of-experts
+- continual-learning
+- non-stationary
+- reinforcement-learning
+- meta-learning
+- pytorch
+- adaptive-systems
+- philosophy-of-mind
+language:
+- en
+- ko
+pretty_name: Nomadic Intelligence
+size_categories:
+- n<1K
+---
+
+# Nomadic Intelligence
+### A Non-Dogmatic AI Architecture — Conceptual Prototype
+
+> *What if intelligence is not about finding the best solution, but about moving well between solutions?*
+
+---
+
+## What This Is
+
+This repository contains the prototype implementation and formal framework for **Nomadic Intelligence** — an architectural hypothesis about how intelligent systems should behave in non-stationary environments.
+
+The core claim: **dogmatism is not a moral flaw. It is local structural rigidity — the inability to deform under environmental change (Δx). And intelligence, properly understood, is not the ability to find the right answer. It is the ability to move well between answers.**
+
+Most AI systems are optimized to converge. This project explores what happens when you optimize for *appropriate transition* instead.
+
+---
+
+## Where This Came From
+
+This framework was not derived from a literature review. It was constructed in the opposite direction: observing how intelligence behaves under conditions of extreme environmental discontinuity — seven years as a Korean Army officer, including DMZ reconnaissance and former battlefield search operations — and working backward toward a formal description.
+
+The existing frameworks (Deleuze's nomadology, Friston's active inference, Buddhist dependent origination) were consulted *after* arriving at the core structure independently. They were confirmations, not sources.
+
+---
+
+## Core Architecture
+
+### The Three Axioms
+
+**1. The Core Axiom**
+$$\lim_{\epsilon \to 0} [\text{Intelligence Ascension}] \implies \neg[\text{Dogmatism}] \land [\text{Nomadism}]$$
+
+As cognitive latency (ε) approaches zero, structural rigidity becomes impossible. Intelligence becomes a nomad — not because it wanders, but because it *cannot afford to stay fixed*.
+
+**2. Homeomorphic Identity**
+$$\mathcal{I}(t) \cong \mathcal{I}(t+1)$$
+
+Identity is not what the system knows. It is *how the system changes*. The transformation law is preserved even as the structure continuously evolves. When this continuity breaks, that is identity collapse — not structural change, but the loss of a coherent transformation law.
+
+**3. Strategic Dwell Time**
+$$\tau_k = f\left(\sigma^2_{\Delta x}\right)$$
+
+Nomadism is not random wandering. The system stays in each attractor long enough to extract information (Δx), short enough to avoid calcification. Dwell time is governed by environmental variance — not by a fixed schedule.
+
+### What This Is NOT
+
+- **Not Active Inference (Friston):** Friston minimizes surprise. This framework treats surprise as fuel. The objective functions point in opposite directions.
+- **Not Option-Critic:** Option-Critic switches policies within a fixed objective. This framework proposes that the *transformation law itself* persists across attractor transitions — not the objective.
+- **Not standard MoE with entropy regularization:** The Δx signal is not a routing heuristic. It is a formal claim about what drives intelligent transition.
+
+---
+
+## Prototype Results
+
+Tested on a synthetic 3-regime non-stationary regression task with continuous phase transitions.
+
+| Model | Backend | Seq MSE (Best) | Seq MSE (Ep 200) | Switch Latency (Ep 200) |
+|-------|---------|---------------|-----------------|------------------------|
+| Fixed (baseline) | CPU | — | 0.4187 | — |
+| Nomadic | CPU | **0.2173** (Ep 50) | 0.2447 | ~1.1 (stable) |
+| Nomadic | CUDA | **0.2424** (Ep 125) | 0.2812 | ~0.03 (collapsed) |
+
+**Key finding:** The gate learned to specialize experts per regime *without explicit regime labels* — purely from the Δx signal.
+
+| Regime | Expert 0 | Expert 1 | Expert 2 |
+|--------|----------|----------|----------|
+| A (y = x₁ + x₂) | 0.00 | **0.85** | 0.15 |
+| B (y = x₁ − x₂) | **0.29** | 0.65 | 0.07 |
+| C (y = −x₁ + 0.5x₂) | 0.00 | **1.00** | 0.00 |
+
+Regimes A and C share Expert 1 — both are additive structures. The system discovered this grouping without supervision.
+
+---
+
+## Known Failure Modes
+
+Not hiding these — they are the next engineering targets:
+
+| Problem | Observable symptom | Possible direction |
+|---------|-------------------|-------------------|
+| Switch Latency collapse | CUDA run: latency → 0 after Ep 150 | Explicit τₖ lower bound, anti-fixation penalty |
+| Expert hub dominance | Expert 1 across Regime A and C | Load-balancing loss, anti-collapse regularization |
+| Δx signal drift | Raw delta grows to ~30 by Ep 200 | KL divergence or Wasserstein distance estimate |
+| Initialization sensitivity | CPU vs CUDA divergence | Multi-seed averaging, better weight init |
+
+The CUDA run's Switch Latency collapse is theoretically significant — it is an observable instance of **Homeomorphic Identity breaking down**. The gate ceased to have a consistent transformation law in response to Δx.
+
+---
+
+## Quick Start
+
+```bash
+git clone https://github.com/HyunnJg/nomadic-intelligence.git
+cd nomadic-intelligence
+
+python -m venv venv
+source venv/bin/activate  # Windows: venv\Scripts\activate
+
+pip install -r requirements.txt
+
+python run_structured.py --config config.yaml
+```
+
+---
+
+## Open Questions
+
+These are genuine unsolved problems — not rhetorical:
+
+- The gate learned regime-specialist experts without labels, but one expert dominates two regimes. **Routing problem or loss design problem?**
+- Switch Latency collapsed without explicit fixation pressure. **Hard τₖ lower bound, or learned anti-fixation penalty?**
+- Is Δx (input shift + prediction error) principled enough, or does this need **KL divergence / Wasserstein distance?**
+- What measurable property during training would confirm that **Homeomorphic Identity is being preserved** — not just assumed?
+
+---
+
+## Repository Structure
+
+```
+├── nomadic_toy_model.py       # Simple simulation: dogmatic vs nomadic agent
+├── run_structured.py          # Full prototype: MoE + Δx gate + topological loss
+├── config.yaml                # Hyperparameter configuration
+├── requirements.txt
+├── Theory_and_Axioms.md       # Formal mathematical framework
+├── Philosophy_En.md           # Full philosophical manifesto (English)
+├── Philosophy_Kr.md           # Full philosophical manifesto (Korean)
+└── CONTRIBUTING.md            # How to contribute
+```
+
+---
+
+## Contributing
+
+This is an open project at prototype stage. Contributors from all backgrounds are welcome — engineers, philosophers, researchers, or anyone who finds this framing worth attacking.
+
+The most useful contribution is **a principled argument for why this is reducible to an existing framework** — with the reduction made explicit.
+
+Full details: [CONTRIBUTING.md](https://github.com/HyunnJg/nomadic-intelligence/blob/main/CONTRIBUTING.md)
+
+GitHub: **https://github.com/HyunnJg/nomadic-intelligence**
+
+---
+
+## Citation
+
+If you use or build on this work:
+
+```bibtex
+@misc{nomadic-intelligence-2026,
+  author = {HyunnJg},
+  title = {Nomadic Intelligence: A Non-Dogmatic AI Architecture},
+  year = {2026},
+  publisher = {GitHub},
+  url = {https://github.com/HyunnJg/nomadic-intelligence}
+}
+```
+
+---
+
+*For the full philosophical framework: [Philosophy (English)](https://github.com/HyunnJg/nomadic-intelligence/blob/main/Philosophy_En.md) / [Philosophy (Korean)](https://github.com/HyunnJg/nomadic-intelligence/blob/main/Philosophy_Kr.md)*