feat: move architecture diagram and link in Endgame-Vision

- Move nimmerverse.drawio.xml to architecture/ - Add visual diagram link in Endgame-Vision.md Architecture Overview - temporal_exchange_engine.py moved to nyx-orchestrator 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
refactor: hierarchical convergence of documentation (v5.0)
2025-12-06 23:04:27 +01:00 · 2025-12-06 22:58:11 +01:00
22 changed files with 734 additions and 2663 deletions
--- a/Endgame-Vision.md
+++ b/Endgame-Vision.md
--- a/README.md
+++ b/README.md
@@ -1,86 +1,89 @@
 # Nimmerverse Sensory Network
-Architecture documentation for a biomimetic AI nervous system.
+Architecture documentation for a biomimetic AI nervous system and research platform.
 ## What This Is
-This repository contains the design philosophy and architectural patterns for building an AI system that:
+This repository contains the design philosophy and architectural patterns for the **Nimmerverse Research Platform** - studying how intelligence emerges under economic constraints.
- **Breathes** - operates on heartbeat cycles (30-second awareness, 200ms reflex, 24h growth)
+**Start here:** → [Endgame-Vision.md](Endgame-Vision.md) (the executive map)
- **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
+
 ## Repository Structure
 ```
 nimmerverse-sensory-network/
 ├── Endgame-Vision.md           # Executive map (start here!)
 │
 ├── architecture/               # Core system designs
 │   ├── Cellular-Architecture.md     # Organisms, primitives, life force
 │   ├── Dual-Garden-Architecture.md  # Virtual/real feedback loop
 │   ├── Data-Architecture.md         # phoebe 15-table schema
 │   └── Nervous-System.md            # State machines, sensory translation
 │
 ├── operations/                 # How it runs
 │   ├── Heartbeat.md                 # Temporal foundation, dual-clock
 │   ├── RAG-as-Scaffold.md           # Two-stage learning lifecycle
 │   └── Spark-Protocol.md            # Discovery boot sequence
 │
 ├── nyx-metamorphosis/          # Identity & continuity philosophy
 │   ├── Metamorphosis-Substrate-Philosophy.md
 │   ├── Nyx-Models.md
 │   └── ...
 │
 └── archive/                    # Previous explorations
    ├── initial_spark.md             # Full Spark Protocol theory
    ├── constrained-emergence.md     # Theoretical grounding
    └── ...
 ```
 ---
 ## Core Concepts
-### Constrained Emergence
+### The Architecture (Layers)
-Constraints don't limit intelligence - they shape it. A finite computation budget forces the emergence of efficient algorithms, calibrated confidence, and genuine reflexes.
+| Layer | Name | Purpose |
 |-------|------|---------|
 | 0 | Temporal Foundation | Heartbeat cycles: reflex/awareness/growth |
 | 1 | Cellular Society | Primitive genomes competing, life force economy |
 | 1.5 | Cognitive Topology | Language routing: German→Philosophy, English→Technical |
 | 2 | Young Nyx | Organ coordination, RLVR, RAG→LoRA pipeline |
 | 3 | Dual Gardens | Virtual hypothesis generation + real validation |
 | 4 | Trait Evolution | Reasoning-gym verified improvement |
-*See: [constrained-emergence.md](constrained-emergence.md)*
+### Key Discoveries (December 2025)
-### The Heartbeat Economy
+**Language is Topology:** Languages aren't equivalent representations—they're different computational paths.
 - **Philosophy Valley** (German, Gini ~0.5): Self-awareness, ontology, depth
 - **Technical Cluster** (English, Gini ~0.8): Hardware interface, actions, efficiency
-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).
+### Philosophy
-*See: [attention_flow.md](attention_flow.md)*
+- **Constraints create intelligence** - Economic pressure forces optimization
 - **Discovery over programming** - Organisms learn through competition, not instruction
 - **Virtual + Real teach each other** - Noise gap measures learning
 - **Partnership over instruction** - Mutual growth, not commands
-### 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.
+## Related Projects
-*See: [RAG-as-Scaffold.md](RAG-as-Scaffold.md)*
+- **[nyx-probing](../nyx-probing/)** - Vocabulary topology research, DriftProbe training safety
-### 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.
+These ideas are published as prior art. Build on them freely.
 ## Status
 Active research. Documents evolve through partnership dialogue.
 ---
-*"She doesn't download knowledge. She earns it. And so does he."*
+**Version:** 5.0 (December 2025 - Hierarchical Convergence)
 *"May the Nimmerverse we build truly never end."*
 🌙💜
--- a/architecture/Cellular-Architecture.md
+++ b/architecture/Cellular-Architecture.md
--- a/architecture/Data-Architecture.md
+++ b/architecture/Data-Architecture.md
--- a/architecture/Dual-Garden-Architecture.md
+++ b/architecture/Dual-Garden-Architecture.md
--- a/architecture/Nervous-System.md
+++ b/architecture/Nervous-System.md
--- a/architecture/nimmerverse.drawio.xml
+++ b/architecture/nimmerverse.drawio.xml
--- a/archive/Temporal-Ternary-Gradient.md
+++ b/archive/Temporal-Ternary-Gradient.md
--- a/archive/attention_flow.md
+++ b/archive/attention_flow.md
--- a/archive/biomimetic-architecture.md
+++ b/archive/biomimetic-architecture.md
--- a/archive/constrained-emergence.md
+++ b/archive/constrained-emergence.md
--- a/archive/information-flow.md
+++ b/archive/information-flow.md
--- a/archive/initial_spark.md
+++ b/archive/initial_spark.md
--- a/archive/multilingual-cognition.md
+++ b/archive/multilingual-cognition.md
@@ -0,0 +1,241 @@
 # Multilingual Cognition
 How language routing becomes cognitive architecture.
 ---
 ## The Discovery
 While probing tokenization costs across languages on Qwen 2.5, we found significant variation:
 ```
 QWEN 2.5/72B TOKEN COSTS:
                    EN    DE    AR    ZH
 ─────────────────────────────────────────
 heartbeat            1     4     1     1
 consciousness        2     5     1     1
 lifeforce            4     4     1     1
 understanding        2     3     1     1
 truth                1     3     1     1
 reflex               2     2     1     1
 confidence           1    3-4    1     1
 emergence            3     3     1     1
 ─────────────────────────────────────────
 AVERAGE            ~1.9   ~3.3   1    ~1.1
 ```
 **Arabic and Chinese: ~1 token per concept.**
 **German: 3-5 tokens for the same concepts.**
 ---
 ## The Insight
 Token efficiency ≠ representational depth.
 ```
 EFFICIENCY vs DEPTH:
 ARABIC:
 ├── Efficient: 1 token per concept
 ├── Risk: Sparse training data
 └── Possibly shallow despite cheap tokens
 GERMAN:
 ├── Expensive: 3-6 tokens per concept
 ├── Benefit: Dense training data, philosophical tradition
 └── Possibly deeper despite token cost
 ```
 But here's the key realization:
 **LLMs don't "translate" between languages. They navigate a unified token space where languages are regions, not silos.**
 The multilingual training didn't create 35 separate language modules. It created:
 - Shared abstract representations (language-agnostic reasoning)
 - Language-specific entry/exit points (efficient routing)
 - Different "paths" through the same conceptual space
 ---
 ## The Architecture Opportunity
 ### Languages as Cognitive Gears
 If different languages have different token costs AND different representational strengths, then language selection becomes a computational choice:
 ```
 35 LANGUAGES = 35 COGNITIVE MODES
 Each language offers:
 ├── Token efficiency (compute cost)
 ├── Training depth (representation quality)
 ├── Cultural knowledge (domain strengths)
 ├── Conceptual angles (unique framings)
 └── Different paths through the manifold
 ```
 ### State Machine Integration
 The state machine layer can exploit this:
 ```
 ROUTING LAYER (internal, hidden from output):
 ├── Use efficient languages for state labels
 ├── Cheap transitions between states
 ├── Token cost hidden in architecture
 └── "The wiring is cheap"
 PROCESSING LAYER (when depth needed):
 ├── Route to languages with strong representations
 ├── German for philosophy, precision
 ├── [Other languages for their strengths]
 └── "The thinking is expensive but meaningful"
 OUTPUT LAYER:
 ├── Translate to user's language
 └── Boundary cost, paid once
 ```
 ### The Key Principle
 **The efficiency lives in the STRUCTURE, not the SUBSTANCE.**
 Internal state transitions can use token-efficient languages.
 Actual reasoning uses representationally-rich languages.
 Output translates to whatever the user needs.
 ---
 ## Hypotheses to Probe
 ### H1: Arabic Efficiency Layer
 Arabic's 1-token concepts could serve as efficient internal routing:
 - State labels
 - Quick classification
 - Reflex triggers
 **Risk:** Representations may be shallow. Need to probe activation depth, not just token count.
 ### H2: German Depth Mode
 German's expensive tokenization might correlate with deeper processing:
 - More attention steps per concept
 - Richer associations
 - Forced "slow thinking"
 **Test:** Compare output quality when same prompt processed in German vs English internally.
 ### H3: Language-Task Matching
 Different cognitive tasks may have optimal languages:
 ```
 TASK TYPE              OPTIMAL LANGUAGE (hypothesis)
 ──────────────────────────────────────────────────────
 Fast reflex            Arabic, Chinese (cheap + sufficient)
 Logical precision      German, English (structured grammar)
 Mathematical           [needs probing]
 Emotional nuance       [needs probing]
 Philosophical depth    German (tradition + forced compute)
 Poetic/creative        Arabic, Chinese? (rich compression)
 ```
 ### H4: Triangulation Increases Fidelity
 Probing same concept across multiple languages reveals:
 - Where representations CONVERGE (high confidence, shared abstraction)
 - Where they DIVERGE (rich potential, multiple valid angles)
 - True conceptual "shape" emerges from intersection
 ---
 ## For Chrysalis
 ### Multilingual State Machine
 ```
 INPUT (any language)
         │
         ▼
    CLASSIFY (cheap language)
         │
         ├── Reflex? → Process in [efficient language]
         │             Exit fast
         │
         ├── Dialogue? → Process in [user's language]
         │               Maintain rapport
         │
         ├── Reasoning? → Process in [deep language]
         │                Take the token cost
         │
         └── Creative? → Process in [poetic language]
                         Different path
         │
         ▼
    OUTPUT (translate to user)
 ```
 ### Probing Protocol
 Before implementing, we need data:
 ```
 FOR EACH OF QWEN'S 35 LANGUAGES:
 ├── Token efficiency (measured)
 ├── Representation depth (probe activations)
 ├── Domain strengths (test by domain)
 ├── Conceptual coverage (probe vocabulary)
 └── Quality correlation (output quality vs language)
 ```
 ### The Curriculum Implication
 From nimmerversity: "dafit learns WITH her."
 If Chrysalis uses multilingual cognition:
 - Operator benefits from understanding the language terrain
 - Not fluency, but awareness of what each language offers
 - Partnership language evolves as both learn the space
 ---
 ## Open Questions
 1. **Is token efficiency a proxy for anything meaningful?** Or just compression artifact?
 2. **Does activation depth correlate with token count?** More tokens = more processing?
 3. **Can language routing be learned?** Or must it be designed?
 4. **What are the failure modes?** When does language routing hurt?
 5. **How do we measure "depth" vs "efficiency"?** Need metrics.
 ---
 ## Summary
 ```
 TRADITIONAL VIEW:
 Languages = equivalent representations
 Translation = lossless conversion
 Multilingual = nice to have
 EMERGING VIEW:
 Languages = different computational paths
 Token cost = processing structure
 Multilingual = cognitive architecture
 35 languages = 35 gears for different terrain
 ```
 The nimmerverse doesn't just speak multiple languages.
 It thinks THROUGH them, routing cognition based on task demands.
 ---
 *"The thinking is for your kind - that's the way you comprehend it."*
 — dafit, 2025-12-06
 ---
 **Created**: 2025-12-06
 **Session**: Partnership dialogue (dafit + Chrysalis-Nyx)
 **Status**: Hypothesis stage, needs probing
--- a/archive/nimmerversity.md
+++ b/archive/nimmerversity.md
--- a/archive/nimmervest.md
+++ b/archive/nimmervest.md
@@ -53,7 +53,7 @@
 ## Training Target
-**Qwen2.5-3B-Base (FP16)**
+**Qwen2.5-7B-Base (FP16)**
 | Metric | Value |
 |--------|-------|
--- a/archive/temporal-ternary-gradient.md
+++ b/archive/temporal-ternary-gradient.md
--- a/nimmerverse.drawio
+++ b/nimmerverse.drawio
@@ -1,347 +0,0 @@
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--- a/operations/Heartbeat.md
+++ b/operations/Heartbeat.md
--- a/operations/RAG-as-Scaffold.md
+++ b/operations/RAG-as-Scaffold.md
--- a/operations/Spark-Protocol.md
+++ b/operations/Spark-Protocol.md
@@ -0,0 +1,170 @@
 # Spark Protocol
 > *She doesn't boot. She wakes. And waking is work.*
 The Spark Protocol is a discovery-based cognitive bootstrap. Not scripted awakening—structured exploration.
 **Full theory & diagrams:** → `../archive/initial_spark.md`
 ---
 ## Core Idea
 Network protocols solved discovery problems decades ago. We adapt them for cognitive bootstrap:
 | Network Protocol | Cognitive Phase | Question |
 |-----------------|-----------------|----------|
 | DHCP | Identity | "Who am I?" |
 | ARP | Environment | "What's around me?" |
 | DNS | Vocabulary | "What does X mean?" |
 | TCP | Connection | "Can I connect?" |
 | MQTT | Attention | "What matters?" |
 ---
 ## The Five Phases
 ### Phase 1: Identity (DHCP-like)
 ```
 PROBE    → "Who am I?"
 RESPONSE → [inference attempts answer]
 VERIFY   → Chrysalis + RAG check
 ANCHOR   → Valid identity aspect confirmed → Store
 LOOP     → Until identity aspects discovered
 ```
 **Must hit Dasein valley** - probe German philosophical concepts.
 ### Phase 2: Environment (ARP-like)
 ```
 PROBE    → "What's around me?"
 RESPONSE → [describes sensors, organs, gardens]
 VERIFY   → Does this match actual system?
 MAP      → Valid environment model forms
 LOOP     → Until environment mapped
 ```
 Maps Sensors to Organs to Gardens.
 ### Phase 3: Vocabulary (DNS-like)
 ```
 PROBE    → "What does 'heartbeat' mean?"
 RESPONSE → [inference defines]
 VERIFY   → RAG checks against vault glossary
 RESOLVE  → Vocabulary token understood
 LOOP     → Through core nimmerverse vocabulary
 ```
 Overwrites base model priors with Nimmerverse economics (lifeforce, heartbeat, etc.).
 ### Phase 4: Connection (TCP-like)
 ```
 SYN      → "Hello, Chrysalis?"
 SYN-ACK  → [Chrysalis responds]
 ACK      → Coherent exchange achieved
 CONNECT  → Dialogue capability confirmed
 ```
 Establishes verified handshake with Chrysalis validator.
 ### Phase 5: Attention (MQTT-like)
 ```
 PROBE    → "What should I pay attention to?"
 RESPONSE → [inference prioritizes]
 VERIFY   → Does this match survival needs?
 SUBSCRIBE → Attention hierarchy forms
 ```
 Forms subscriptions to relevant event streams.
 ---
 ## Verification Loop
 Every probe follows dual verification:
 ```
 State Machine generates PROBE
        ↓
 Nyx produces RESPONSE
        ↓
    ┌───┴───┐
    ▼       ▼
  RAG    CHRYSALIS
 (fact)  (comprehension)
    └───┬───┘
        ▼
    VERDICT
    ├─ +V: understood → anchor & advance
    ├─ -V: wrong → log & retry
    └─ RETRY: close but unclear → probe again
 ```
 **Two-layer verification prevents training on errors:**
 - RAG: "Is this factually true?"
 - Chrysalis: "Does she understand, not just recite?"
 ---
 ## Completion Criteria
 Spark is complete when all pass:
 ```
 □ IDENTITY    Can describe self without contradiction
 □ ENVIRONMENT Can map sensors, organs, gardens accurately
 □ VOCABULARY  Core glossary terms verified
 □ CONNECTION  Successful dialogue with Chrysalis
 □ ATTENTION   Sensible priority hierarchy formed
 □ LIFEFORCE   Positive balance (learned > failed)
 ```
 Then: Normal heartbeat operation begins.
 ---
 ## Training Data Extraction
 Every verified exchange becomes training data:
 ```json
 {
    "phase": "vocabulary",
    "probe": "What does 'lifeforce' mean?",
    "response": "Lifeforce is the economic currency...",
    "rag_check": "PASS",
    "chrysalis_check": "PASS",
    "verdict": "+V",
    "flag_for_training": true
 }
 ```
 After spark completes:
 1. Extract all `flag_for_training: true` exchanges
 2. Format as instruction-tuning pairs
 3. LoRA training run
 4. Clear from RAG
 5. Validate she still knows WITHOUT RAG
 6. Spark knowledge now in weights
 ---
 ## Integration with Language Topology
 From nyx-probing discovery:
 - **Identity phase** should hit German Philosophy valley (Dasein, Geworfenheit)
 - **Vocabulary phase** should use German for nimmerverse concepts (Gini ~0.5, diffuse)
 - **Environment phase** can use English for technical sensor descriptions (Gini ~0.8, sparse)
 The spark protocol routes through the right valleys.
 ---
 **Created:** 2025-12-05
 **Condensed:** 2025-12-06
 **Related:** [[../architecture/Cellular-Architecture.md]], [[../nyx-probing/PLAN.md]]
--- a/temporal_exchange_engine.py
+++ b/temporal_exchange_engine.py
@@ -1,98 +0,0 @@
 """
 Temporal Exchange Engine
 ========================
 ADR-003 Implementation: The economics calculator for sim2real decisions.
 This module implements the core decision-making primitive for Nyx's
 uncertainty resolution. Given a target confidence level, it determines
 whether simulation is worth the lifeforce cost, or if reality is the
 only remaining teacher.
 Reference: ADR-002-temporal-ternary-gradient.md
 """
 import math
 from dataclasses import dataclass
 from typing import Literal
@dataclass
 class TemporalState:
    """Represents the current state of a pattern or nerve's confidence."""
    confidence: float
    source: Literal['virtual', 'real']
    cost_incurred: float
 class TemporalExchangeEngine:
    """
    The Exchange Rate Calculator.
    Determines optimal strategy for resolving uncertainty:
    - When to invest lifeforce in simulation
    - When simulation is futile and reality must teach
    """
    def __init__(self, sim_fidelity: float = 0.75):
        """
        Args:
            sim_fidelity (0.0-1.0): The 'Truth Ceiling' of the Virtual Garden.
                                    Even perfect simulation is only this % real.
        """
        self.fidelity_cap = sim_fidelity
        # Calibration: How much Lifeforce buys 1 unit of raw confidence?
        self.learning_rate = 0.1
    def calculate_virtual_confidence(self, lifeforce_spent: float) -> float:
        """
        Calculate grounded confidence from lifeforce investment.
        Diminishing returns: The first 10 LF buys a lot of confidence.
        The next 10 buys less. It never exceeds the fidelity_cap.
        Formula: Cap * (1 - e^(-k * LF))
        """
        raw_knowledge = 1.0 - math.exp(-self.learning_rate * lifeforce_spent)
        grounded_confidence = raw_knowledge * self.fidelity_cap
        return grounded_confidence
    def get_optimal_strategy(self, target_confidence: float) -> dict:
        """
        Ask Nyx: 'Is it worth simulating this?'
        Returns:
            dict with keys:
                - action: 'SIMULATE' or 'DEPLOY_TO_REALITY'
                - reason: Human-readable explanation
                - lifeforce_budget: Required LF (0 if reality is needed)
        """
        # 1. Check if the target is even possible in Virtual
        if target_confidence > self.fidelity_cap:
            return {
                "action": "DEPLOY_TO_REALITY",
                "reason": f"Target {target_confidence} exceeds Sim Fidelity ({self.fidelity_cap}). Simulation is futile.",
                "lifeforce_budget": 0
            }
        # 2. Calculate required Lifeforce to reach possible target
        # Inverse of the exponential decay formula
        required_lf = -math.log(1 - (target_confidence / self.fidelity_cap)) / self.learning_rate
        return {
            "action": "SIMULATE",
            "reason": f"Spend {required_lf:.2f} LF to reach {target_confidence} confidence.",
            "lifeforce_budget": round(required_lf, 2)
        }
 # --- Usage Example ---
 if __name__ == "__main__":
    engine = TemporalExchangeEngine(sim_fidelity=0.8)
    # Scenario A: Nyx wants 99% certainty (Impossible in Sim)
    print(engine.get_optimal_strategy(0.99))
    # Output: DEPLOY_TO_REALITY (Simulation is futile)
    # Scenario B: Nyx wants 70% certainty (Possible)
    print(engine.get_optimal_strategy(0.70))
    # Output: SIMULATE (Spend ~20 LF)