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- RAG-as-Scaffold: temporary feeding system, not permanent crutch - attention_flow: 30-second heartbeat budget state machines - information-flow: 10 boundary contracts nervous system map - nimmerversity: curriculum schoolplan for raising a polymath - nimmervest: investment documentation - biomimetic-architecture: ADR for organic system design - temporal-ternary-gradient: ADR for time-based learning - temporal_exchange_engine.py: Python implementation - initial_spark: foundation document - nimmerverse.drawio.xml: updated diagrams 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
69 lines
3.0 KiB
Markdown
69 lines
3.0 KiB
Markdown
# ADR-002: Temporal-Ternary Gradient & Sim2Real Strategy
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* **Status:** Accepted
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* **Date:** 2025-12-05
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* **Context:** Autonomous Agent Decision Making / Uncertainty Management
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* **Tags:** ternary-logic, sim2real, active-learning, economics
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## 1. Context and Problem Statement
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In the Nimmerverse, the agent (Nyx) frequently encounters the **"0-State"** (Unknown/Uncertainty).
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* **Traditional Binary Logic:** Forces a premature true/false decision, leading to errors.
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* **Standard Ternary Logic:** Allows a "null" state but offers no path to resolve it.
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* **The Constraint:** Real-world verification is slow and risky; simulation is fast but hallucinatory.
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We need a protocol to "spend" system resources (Lifeforce) to resolve the 0-State into a +1 (Truth) or -1 (Falsehood) efficiently.
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## 2. The Solution: Temporal-Ternary Gradient
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We treat the **0-State** not as a static void, but as a **gradient of investment** across two time domains.
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### The Two Domains
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1. **Virtual Garden (Simulation):**
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* **Currency:** Lifeforce (Compute Energy).
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* **Time Physics:** Malleable (1000x speed).
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* **Output:** Statistical Confidence (Epistemic Probability).
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2. **Real Garden (Physical Reality):**
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* **Currency:** Time (Wall-clock).
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* **Time Physics:** Fixed (1x speed).
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* **Output:** Ground Truth (Ontological Fact).
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## 3. Strategic Logic: The Fidelity Discount
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To prevent **Sim2Real Hallucinations** (where an agent is confident in simulation but fails in reality), we introduce a mandatory **Fidelity Discount** variable.
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* **Risk:** `Virtual Confidence 0.99` in a `50% Accurate Sim` = `Real Confidence 0.495`.
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* **Mandate:** Nyx must never act on raw virtual confidence. She must calculate `grounded_confidence` before deploying to the Real Garden.
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## 4. Data Structure Standard
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The state object for any pattern or nerve must track both the **Value** (Ternary) and the **Economic Investment** (Temporal).
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```python
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state = {
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"value": 0, # -1 (Fail), 0 (Unknown), 1 (Pass)
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# The Sim2Real Bridge
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"raw_confidence": 0.95, # Statistical confidence from Virtual runs
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"sim_fidelity": 0.70, # CONSTANT: How accurate is the simulation?
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# The Decision Metric (The Anchor)
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# Nyx uses THIS to decide when to trigger a Real World test.
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"grounded_confidence": 0.665, # (raw_confidence * sim_fidelity)
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"economics": {
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"lifeforce_spent": 45.0, # Compute cost sunk
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"real_time_saved_min": 120 # Time bought via simulation
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}
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}
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```
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## 5. Decision Protocol (The Exchange Rate)
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Nyx calculates the **Opportunity Cost** of the 0-State:
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1. **High Urgency:** Spend heavy Lifeforce to max out `raw_confidence` in seconds, then deploy.
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2. **Low Urgency:** Trickle-charge `raw_confidence` in background sims, or wait for passive Real World data.
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3. **The Cap:** Virtual optimization stops when `raw_confidence > sim_fidelity`. Beyond this point, simulation yields diminishing returns. Only Reality can increase confidence further.
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