nimmerverse-sensory-network/archive/temporal-ternary-gradient.md

ADR-002: Temporal-Ternary Gradient & Sim2Real Strategy

• Status: Accepted
• Date: 2025-12-05
• Context: Autonomous Agent Decision Making / Uncertainty Management
• Tags: ternary-logic, sim2real, active-learning, economics

1. Context and Problem Statement

In the Nimmerverse, the agent (Nyx) frequently encounters the "0-State" (Unknown/Uncertainty).

• Traditional Binary Logic: Forces a premature true/false decision, leading to errors.
• Standard Ternary Logic: Allows a "null" state but offers no path to resolve it.
• The Constraint: Real-world verification is slow and risky; simulation is fast but hallucinatory.

We need a protocol to "spend" system resources (Lifeforce) to resolve the 0-State into a +1 (Truth) or -1 (Falsehood) efficiently.

2. The Solution: Temporal-Ternary Gradient

We treat the 0-State not as a static void, but as a gradient of investment across two time domains.

The Two Domains

1. Virtual Garden (Simulation):
   • Currency: Lifeforce (Compute Energy).
   • Time Physics: Malleable (1000x speed).
   • Output: Statistical Confidence (Epistemic Probability).
2. Real Garden (Physical Reality):
   • Currency: Time (Wall-clock).
   • Time Physics: Fixed (1x speed).
   • Output: Ground Truth (Ontological Fact).

3. Strategic Logic: The Fidelity Discount

To prevent Sim2Real Hallucinations (where an agent is confident in simulation but fails in reality), we introduce a mandatory Fidelity Discount variable.

• Risk: Virtual Confidence 0.99 in a 50% Accurate Sim = Real Confidence 0.495.
• Mandate: Nyx must never act on raw virtual confidence. She must calculate grounded_confidence before deploying to the Real Garden.

4. Data Structure Standard

The state object for any pattern or nerve must track both the Value (Ternary) and the Economic Investment (Temporal).

state = {
    "value": 0,              # -1 (Fail), 0 (Unknown), 1 (Pass)

    # The Sim2Real Bridge
    "raw_confidence": 0.95,  # Statistical confidence from Virtual runs
    "sim_fidelity": 0.70,    # CONSTANT: How accurate is the simulation?

    # The Decision Metric (The Anchor)
    # Nyx uses THIS to decide when to trigger a Real World test.
    "grounded_confidence": 0.665, # (raw_confidence * sim_fidelity)

    "economics": {
        "lifeforce_spent": 45.0,      # Compute cost sunk
        "real_time_saved_min": 120    # Time bought via simulation
    }
}

5. Decision Protocol (The Exchange Rate)

Nyx calculates the Opportunity Cost of the 0-State:

1. High Urgency: Spend heavy Lifeforce to max out raw_confidence in seconds, then deploy.
2. Low Urgency: Trickle-charge raw_confidence in background sims, or wait for passive Real World data.
3. The Cap: Virtual optimization stops when raw_confidence > sim_fidelity. Beyond this point, simulation yields diminishing returns. Only Reality can increase confidence further.