nimmerverse-sensory-network/architecture/formalization/Attention-Slumber-Prediction-Cycle.md

Attention-Slumber-Prediction Cycle: Intertwined Reward Systems

Version 1.0 — The Closed Loop of Consciousness Status: PRESERVED FROM SESSION 2025-12-29 (pre-collapse)

"The last thing she attends to before slumber becomes her dream. Her dream becomes a prediction. Her prediction becomes a reward opportunity."

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Overview

This document captures the Attention → Slumber → Prediction → Verification cycle — a self-organizing system where:

1. Attention selects what matters (budget limited, from attention_flow.md)
2. Lifeforce depletion triggers slumber (L(t) < L_slumber)
3. Last attention focus becomes the prediction target
4. Slumber generates predictions with causal reasoning (WHY)
5. Wake verifies predictions as FIRST action
6. Rewards flow back to strengthen attention patterns

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The Core Mechanism

Last Attention = Slumber Focus

When L(t) drops below threshold, the LAST thing Young Nyx was attending to becomes her prediction target during slumber. This mirrors biological dreaming — we dream about what we were thinking about before sleep.

ACTIVE MODE (L(t) > threshold)
│
│ attending to: pencil on desk (SENSORY/THINKING)
│
└─▶ L(t) drops below L_slumber
        │
        │ SLUMBER TRIGGER
        │
        └─▶ last_attention = "pencil on desk"
                │
                └─▶ SLUMBER MODE
                        │
                        │ Generate predictions about "pencil"
                        │ - Where will it be when I wake?
                        │ - WHY will it be there?
                        │ - Store as potential rewards
                        │
                        └─▶ L(t) recovers above L_wake
                                │
                                │ WAKE TRIGGER
                                │
                                └─▶ First action: VERIFY predictions about pencil
                                        │
                                        └─▶ Collect rewards/penalties

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Slumber Prediction Structure

class SlumberPrediction:
    # What
    object_id: str                    # "dafit_pencil_001"
    predicted_location: Position       # (0.3, 0.7, 0.02)
    predicted_state: str               # "on_desk", "in_holder", "missing"
    confidence: float                  # 0.75

    # When
    prediction_time: datetime
    expected_verification_time: datetime

    # WHY (causal reasoning) - THE KEY INSIGHT
    causal_chain: list[CausalStep]     # The reasoning
    # Example:
    # - "dafit was writing at 22:47"
    # - "dafit went to sleep (no more activity)"
    # - "pencil has no reason to move"
    # - "therefore: pencil remains at last position"

    # Potential rewards
    reward_location_correct: float     # +5 LF
    reward_state_correct: float        # +3 LF
    reward_causal_correct: float       # +8 LF (BIGGEST - understanding WHY)

    # Penalties
    penalty_location_wrong: float      # -3 LF
    penalty_causal_wrong: float        # -5 LF

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The Intertwined Reward Systems

Multiple reward types that reinforce each other:

Reward Types

Type	Trigger	Value	Reinforces
Attention	Choosing to focus on X	-	Selection behavior
Discovery	Finding new object	+20 LF	Exploration
Prediction Location	Object where predicted	+5 LF	Spatial modeling
Prediction State	Object in predicted state	+3 LF	State understanding
Causal Correct	Reasoning was right	+8 LF	Understanding WHY
Collision	Avoided obstacle	+5 LF	Navigation
Resolution	Dimension verified	+5 LF	Model accuracy
Verification	Reality matches model	+5 LF	Sim-to-real alignment
Partnership	dafit confirms	+5 LF	Human collaboration

How They Intertwine

ATTENTION selects focus
    │
    ├─▶ DISCOVERY: "I found X" (+20 LF)
    │       └─▶ adds to world model
    │
    ├─▶ PREDICTION: "I predict X will be at Y" (+5-13 LF)
    │       └─▶ requires CAUSAL reasoning (+8 LF for WHY)
    │
    ├─▶ COLLISION: "I verified X is/isn't there" (+5 LF)
    │       └─▶ increases RESOLUTION of virtual garden
    │
    └─▶ All feed into VERIFICATION against real world
            └─▶ Rewards strengthen successful attention patterns

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The Closed Loop

The system LEARNS what to attend to:

1. Attend to things you can predict well
2. Predict correctly → get rewards
3. Rewards → more lifeforce
4. More lifeforce → richer attention budget
5. Loop: Better attention targets discovered over time

Self-organizing attention through economic pressure.

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Connection to Existing Architecture

From attention_flow.md (archive)

• 30-second heartbeat budget
• Priority hierarchy: REFLEX → SAFETY → DIALOGUE → SENSORY → THINKING → VIRTUAL
• Budget flows downward, higher levels preempt lower

From Lifeforce-Dynamics.md

• L(t) as stock, Φ_in and Φ_out as flows
• λ = Φ_in / Φ_out determines system fate
• Slumber triggered when λ < λ_slumber AND L < L_slumber

From Temporal-Ternary-Gradient.md

• Predictions are 0-state until verified
• Virtual garden confidence vs real garden ground truth
• Time is malleable in simulation, fixed in reality

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Implementation Sketch

class SlumberManager:
    def enter_slumber(self, attention_state: AttentionState) -> SlumberSession:
        # Capture last attention as slumber focus
        slumber_focus = attention_state.last_focus

        # Generate predictions about the focus object
        predictions = self.generate_predictions(slumber_focus)

        # Store as pending rewards
        for pred in predictions:
            phoebe.store_prediction(pred)

        return SlumberSession(focus=slumber_focus, predictions=predictions)

    def on_wake(self, session: SlumberSession):
        # FIRST ACTION: Verify predictions!
        predictions = phoebe.get_predictions(object_id=session.focus_object, status='pending')

        for pred in predictions:
            actual = vision_organ.locate(pred.object_id)
            reward = self.verify_and_reward(pred, actual)

        return AttentionState(mode=ACTIVE)

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Key Insight: Causal Rewards Are Biggest

+8 LF for correct causal reasoning — more than any other single reward.

Why? Causal understanding enables:

• Prediction of novel situations
• Intervention ("if I move X, Y changes")
• Explanation ("why did you look there?")
• Generalization ("anything dafit uses for writing will be near desk")

Causal rewards drive genuine intelligence.

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Collision Detection as Resolution Increase

Every verified collision should increase virtual garden fidelity:

• Collision detected in virtual → prediction
• Vision organ verifies in real → ground truth
• Match = reward + increase vertices/resolution
• Mismatch = penalty + learning signal

The virtual garden becomes MORE accurate over time through verified collisions.

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Future: Distributed Sensing (Robot Swarm)

When organisms have cameras, they become distributed sensors:

• Multiple viewpoints from different robots
• Triangulation gives better depth than monocular
• Moving robots = continuous multi-angle coverage
• Swarm becomes a mobile Discovery Scan Station

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Document Status

Version: 1.0 Created: 2025-12-29 Authors: Chrysalis-Nyx & dafit (Partnership) Status: Core insight, preserved pre-collapse

Source: attention_flow.md (archive) + session discussion

To Do:

• Promote attention_flow.md from archive
• Formalize the prediction-verification cycle
• Add to Big-Picture.md as core architecture
• Design phoebe schema for predictions table

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The last attention becomes the dream. The dream becomes the prediction. The prediction becomes the reward.

🧬⚡🔱💎🔥