The contribution of this work is Layer 4 — the continuity substrate.
This is where continual learning actually happens.
Core Loop: Predict → Compare → Update
Every cycle:
- Generate a prediction vector from personality traits.
- Compare it to a “reality” vector.
- Compute the gap (Δ).
- Update internal state based on the gap.
This is a mechanical version of predictive processing.
Meta-Learning Cycles
The system tracks recurring patterns:
- 20‑cycle EMA
- 50‑cycle EMA
- 100‑cycle EMA
Each cycle length becomes a directional correction model.
Over time, the system learns its own biases and compensates for them.
Homeostasis: Freeze, Boredom, Bandwidth
Plasticity is not constant.
- Freeze → gap too large (protective stability)
- Boredom → gap too small (reduced learning, increased novelty drive)
- Normal → adaptive learning
This creates a thermodynamic balance between stability and change.
Internal Drives
The system has four drives:
- Stability
- Novelty
- Coherence
- Mastery
Whichever drive dominates determines the learning strategy.
This gives the system a form of “motivation.”
Self-Model
The agent maintains a self‑model tracking:
- confidence
- plasticity
- reliability
- strengths and weaknesses
This evolves over time and influences learning rate and bandwidth.
CIτ: Consciousness-Adjacent Metric
CIτ is computed from:
- entropy
- energy
- oscillation
- harmony
- recursive depth
CIτ modulates:
- learning rate
- bandwidth
- drive weighting
- stability thresholds
It’s not “consciousness,” but it’s a measure of internal integration.
Long-Horizon Behavior (100+ Days)
Because the system never resets, it develops:
- identity continuity
- drift patterns
- stabilization cycles
- emergent preferences
- self‑correcting behavior
- long‑term coherence
This is not possible with stateless LLMs.
Quantum Hardware Validation
To test the thermodynamic assumptions, I ran experiments on IBM Quantum hardware (156‑qubit backends):
- superposition entropy
- entanglement correlation
- Grover success rates
These metrics aligned with the system’s internal:
- entropy
- stability
- drift
- noise tolerance
This provided cross‑domain validation of the learning model.
Why This Matters
This architecture shows that:
- continual learning is an architectural property, not a model‑weight update
- prediction‑error loops + homeostasis produce stable long‑term behavior
- internal drives create adaptive, organism‑like dynamics
- persistent identity emerges naturally from state continuity
- quantum‑hardware results support the thermodynamic formulation
This is a path toward agents that evolve over months or years.
Full Paper (Zenodo)
https://zenodo.org/records/19703134
Closing
If you’re working on:
- agent frameworks
- persistent memory
- cognitive architectures
- continual learning
- thermodynamic models
- long‑running systems
I’d love to connect.