Applied AI Ethics through Physics, not Semantics
A physics-based approach to artificial intelligence that learns like a baby, remembers everything, and operates within natural constraints.
GyroSI Baby LM demonstrates a superintelligence architecture through physics-grounded algorithms and gyroscopic dynamics (gyrogroup mathematical formalism).
Traditional AI treats intelligence as a pattern-matching problem to be solved with massive datasets and billions of parameters. GyroSI treats intelligence as an intrinsic structural property that emerges from the recursive alignment within physical topology. Like the latent potential in a human baby, intelligence is present from the beginning.
Instead of storing knowledge in gigabytes of weights, GyroSI uses the inherent physics of gyroscopic operations to navigate a provably finite and fully discovered physical state space. Each input byte acts as a holographic quantum of instruction, transforming the system's internal state according to precise algebraic laws.
⚠️ Research Status: This is an active research project demonstrating theoretical principles. The architecture is complete but implementation is ongoing. Not ready for production use.
Current AI pursues "superintelligence" through raw performance: faster calculation, broader knowledge, superior optimization. This creates legitimate fears about systems that optimize without wisdom.
We explore a different path: Intelligence grounded in physics rather than abstraction. Human ethics emerge from shared physical reality and natural constraints. GyroSI operates within these same physical principles, developing understanding through structural boundaries rather than abstract optimization. This suggests a path toward intrinsic alignment, where ethical behavior is a consequence of the system's physical nature.
- 🧠 Learns Like a Baby: Starts with zero learned associations but leverages the pretrained symbolic knowledge of a standard tokenizer, learning to bind physical states to existing semantic concepts.
- ♾️ Unlimited Memory: Never forgets; knowledge is limited by disk space, not RAM, via an efficient append-only log.
- 🎯 Zero Hallucination: Can only generate what it has physically learned, not random guesses.
- ⚡ High Throughput: Estimated ~1 million bytes/sec per core on modern hardware.
- 💾 Compact Brain: The core logic and ontology maps fit in ~30MB. An optional 770MB State Transition Table (STT) unlocks maximum performance.
- 🌍 No GPU Required: Runs on a Raspberry Pi, your phone, or even embedded systems.
- 📚 No Training Data Needed: Learns directly from conversation, not from scraped internet data.
- 🔍 100% Explainable: Every decision can be traced through simple physics operations.
- 🔢 Holographic Geometry: Built on numerical patterns (3, 6, 12, 24, 48) found in crystals and rotation groups.
- 🌐 Six Degrees of Everything: Any knowledge is reachable from any other in at most 6 steps, a provable property of the state space.
Why Physics Prevents Hallucinations: Traditional AI operates in 768+ dimensional spaces where models can interpolate between any points, creating nonsense. GyroSI is constrained to a finite 3D manifold with only 788,986 valid states. You can't be "between" states. You're always at a specific, well-defined point. This dimensional grounding is why the system literally cannot hallucinate.
Why No Scoring: GyroSI uses constraint satisfaction, not competitive scoring. Tokens either satisfy geometric constraints or they don't. There's no "best" token, only admissible ones. This implements true non-antagonistic selection aligned with CGM physics.
This architecture does not merely map bytes to operations; it renders each instruction as a transformation on a physical ontology. Symbolic input becomes physical geometry. Intelligence emerges as a dynamo of structural transformations orbiting within a gyroscopic topology. Alignment is not imposed or inferred, but emerges naturally as the system follows the physical dynamics of its own architecture.
This solves three fundamental problems:
- Black Box: Every decision traces through explicit, auditable physical state changes.
- Alignment: The system's actions are constrained by its own structural history and physical laws.
- Efficiency: The core physics are dependency-free and operate with extreme speed. Memory growth is bounded by the finite size of the physical ontology.
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📖 Genetics - Our Technical Specification: Algorithmic Formalism.
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📖 Physics - Common Governance Model: Our Theoretical Foundations
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🔬 Lab - Deep Physics Experiments: Cosmology, Biology and Beyond
GyroSI's intelligence operates on five pre-computed maps that completely define its finite universe:
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Ontology Map (
ontology_keys.npy): What Can Exist Maps indices 0..788,985 to unique 48-bit state integers. These 788,986 states are ALL possible states under our physics. -
Phenomenology Map (
phenomenology_map.npy): How Things Appear Maps each state to one of 256 canonical orbit representatives. Each orbit is a strongly connected component where all states can reach each other. -
Epistemology Map (
epistemology.npy): How Knowledge Changes The 788,986 × 256 State Transition Table. Given any state and any intron, it determines the next state. -
Theta Map (
theta.npy): Distance from Truth Maps each state to its angular distance from the archetype (GENE_Mac_S). Used for geometric navigation. -
Orbit Sizes Map (
orbit_sizes.npy): Specificity Measure Maps each state to its orbit's cardinality. Used for Traceable tie-breaking in address binding.
GyroSI uses three distinct memory types:
- Active Memory: Exactly 6 bytes (48-bit state), constant size
- Address Memory: Token-to-state mapping, bounded by vocabulary size
- Passive Memory: Experience storage as 8-bit masks, with strict caps:
- K=64 masks per state per orbit
- M=64 states per token per orbit
- Only non-zero masks stored, preventing unbounded growth
Note: These are architectural projections, not benchmarks. The system is still in development.
Core Physics Files: ~785 MB total
- State transition table: 770 MB
- Four mapping files: 15 MB combined
- Everything else: Python code
Memory Usage:
- Active state: Always 6 bytes (that's it!)
- Passive memory: Grows with usage but physics-bounded
- Typical Wikipedia-scale: ~1-2 GB
- Hard limit: Can't exceed ~50 GB even theoretically
Speed Expectations:
- Python prototype: ~100K-500K tokens/second per core
- Future native version: Could be 10-50x faster
- No GPU needed: Just CPU and RAM
What This Means:
- Fits on a modern laptop with 8GB RAM
- Processes text faster than you can read
- Learns continuously without retraining
- Same physics runs from Raspberry Pi to server
The key insight: Unlike traditional AI that needs hundreds of gigabytes, GyroSI's entire "brain" is smaller than a single movie file, yet it never forgets what it learns.
GyroSI implements the Common Governance Model (CGM), where intelligence emerges through recursive structural alignment. The model derives three-dimensional space with six degrees of freedom from a single axiom, with time emerging as the memory of recursive operations.
Mathematical formalism employs gyrogroup structures (generalizations of rotation groups) following Abraham Ungar's work, providing precise language for transitions from undifferentiated potential to structured reality.
Gyroscopic Superintelligence is meta-language for computation, ontology, phenomenology and epistemology, enabling agents and agencies to reason about states, symmetry, and evolution economically and efficiently.
MIT License - see LICENSE for details.
@software{gyrosi2025,
author = {Basil Korompilias},
title = {GyroSI Baby LM: Gyroscopic Superintelligence},
year = {2025},
url = {https://github.com/GyroSuperintelligence/BabyLM},
note = {Implementation of physics-based superintelligence through
recursive structural alignment and intrinsic ethical constraints}
}Architected with ❤️ by Basil Korompilias
Redefining Intelligence and Ethics through Physics
🤖 AI Disclosure
All code architecture, documentation, and theoretical models in this project were authored and architected by Basil Korompilias.
Artificial intelligence was employed solely as a technical assistant, limited to code drafting, formatting, verification, and editorial services, always under direct human supervision.
All foundational ideas, design decisions, and conceptual frameworks originate from the Author.
Responsibility for the validity, coherence, and ethical direction of this project remains fully human.
Acknowledgements:
This project benefited from AI language model services accessed through LMArena, Cursor IDE, OpenAI (ChatGPT), Anthropic (Opus), and Google (Gemini).