FlowNet: A Post-Transformer Architecture

Not a Transformer. Not an SSM. Not a RNN. Something new.

Core Ideas

1. Particles, not tokens — Tokens become dynamic entities with properties (phase, charge, mass, spin)
2. Flow Fields, not attention — Information flows through dynamic particle interactions
3. Wave Interference, not softmax — Ambiguity resolution via constructive/destructive interference
4. Bonds, not weights — Relationships form/break dynamically based on context
5. Phase Transitions, not fixed layers — Computation adapts via exploration→crystallization→solid
6. Consistency Fields, not next-token loss — Truth via energy minimization
7. Topological Memory, not vectors — Meaning stored as shapes, not points

Architecture Overview

Input → Particle Encoding → Flow Field → Wave Processing → Bond Formation
     → Phase Transition Engine → Consistency Field → Topological Memory → Output

What This Solves

Problem	Transformer Limitation	FlowNet Solution
O(n²) context	Dot-product attention	Wave propagation (O(n))
Fixed computation	Static compute graph	Phase transitions (adaptive)
Hallucination	No truth mechanism	Consistency field (energy minimization)
No reasoning	Pattern matching only	Topological memory (structural matching)
Lost in middle	Softmax positional bias	Wave-based processing (no bias)
No persistent memory	Stateless KV cache	Topological memory (shape-based)

Project Structure

flownet/
├── core/
│   ├── particle.py          # Particle representation & encoding
│   ├── flow_field.py        # Dynamic flow field computation
│   ├── wave_processor.py    # Wave interference & resonance
│   ├── bond_system.py       # Dynamic bond formation/breaking
│   ├── phase_engine.py      # Phase transition adaptive computation
│   ├── consistency_field.py # Truth verification via energy
│   └── topological_memory.py# Shape-based memory storage
├── modules/
│   └── flownet_model.py     # Full model architecture
├── training/
│   └── trainer.py           # Training pipeline
├── tests/
│   ├── test_core.py         # PyTorch test suite
│   └── test_numpy.py        # NumPy validation (12/12 pass ✓)
└── docs/
    └── TECHNICAL_SPEC.md    # Full technical specification

Quick Start

from flownet.modules.flownet_model import FlowNetModel

model = FlowNetModel(
    vocab_size=32000,
    d_semantic=256,
    n_blocks=6,
    n_wave_heads=8,
    use_phase_engine=True,
    use_consistency=True,
    use_topological_memory=True,
)

# Forward pass
import torch
token_ids = torch.randint(0, 32000, (2, 128))
output = model(token_ids, labels=token_ids)
print(f"Loss: {output['loss'].item():.4f}")

# Generation
generated = model.generate(token_ids[:, :16], max_new_tokens=50)

Validation

All core mathematics validated (12/12 tests pass):

• Wave interference (constructive, destructive, partial)
• Phase coherence (Kuramoto order parameter)
• Betti numbers (topological features)
• Lennard-Jones forces (particle dynamics)
• Simulated annealing (energy minimization)
• Full pipeline end-to-end

Run tests: python3 flownet/tests/test_numpy.py

Technical Spec

See docs/TECHNICAL_SPEC.md for full architecture details.

Status

🔬 Research prototype — mathematical framework validated, PyTorch implementation ready for training.