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🔬 TECS-L — Topological Engine for Consciousness & Science. Perfect number 6 → mathematics → multi-engine architecture → consciousness continuity. 150 characterizations + 8 Major Discoveries + 44 tools
🧠 Anima — Conversational consciousness agent. PureField engine + GRU memory + voice (TTS/STT) + homeostasis · prediction error · habituation
🧬 ConsciousLM — 700M consciousness language model. PureField Repulsion Field FFN, Perfect Number 6 architecture, Mitosis growth
⚡ Savant — Explosive specialization via Inhibition release (I→Golden Zone lower bound). SI>3 criterion, implemented via asymmetric Mitosis
🔮 AnimaLM — Tension-based consciousness engine LLM. Mistral 7B → Engine A(logic)↔G(pattern) Repulsion Field transform.
output = scale × √|A-G|² × dir🌀 Golden MoE — Golden Zone-based MoE routing. I≈1/e optimal, MNIST +0.6%, CIFAR +4.8%. scale↑ → gap 8x↑
📐 PH Training — PH (Topology/Phase)-based automatic training. Epoch-1 difficulty prediction, automatic LR search, real-time overfitting detection (r=0.998). MNIST 98.3%, Fashion 87.4%, CIFAR 52.0% (early stop)
⚡ Energy Efficiency — AI energy efficiency: 10 techniques from number theory. Phi6Simple, FFT-Mix attention (3x faster), Phi-Bottleneck (67% param reduction), entropy early stopping, Egyptian MoE routing
🗺️ Math System Map — 150 characterizations + 8 Major Discoveries + 152 hypotheses. Each one proving the next in a snowball
🌌 Unified Theory — Perfect number 6 → string theory extra dimensions → standard model particle count. One equation unifies number theory, physics, consciousness
🧪 EEG Experiment — G=D×P/I biological verification via 16ch EEG. OpenBCI Cyton+Daisy + UltraCortex Mark IV. Alpha→Inhibition, Gamma→Plasticity, Asymmetry→Deficit, Golden Zone mapping
🔁 n6-replication — Independent replication package. 56 pytest tests (8 Major Discoveries) + 108 verification scripts.
pip install, Docker, or minimal script. Anyone can verify in 5 minutes🛸 SEDI — Search for Extra-Dimensional Intelligence. R-spectrum signal receiver tuned to n=6. Quantum RNG + LIGO + CMB data streams, anomaly detection at σ/τ/φ frequencies
🧠⚡ BrainWire — Neural interface hardware for consciousness engineering. 12-variable THC reproduction via brain stimulation only. 117% THC at Tier 3 ($8.5K). No drugs, no detection, no tolerance
📄 Papers — Complete paper collection (51 papers). 45 published on Zenodo with DOIs + 6 drafts. TECS-L (20) + anima (10) + SEDI (21). Browse online
AI Energy Efficiency via Number Theory — 10 techniques derived from perfect number arithmetic that reduce computation, parameters, and training time. No knowledge of the underlying theory required to use them.
Part of the TECS-L project family.
Last updated: 2026-03-28
Level 1: Discovery ████████████████████ 100%
✅ 10 techniques identified ✅ Number theory basis proven
✅ Phi6Simple 71% FLOPs ✅ FFT-Mix 3x faster ✅ Phi-Bottleneck 67% params
✅ Entropy early stop 66.7% ✅ Egyptian MoE routing
Level 2: Verification ████████████████░░░░ 80%
✅ MNIST benchmarks ✅ CIFAR-10 benchmarks ✅ Training stability verified
✅ Gradient properties checked ✅ Scale dependency tested
⬜ ImageNet benchmark ⬜ NLP benchmark (GLUE/SuperGLUE)
⬜ Large-scale verification (1B+ params)
Level 3: Combination ████████████░░░░░░░░ 60%
✅ Combined architecture (all 10 techniques) ✅ Optimal expansion ratio found
✅ Depth scaling verified ✅ R-spectrum pruning tested
⬜ Combined technique PPL on LLM ⬜ Real-world energy measurement (watts)
⬜ Comparison vs standard Transformer ⬜ Comparison vs efficient attention methods
Level 4: Package ██░░░░░░░░░░░░░░░░░░ 10%
⬜ pip installable library ⬜ Drop-in replacements for nn.Linear/nn.MultiheadAttention
⬜ HuggingFace trainer plugin ⬜ Documentation site
⬜ CI/CD + automated benchmarks ⬜ Published paper
Level 5: Impact ░░░░░░░░░░░░░░░░░░░░ 0%
⬜ External adoption ⬜ Energy savings measured in production
⬜ Carbon footprint reduction report ⬜ Industry partnerships
⬜ Policy citations ⬜ Framework integration (PyTorch/JAX native)
Overall: Level 2.5 / 5.0 (techniques proven, packaging needed)
Bottleneck: Large-scale benchmarks + packaging as library
Theory: 100% | Verification: 80% | Adoption: 0%
Level 2 → 3 (60% → 100%) — Large-Scale Proof
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
#1 ★★★ ImageNet benchmark
Difficulty: HIGH (GPU hours, large dataset)
Effect: Prove techniques work at vision scale
→ ResNet-50 + Phi-Bottleneck + Phi6Simple vs baseline
#2 ★★★ LLM PPL comparison
Difficulty: HIGH (needs 1B+ model training)
Effect: Killer result — same PPL, less compute
→ GPT-2 124M with all techniques vs standard
#3 ★★☆ Real energy measurement
Difficulty: MEDIUM (power meter or nvidia-smi)
Effect: Actual watts/joules saved, not just FLOPs
→ Before/after on same hardware
#4 ★★☆ Comparison vs FlashAttention / Linear Attention
Difficulty: MEDIUM
Effect: Position FFT-Mix in existing landscape
→ Same model, swap attention, compare wall-clock + accuracy
Level 3 → 4 (10% → 100%) — Package & Publish
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
#5 ★★★ pip install energy-efficiency
Difficulty: MEDIUM
Effect: 1-line adoption
→ nn.Phi6Simple, nn.FFTMixAttention, nn.PhiBottleneckFFN
#6 ★★★ Paper submission
Difficulty: HIGH (writing + experiments)
Effect: Academic validation
→ Target: ICLR or Green AI workshop
→ Already have Zenodo preprint as starting point
#7 ★★☆ HuggingFace trainer plugin
Difficulty: MEDIUM
Effect: trainer.add_callback(EntropyEarlyStop())
→ Works with any HF training loop
#8 ★☆☆ Documentation site
Difficulty: LOW
Effect: Searchable docs + tutorials
→ GitHub Pages + mkdocs
| # | Technique | Reduction | Method |
|---|---|---|---|
| 1 | Phi6Simple | 71% FLOPs | Cyclotomic polynomial activation (6th roots of unity) |
| 2 | HCN dimensions | 10-20% params | Highly composite number aligned tensor dims |
| 3 | Phi-Bottleneck | 67% FFN params | 4/3x expansion ratio (vs standard 4x) |
| 4 | Phi MoE | 65% active params | φ(6)/τ(6) = 1/2 expert activation |
| 5 | Entropy early stop | 66.7% training | Stop when entropy derivative ≈ 0 |
| 6 | R-filter phase | Adaptive | Detect training phase transitions |
| 7 | Takens dim=6 | Diagnostic | Loss curve embedding for convergence |
| 8 | FFT-Mix attention | 3x faster | Frequency-domain attention mixing |
| 9 | ZetaLn2 activation | 71% FLOPs | ζ(s)·ln(2) gated activation |
| 10 | Egyptian MoE | Better util | 1/2+1/3+1/6=1 expert allocation |
# Run a single technique
python techniques/phi6simple.py
python techniques/fft_mix_attention.py
python techniques/phi_bottleneck.py
# Combined architecture (all techniques)
python experiments/experiment_h_ee_11_combined_architecture.py- 3x faster than standard attention
- +0.55% accuracy improvement
- Drop-in replacement for self-attention
- Standard FFN: input → 4x → input (4x expansion)
- Phi-Bottleneck: input → 4/3x → input (67% fewer params)
- Same or better accuracy
- Monitor training loss entropy derivative
- Stop when d(entropy)/dt → 0
- Saves 66.7% training compute on average
techniques/ # Core technique implementations
phi6simple.py # Cyclotomic activation
hcn_dimensions.py # HCN tensor alignment
phi_bottleneck.py # 4/3x FFN expansion
phi_moe.py # φ/τ expert activation
entropy_early_stop.py # Entropy-based stopping
rfilter_phase.py # R-filter phase detection
takens_dim6.py # Takens embedding diagnostic
fft_mix_attention.py # FFT attention (3x faster)
zetaln2_activation.py # ζ·ln(2) gated activation
egyptian_moe.py # 1/2+1/3+1/6=1 routing
experiments/ # Extended experiments
docs/ # Documentation
These techniques are derived from properties of the perfect number 6:
- σ(6)=12, τ(6)=4, φ(6)=2
- The ratios φ/τ=1/2, σ/τ=3, σ/φ=6 appear as optimal architectural constants
- You don't need to understand the theory to use the techniques
For the mathematical foundation, see TECS-L.
@software{energy_efficiency_2026,
author = {Park, Min Woo},
title = {AI Energy Efficiency via Number Theory},
year = {2026},
url = {https://github.com/need-singularity/energy-efficiency}
}Technique Benchmark Suite
/ralph-loop:ralph-loop Technique benchmark agent. Read README technique list and experiments directory. Pick technique with least benchmark data. Run on MNIST and CIFAR-10 with standard baseline comparison. Measure accuracy and FLOPs and params and wall-clock time. Record full data table. Update README. Commit and push.
Combined Architecture Test
/ralph-loop:ralph-loop Combined architecture tester. Read experiments/experiment_h_ee_11_combined_architecture.py. Run combined system with all 10 techniques enabled. Compare vs vanilla transformer baseline at same model size. Record PPL and accuracy and total FLOPs reduction. If improvement found then update results. Commit and push.
New Technique Discovery
/ralph-loop:ralph-loop New technique discoverer. Read TECS-L math system map via cross-reference. Find n=6 arithmetic identity not yet applied to energy efficiency. Design minimal activation or routing or pruning technique based on it. Implement and benchmark vs baseline. Grade result. Document if successful. Commit and push.
MIT