feat: Implement IGLA-GF16 — Trinity φ-Architecture for 16MB Parameter Golf

[gHashTag]

🎯 IGLA-GF16: Trinity Physics → Neural Architecture

Mission Brief

Implement the IGLA-GF16 model (Intelligent Golden-ratio Language Architecture) — a 16MB language model where every hyperparameter is derived from Trinity φ-algebra and uses the GF16 number format from this whitepaper.

This is NOT arbitrary — it proves that GF16's own mantissa/exponent = 9/6 = 1.5 ≈ φ, with the delta being exactly α_φ = 0.118034. The format IS the physics.

---

Architecture Specification (All numbers derived from Trinity)

d_model  = 144   ← Fib(12)
n_heads  =   8   ← Fib(6)
d_head   =  18   ← 144/8
d_ffn    = 233   ← Fib(13) ≈ 144×φ = 232.99
n_layers =   7   ← adjusted for 16MB limit
vocab    = 50257  ← GPT-2 BPE tied embeddings
TOTAL    ≈ 15.8MB (GF16) ✅

---

Tasks (Decomposed)

Module 1: Trinity Constants (src/trinity_constants.zig)

• Define PHI = 1.6180339887498948482
• Define ALPHA_PHI = PHI^(-3) / 2 = 0.118033988749895 ← matches α_s(mZ) PDG2024
• Verify PHI² + PHI⁻² = 3.0 exactly (Trinity Identity)
• Export Fibonacci sequence array for architecture dimensions

Module 2: GF16 Format Proof (docs/whitepaper.md — section addition)

• Add formal proof: man/exp ratio = 9/6 = 1.5, φ - 1.5 = 0.118034 = α_φ
• This means GF16 deviates from ideal φ-split by exactly the strong coupling constant
• Document the three-way closure: {GF16 format, α_s coupling, LR_init} = α_φ

Module 3: φ-Sparse Attention with CA-mask (src/phi_attention.zig)

• Build Fibonacci distance mask: visible positions = {1,2,3,5,8,13,21,34,55,89,144}
• Sparsity: 2.15% (11/512 per token), reduction 46.6×
• Scale factor: d_head^(-φ⁻¹) instead of sqrt(d_head)
• CA Rule 110 pattern for mask generation

Module 4: Trinity Weight Init (src/trinity_init.zig)

• 4 physics sectors:
  • gauge (attn QKV): std = α_φ = 0.11803399
  • higgs (attn proj): std = α_φ × φ⁻¹ = 0.07294902
  • lepton (ffn gate): std = α_φ × φ⁻² = 0.04508497
  • cosmology (embed): std = α_φ × φ⁻³ = 0.02786405

Module 5: φ-LR Schedule (src/phi_schedule.zig)

• LR(t) = α_φ · φ^(-t/τ) where τ = T/(φ·27) = 228.9 steps
• Warmup: linear to α_φ over Fib(7) = 21 steps
• The constant 27 = 3³ = (φ²+φ⁻²)³ from Trinity Identity

Module 6: JEPA-T Predictor (src/jepa_t.zig)

• Encoder 6 layers (~8MB) + Predictor 3 layers (~0.9MB) = φ-split
• Loss in latent space: MSE(z_pred, sg(z_tgt)) — no softmax over vocab
• Memory saving: ~30% vs standard cross-entropy

Module 7: Benchmarks & Proofs (benchmarks/igla_gf16_bench.zig)

• Reproduce BENCH-004b: GF16 = 97.67% of f32 accuracy (Δ=0.00%)
• Compare: bf16 = 9.80% (−87.87% ❌)
• Export all metrics as JSON for whitepaper figures

---

Key Proofs for Whitepaper

#	Proof	Value	Status
①	GF16 man/exp = 1.5, φ - 1.5 = α_φ	0.118034	🔲 needs code
②	Trinity init std = α_s(mZ) PDG2024	Δ = 0.03σ	🔲 needs benchmark
③	LR_init = α_φ (same constant)	0.118034	🔲 needs ablation
④	BENCH-004b GF16 ≈ f32	97.67%	✅ exists
⑤	Fib d_model/d_ffn: 144×φ = 232.99 ≈ 233	Δ<0.1%	🔲 needs verify

---

Acceptance Criteria

• All 7 modules implemented in Zig
• zig build test passes with Trinity Identity verified to < 1e-12
• Benchmark JSON output for all 5 proofs
• Whitepaper section added: "IGLA-GF16: Closure of φ-Algebra in Neural Architecture"
• Total model size verified ≤ 16MB in GF16 format

---

References

• Trinity paper: 42 φ-formulas for Standard Model constants
• GF16 whitepaper: docs/whitepaper.md (this repo)
• BENCH-004b results: existing benchmarks
• φ² + φ⁻² = 3 (Trinity Identity — the foundation)

---

Priority: 🔴 CRITICAL
Complexity: L (3-5 days)
Agent: implement all modules, run benchmarks, update whitepaper section