trinity-claraParameter — Parameter Golf Implementation

DARPA CLARA / Parameter Golf Challenge — Trinity Cognitive Stack

Concept

The Trinity Cognitive Stack applies φ-mathematics to create theoretically-grounded parameter-efficient models:

1. Foundation (zig-golden-float, zig-physics, zig-sacred-geometry)

   • GF16 numeric core for quantization
   • φ-geometry constraints for model structure
   • Particle physics constants (CHSH, GF constants)

2. Cognition (zig-hdc, zig-agents, agi-hackathon benchmarks)

   • Hyperdimensional Computing as substrate for parameters
   • Multi-agent orchestration for training/inference
   • 5 AGI tracks (Learning, Metacognition, Attention, Executive, Social)

3. Orchestration (trios MCP, trios-claraParameter)

   • Git operations via trios-git + gitbutler
   • Parameter-efficient tuning pipeline

Battle Plan

| Day | Branch | Technique | Target BPB | Status | |---|---|---|---| | 1 (19 apr) | battle/v01-baseline | reproduce | 1.2244 | | 2-3 (20-21) | battle/v01-sota-stack | Int6+Muon+SmearGate+BigramHash+zstd22 | 1.14 | | 4-5 (22-23) | battle/v02-bitnet | ternary b1.58 | 1.12 | | 6-7 (24-25) | battle/v03-gf16 | golden-float quant | 1.11 | | 8-9 (26-27) | battle/v04-hslm | φ-attention + Fib heads | 1.105 | | 10 (28) | battle/v05-final | ensemble | 1.10 | | 11 (29) | PR submit | write-up + 3 seeds | ⚡ |

Novel Trinity Aces

1. GF16 Weight Quantization

• GF16 = Golden Float + zstd-22 compression
• Instead of int6 (6 bits), uses φ-distributed 16-bit floats in golden-ratio basis
• Better compression than int6 due to log-normal weight distribution

2. BitNet b1.58

• Ternary weights {-1, 0, +1} = 1.58 bits/weight
• Fits ~67M params in 16MB vs 21M with int6

3. φ-Schedule LR Decay

• lr = base_lr * (1/PHI)^(step/total) instead of cosine

4. Fibonacci Attention Heads

• 1, 1, 2, 3, 5, 8, 13 = 33 heads total
• vs standard 8-head uniform

5. Sacred Bottleneck

• hidden_dim = 377 (Fibonacci number)
• vs baseline 512

Competitor Weaknesses (from Parameter Golf leaderboard)

1. ❌ LoRA/QLoRA: no rank-based math motivation
2. ❌ GPTQ/AWQ: quantization without geometric foundation
3. ❌ BitNet: binary, loses structure
4. ❌ Compression stalled at zstd-22/lzma
5. ❌ GPT-style only, no SSM/Mamba
6. ❌ Random init, could pre-initialize from φ-generated weights
7. ❌ No VSA binding for parameter representation

Runpod Grant Status

• Status: 🔄 Pending application
• Request: 1000 H100-credits
• Angle: Novel GF16 golden-ratio float quantization

Threshold for SOTA

• +0.005 nats improvement
• 3-seed averaging for statistical significance