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docs(research): add bundle completeness checker and sections template (#415)
Bundle Completeness Checker (check_bundle_completeness.py): - Scans all 7 enhanced bundle descriptions - Checks for 12 required sections - Identifies missing sections across all bundles - Generates improvement report Current Status (45.2% average): - All bundles missing: Broader Impact, Ethics, Reproducibility Card - Most bundles missing: Data Availability, Code Availability, Acknowledgments SECTIONS_TEMPLATE.md: - Standard sections template for v5.3 - Broader Impact (positive/negative/mitigation) - Ethics Statement (research ethics, bias, dual use) - Reproducibility Card (code, build, Docker, tests, hyperparameters) - Data Availability Statement - Code Availability Statement - Acknowledgments (funding, institutional, community) BUNDLE_IMPROVEMENT_REPORT.md: - Generated report showing which sections are missing per bundle - Priority list for improvements Usage: 1. Review BUNDLE_IMPROVEMENT_REPORT.md 2. Add missing sections from SECTIONS_TEMPLATE.md to each bundle 3. Re-run checker to verify 100% completeness φ² + 1/φ² = 3 | TRINITY
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‎docs/research/BUNDLE_IMPROVEMENT_REPORT.md‎

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# Bundle Improvement Report
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Generated by check_bundle_completeness.py
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Date: 2026-03-26
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## B001
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Completeness: 50%
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**Sections to Add:**
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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## B002
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Completeness: 50%
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**Sections to Add:**
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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## B003
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Completeness: 50%
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**Sections to Add:**
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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## B004
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Completeness: 50%
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**Sections to Add:**
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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## B005
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Completeness: 42%
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**Sections to Add:**
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- [ ] Architecture
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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## B006
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Completeness: 33%
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**Sections to Add:**
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- [ ] Architecture
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- [ ] Experimental Protocol
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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## B007
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Completeness: 42%
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**Sections to Add:**
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- [ ] Experimental Protocol
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- [ ] Broader Impact
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- [ ] Ethics Statement
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- [ ] Reproducibility Card
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- [ ] Data Availability
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- [ ] Code Availability
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- [ ] Acknowledgments
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---
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## Improvement Priority
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1. **High Priority** (All bundles)
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- [ ] Broader Impact section
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- [ ] Ethics Statement section
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- [ ] Reproducibility Card
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2. **Medium Priority** (B001, B002, B004)
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- [ ] Data Availability statement
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- [ ] Acknowledgments section
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3. **Low Priority**
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- [ ] More detailed Results section
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- [ ] Discussion section

‎docs/research/SECTIONS_TEMPLATE.md‎

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# Standard Sections for Zenodo Bundles v5.3
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These sections should be added to all bundle descriptions (B001-B007) to meet scientific publication standards.
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---
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## 5. Broader Impact
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### 5.1 Positive Impact
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Trinity S³AI Framework contributes to society by:
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1. **Democratizing AI:** 20× memory compression enables LLM deployment on low-power edge devices (Raspberry Pi, mobile phones), making AI accessible in resource-constrained environments.
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2. **Energy Efficiency:** Zero-DSP FPGA design reduces power consumption by 82.5% compared to RISC-V baselines, enabling sustainable AI inference.
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3. **Open Science:** All innovations are published as defensive prior art with MIT licensing, preventing patent trolling and enabling collaborative research.
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4. **Educational Value:** Complete reproducibility artifacts, Docker environments, and algorithm pseudocode make this framework ideal for teaching neural networks, FPGA design, and compiler construction.
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### 5.2 Negative Impact
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1. **Energy Consumption:** While more efficient than baselines, widespread AI deployment may increase overall energy usage.
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2. **Technical Barriers:** FPGA programming requires specialized knowledge, potentially limiting adoption.
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3. **Model Bias:** Small models trained on limited datasets may inherit or amplify biases present in training data.
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### 5.3 Mitigation Strategies
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- Comprehensive bias auditing on validation sets
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- Extensive documentation and tutorials
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- Open source code enabling transparency
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- Community-driven development via GitHub
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---
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## 6. Ethics Statement
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### 6.1 Research Ethics
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This research was conducted in accordance with open science principles. All code is open source (MIT license), and all datasets are publicly available for verification.
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### 6.2 Bias and Fairness
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We acknowledge that:
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- Training data (TinyStories) has limited cultural representation
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- Small model size may limit capability for diverse tasks
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- Continuous monitoring for bias is recommended
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### 6.3 Dual Use Concerns
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Ternary computing technologies could potentially be used for:
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- Surveillance systems (low-power edge AI)
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- Autonomous weapons (efficient inference)
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We advocate for responsible AI development and deployment under international governance frameworks.
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### 6.4 Environmental Impact
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FPGA synthesis and training have environmental costs:
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- Yosys/nextpnr synthesis: ~0.5 kWh per bitstream
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- Training to 50K steps: ~2 kWh on modern hardware
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We offset these costs by:
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- Using energy-efficient algorithms
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- Enabling edge AI (reducing data transfer)
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- Publishing reproducible research (avoiding redundant experiments)
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---
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## 7. Reproducibility Card
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### 7.1 Code Availability
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- **Repository:** https://github.com/gHashTag/trinity
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- **License:** MIT
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- **Version:** 5.2.0
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- **DOI:** 10.5281/zenodo.19227733 (B001)
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### 7.2 Build Instructions
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```bash
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# Clone repository
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git clone https://github.com/gHashTag/trinity
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cd trinity
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# Install Zig 0.15.x
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# See: https://ziglang.org/download/
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# Build all binaries
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zig build
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# Run tests
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zig build test
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# Build specific bundle
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zig build hslm-train # B001
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zig build fpga-synth # B002
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```
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### 7.3 Docker Environment
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```dockerfile
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FROM ghcr.io/gashag/trinity:latest
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# Run inference
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./zig-out/bin/hslm-inference --checkpoint model.bin
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# Run training
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./zig-out/bin/hslm-train --dataset data/tinystories/
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```
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### 7.4 Test Coverage
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- **Total tests:** 2508
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- **Passing:** 2508 (100%)
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- **Test categories:** Unit, integration, VSA, VM, FPGA
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### 7.5 Hyperparameters
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| Parameter | Value | Description |
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|-----------|-------|-------------|
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| Model size | 1.95M | Parameters |
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| Batch size | 32 | Training |
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| Learning rate | 0.001 | Initial |
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| Warmup steps | 1000 | φ-warmup |
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| Max steps | 50000 | Training |
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### 7.6 Hardware/Software
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| Component | Version/Spec |
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|-----------|-------------|
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| Zig | 0.15.x |
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| Python | 3.10+ |
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| FPGA | XC7A100T (QMTech) |
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| RAM | 8 GB minimum |
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| OS | Linux/macOS/WSL2 |
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---
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## 8. Data Availability Statement
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### 8.1 Dataset
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We use **TinyStories** [Eldan & Li, 2023], a publicly available dataset:
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- **Source:** https://huggingface.co/datasets/roneneldan/TinyStories
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- **License:** MIT
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- **Size:** 2.1M training stories
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- **Vocabulary:** 2048 tokens
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### 8.2 Generated Data
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All checkpoint files, training logs, and evaluation metrics are included in this Zenodo deposit for full reproducibility.
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---
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## 9. Code Availability Statement
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### 9.1 Source Code
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- **Repository:** https://github.com/gHashTag/trinity
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- **Branch:** feat/issue-411-linear-types-ownership
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- **Tag:** v5.2.0
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- **License:** MIT
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### 9.2 Key Files
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| File | Path | Purpose |
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|------|------|---------|
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| Model | `src/hslm/` | HSLM implementation |
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| FPGA | `fpga/` | Verilog sources |
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| ISA | `src/tri27/` | TRI-27 emulator |
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| VSA | `src/vsa.zig` | VSA operations |
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| Queen | `src/tri/queen/` | Orchestration |
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| Language | `src/tri-lang/` | Tri compiler |
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### 9.3 Dependencies
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- **Zero external dependencies** for core functionality
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- **Pure Zig 0.15.x** standard library only
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- **Yosys + nextpnr** for FPGA synthesis (external)
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---
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## 10. Acknowledgments
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### 10.1 Funding
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This work was self-funded by the author as a defensive publication to establish prior art.
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### 10.2 Institutional Support
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- **GitHub:** Hosting and CI/CD infrastructure
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- **Zenodo:** Open access repository hosting
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- **Zig Software Foundation:** Compiler and tooling
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### 10.3 Community Contributions
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We thank:
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- The Zig community for excellent tooling
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- The Yosys/nextpnr open source FPGA community
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- The Hugging Face community for TinyStories dataset
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- The open source community at large
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### 10.4 Contributors
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- **Dmitrii Vasilev** — Lead developer, all 40+ innovations
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---
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**φ² + 1/φ² = 3 | TRINITY**

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