- 
- 
- 
+
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---
diff --git a/docs/lab/papers/patent-strategy/full-analysis.md b/docs/lab/papers/patent-strategy/full-analysis.md
index 889841d89e..61c5f4069f 100644
--- a/docs/lab/papers/patent-strategy/full-analysis.md
+++ b/docs/lab/papers/patent-strategy/full-analysis.md
@@ -415,10 +415,10 @@ Each record is tagged with relevant Cooperative Patent Classification codes for
| Record | CPC Codes |
|--------|-----------|
| D001-D003 (18939352) | H03K19/20, G06F30/34, G06N3/04, G06F7/544 |
-| D004 (19020211) | G06F8/65, G06N20/00, G06F11/36 |
-| D005 (19020213) | G06F7/72, G06N3/04, G06F17/16 |
-| D006 (19020215) | G06N3/0455, G06F17/14, G06N3/084 |
-| D007 (19020217) | G06F7/544, G06F7/72, G06F17/16 |
+| D004 (19020270) | G06F8/65, G06N20/00, G06F11/36 |
+| D005 (19020275) | G06F7/72, G06N3/04, G06F17/16 |
+| D006 (19020280) | G06N3/0455, G06F17/14, G06N3/084 |
+| D007 (19020282) | G06F7/544, G06F7/72, G06F17/16 |
| D008 (TBD) | G06N3/086, G06N20/00, G06F18/24 |
### Description Files
diff --git a/docs/lab/papers/patent-strategy/zenodo-descriptions/D001-D003.html b/docs/lab/papers/patent-strategy/zenodo-descriptions/D001-D003.html
index 6b29b280de..e48d2b4de2 100644
--- a/docs/lab/papers/patent-strategy/zenodo-descriptions/D001-D003.html
+++ b/docs/lab/papers/patent-strategy/zenodo-descriptions/D001-D003.html
@@ -110,9 +110,9 @@ Related DOIs: -D004: Ouroboros | -D005: VSA | -D006: phi-RoPE | -D007: Sparse MatMul
+D004: Ouroboros | +D005: VSA | +D006: phi-RoPE | +D007: Sparse MatMulCPC Classifications: H03K19/20 (Logic circuits using ternary), G06F30/34 (Computer-aided design for FPGA), G06N3/04 (Neural network architecture), G06F7/544 (Arithmetic using non-standard number systems)
diff --git a/docs/lab/papers/patent-strategy/zenodo-descriptions/D004.html b/docs/lab/papers/patent-strategy/zenodo-descriptions/D004.html index 1c48fc4c96..7d7b69f0b7 100644 --- a/docs/lab/papers/patent-strategy/zenodo-descriptions/D004.html +++ b/docs/lab/papers/patent-strategy/zenodo-descriptions/D004.html @@ -92,8 +92,8 @@Related DOIs: D001-D003: Ternary Resonance + FPGA | -D005: VSA | -D006: phi-RoPE | -D007: Sparse MatMul
+D005: VSA | +D006: phi-RoPE | +D007: Sparse MatMulCPC Classifications: G06F8/65 (Updates, patches), G06N20/00 (Machine learning), G06F11/36 (Software testing and debugging)
diff --git a/docs/lab/papers/patent-strategy/zenodo-descriptions/D005.html b/docs/lab/papers/patent-strategy/zenodo-descriptions/D005.html index d9d6681388..434a6ecafa 100644 --- a/docs/lab/papers/patent-strategy/zenodo-descriptions/D005.html +++ b/docs/lab/papers/patent-strategy/zenodo-descriptions/D005.html @@ -82,8 +82,8 @@Related DOIs: D001-D003: Ternary Resonance + FPGA | -D004: Ouroboros | -D006: phi-RoPE | -D007: Sparse MatMul
+D004: Ouroboros | +D006: phi-RoPE | +D007: Sparse MatMulCPC Classifications: G06F7/72 (Methods for operating on vectors), G06N3/04 (Neural network architecture), G06F17/16 (Matrix or vector computation)
diff --git a/docs/lab/papers/patent-strategy/zenodo-descriptions/D006.html b/docs/lab/papers/patent-strategy/zenodo-descriptions/D006.html index 129a068103..f2f03eb6a8 100644 --- a/docs/lab/papers/patent-strategy/zenodo-descriptions/D006.html +++ b/docs/lab/papers/patent-strategy/zenodo-descriptions/D006.html @@ -83,8 +83,8 @@Related DOIs: D001-D003: Ternary Resonance + FPGA | -D004: Ouroboros | -D005: VSA | -D007: Sparse MatMul
+D004: Ouroboros | +D005: VSA | +D007: Sparse MatMulCPC Classifications: G06N3/0455 (Attention mechanisms in neural networks), G06F17/14 (Fourier and related transforms), G06N3/084 (Backpropagation / training methods)
diff --git a/docs/lab/papers/patent-strategy/zenodo-descriptions/D007.html b/docs/lab/papers/patent-strategy/zenodo-descriptions/D007.html index 1d9053701e..a23e08264b 100644 --- a/docs/lab/papers/patent-strategy/zenodo-descriptions/D007.html +++ b/docs/lab/papers/patent-strategy/zenodo-descriptions/D007.html @@ -93,8 +93,8 @@Related DOIs: D001-D003: Ternary Resonance + FPGA | -D004: Ouroboros | -D005: VSA | -D006: phi-RoPE
+D004: Ouroboros | +D005: VSA | +D006: phi-RoPECPC Classifications: G06F7/544 (Arithmetic using non-standard number systems), G06F7/72 (Methods for operating on vectors), G06F17/16 (Matrix or vector computation)
diff --git a/docs/lab/papers/patent-strategy/zenodo-descriptions/D008.html b/docs/lab/papers/patent-strategy/zenodo-descriptions/D008.html index 0d7ff79560..0f362e8f09 100644 --- a/docs/lab/papers/patent-strategy/zenodo-descriptions/D008.html +++ b/docs/lab/papers/patent-strategy/zenodo-descriptions/D008.html @@ -102,9 +102,9 @@Related DOIs: D001-D003: Ternary Resonance + FPGA | -D004: Ouroboros | -D005: VSA | -D006: phi-RoPE | -D007: Sparse MatMul
+D004: Ouroboros | +D005: VSA | +D006: phi-RoPE | +D007: Sparse MatMulCPC Classifications: G06N3/086 (Learning methods using evolutionary computation), G06N20/00 (Machine learning), G06F18/24 (Classification techniques — evolutionary algorithms)
diff --git a/docs/research/.zenodo.B001_v8.0.json b/docs/research/.zenodo.B001_v8.0.json index 4c22d1def4..2a5aa2d322 100644 --- a/docs/research/.zenodo.B001_v8.0.json +++ b/docs/research/.zenodo.B001_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "HSLM (Hierarchical Sacred Language Model), a 1.95M parameter ternary language model achieving perplexity 125.3 ± 2.1 on TinyStories. Uses balanced ternary weights {-1, 0, +1} with pure LUT-based arithmetic, eliminating DSP dependence. Achieves 19.7× compression (385 KB vs 7.6 MB FP32), 0% DSP utilization, and 51,200 tok/s throughput. v8.0 includes cross-bundle citation analysis, unified bibliography, LaTeX table generation, and peer review templates.\n\n## Methodology\n\nThe HSLM architecture follows sacred geometric principles derived from φ (golden ratio ≈ 1.618). The model implements ternary-aware attention mechanism where query-key-value interactions are computed using LUT-based ternary arithmetic, eliminating DSP dependencies required for floating-point operations.\n\n**Model Architecture:**\n- **Parameters:** 1.95M total (embedding: 384, 6 layers of [384, 512, 768, 1024] heads)\n- **Ternary Encoding:** {-1, 0, +1} with φ-normalized quantization\n- **Attention:** Sparse attention with τ = φ^(-1) ≈ 0.618 cache threshold\n- **Position Encoding:** phi-RoPE (rotary position encoding) with θ_i = φ^(-2i/HEAD_DIM)\n\n**Computational Complexity:** O(n²·d_model·L) for attention, O(L) for inference\n\n## Algorithm: Sparse Attention Computation\n\n```python\ndef compute_sparse_attention(query, key, cache, tau=0.618):\n # Cache lookup\n if cache.exists(key): return cache[key]\n # Sparse attention (τ-gating)\n attention_weights = (q @ K) / max(q @ K, axis=-1)\n attention_weights = attention_weights * (attention_weights > tau)\n return attention_weights @ value_vector\n```\n\n**Time Complexity:** O(L·d_model) per token\n**Space Complexity:** O(L²·d_model) parameters\n\n## Reproducibility\n\nAll experiments were conducted with fixed random seeds (42, 133, 267, 313, 647, 751, 941, 997) to ensure statistical significance. Results include 95%/99% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Training logs are archived at ./var/trinity/hslm/\n\n## Datasets\n\n**Training:** TinyStories (10M tokens, filtered for <5K tokens per document)\n- **Evaluation:** TinyStories validation set (12,672 examples, perplexity evaluation)\n- **Preprocessing:** Tokenization via B002 sacred formats, truncation to 512 tokens per sequence\n\n**Splits:** Train/Validation/Test (80/10/10) for developmental evaluation\n\n## Training Configuration\n\n```yaml\noptimizer: HSLM_SACRED\nlearning_rate:\n initial: 0.003\n schedule: cosine\n schedule_args:\n warmup_steps: 2000\n peak_lr: 0.006\n final_lr: 0.0001\n warmup:\n initial_lr: 6e-5\n duration: 2000 steps\nbatch_size: 64\n sequence_length: 512\n num_epochs: 3\n gradient_clipping: 1.0\n```\n\n## Ethical Considerations\n\nResearch conducted under open-source principles with no private data collection. Model weights are released under CC-BY-4.0 license. No personally identifiable information is stored in model checkpoints.\n\n## Broader Impact\n\nThis work advances neuromorphic computing by demonstrating that balanced ternary neural networks can achieve competitive language modeling performance while eliminating hardware dependencies. The pure-Zig implementation (zero external dependencies) enables deployment on embedded systems with limited resources. Applications include language models for resource-constrained edge devices and scientific computing environments requiring interpretable symbolic representations.\n\n## Limitations\n\n- Current implementation uses pure ternary without gradient-based fine-tuning of quantization levels\n- Sparse attention implementation optimized for LUT-heavy FPGAs, may require adaptation for other hardware platforms\n- Training data limited to publicly available corpora; no domain-specific evaluation conducted\n- Inference throughput may vary significantly with temperature and cache hit rate\n\n## Future Work\n\n- Implement gradient-based ternary fine-tuning for improved quantization\n- Evaluate on domain-specific benchmarks (code generation, scientific reasoning)\n- Extend to multi-modal architectures (text + symbolic representations)\n- Investigate adaptive τ based on input complexity\n- Compare against state-of-the-art ternary quantization methods (QAT, TernaryBERT)", + "description": "[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nHSLM (Hierarchical Sacred Language Model), a 1.95M parameter ternary language model achieving perplexity 125.3 ± 2.1 on TinyStories. Uses balanced ternary weights {-1, 0, +1} with pure LUT-based arithmetic, eliminating DSP dependence. Achieves 19.7× compression (385 KB vs 7.6 MB FP32), 0% DSP utilization, and 51,200 tok/s throughput. v8.0 includes cross-bundle citation analysis, unified bibliography, LaTeX table generation, and peer review templates.\n\n## Methodology\n\nThe HSLM architecture follows sacred geometric principles derived from φ (golden ratio ≈ 1.618). The model implements ternary-aware attention mechanism where query-key-value interactions are computed using LUT-based ternary arithmetic, eliminating DSP dependencies required for floating-point operations.\n\n**Model Architecture:**\n- **Parameters:** 1.95M total (embedding: 384, 6 layers of [384, 512, 768, 1024] heads)\n- **Ternary Encoding:** {-1, 0, +1} with φ-normalized quantization\n- **Attention:** Sparse attention with τ = φ^(-1) ≈ 0.618 cache threshold\n- **Position Encoding:** phi-RoPE (rotary position encoding) with θ_i = φ^(-2i/HEAD_DIM)\n\n**Computational Complexity:** O(n²·d_model·L) for attention, O(L) for inference\n\n## Algorithm: Sparse Attention Computation\n\n```python\ndef compute_sparse_attention(query, key, cache, tau=0.618):\n # Cache lookup\n if cache.exists(key): return cache[key]\n # Sparse attention (τ-gating)\n attention_weights = (q @ K) / max(q @ K, axis=-1)\n attention_weights = attention_weights * (attention_weights > tau)\n return attention_weights @ value_vector\n```\n\n**Time Complexity:** O(L·d_model) per token\n**Space Complexity:** O(L²·d_model) parameters\n\n## Reproducibility\n\nAll experiments were conducted with fixed random seeds (42, 133, 267, 313, 647, 751, 941, 997) to ensure statistical significance. Results include 95%/99% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Training logs are archived at ./var/trinity/hslm/\n\n## Datasets\n\n**Training:** TinyStories (10M tokens, filtered for <5K tokens per document)\n- **Evaluation:** TinyStories validation set (12,672 examples, perplexity evaluation)\n- **Preprocessing:** Tokenization via B002 sacred formats, truncation to 512 tokens per sequence\n\n**Splits:** Train/Validation/Test (80/10/10) for developmental evaluation\n\n## Training Configuration\n\n```yaml\noptimizer: HSLM_SACRED\nlearning_rate:\n initial: 0.003\n schedule: cosine\n schedule_args:\n warmup_steps: 2000\n peak_lr: 0.006\n final_lr: 0.0001\n warmup:\n initial_lr: 6e-5\n duration: 2000 steps\nbatch_size: 64\n sequence_length: 512\n num_epochs: 3\n gradient_clipping: 1.0\n```\n\n## Ethical Considerations\n\nResearch conducted under open-source principles with no private data collection. Model weights are released under CC-BY-4.0 license. No personally identifiable information is stored in model checkpoints.\n\n## Broader Impact\n\nThis work advances neuromorphic computing by demonstrating that balanced ternary neural networks can achieve competitive language modeling performance while eliminating hardware dependencies. The pure-Zig implementation (zero external dependencies) enables deployment on embedded systems with limited resources. Applications include language models for resource-constrained edge devices and scientific computing environments requiring interpretable symbolic representations.\n\n## Limitations\n\n- Current implementation uses pure ternary without gradient-based fine-tuning of quantization levels\n- Sparse attention implementation optimized for LUT-heavy FPGAs, may require adaptation for other hardware platforms\n- Training data limited to publicly available corpora; no domain-specific evaluation conducted\n- Inference throughput may vary significantly with temperature and cache hit rate\n\n## Future Work\n\n- Implement gradient-based ternary fine-tuning for improved quantization\n- Evaluate on domain-specific benchmarks (code generation, scientific reasoning)\n- Extend to multi-modal architectures (text + symbolic representations)\n- Investigate adaptive τ based on input complexity\n- Compare against state-of-the-art ternary quantization methods (QAT, TernaryBERT)", "keywords": [ "ternary neural networks", "HSLM", @@ -26,19 +26,13 @@ "related_identifiers": [ { "scheme": "doi", - "identifier": "10.5281/zenodo.19227735", + "identifier": "10.5281/zenodo.19227867", "relation": "references", "resource_type": "software" }, { "scheme": "doi", - "identifier": "10.5281/zenodo.19227843", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227745", + "identifier": "10.5281/zenodo.19227875", "relation": "references", "resource_type": "software" }, @@ -56,9 +50,8 @@ ], "references": [ "Eldan, R., & Li, Y. (2023). TinyStories: How Small Can Language Models Be and Still Speak Coherent English? arXiv preprint arXiv:2305.07759", - "Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. DOI: 10.5281/zenodo.19227735", - "Vasilev, D. (2026). Trinity B006: Sacred GF16/TF3 Encoding. Zenodo. DOI: 10.5281/zenodo.19227843", - "Vasilev, D. (2026). Trinity B007: VSA Operations. Zenodo. DOI: 10.5281/zenodo.19227745" + "Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. DOI: 10.5281/zenodo.19227867", + "Vasilev, D. (2026). Trinity B007: VSA Operations. Zenodo. DOI: 10.5281/zenodo.19227875" ], "license": "CC-BY-4.0", "access_right": "open", @@ -77,4 +70,4 @@ "title": "Trinity S³AI Research Framework" } ] -} +} \ No newline at end of file diff --git a/docs/research/.zenodo.B001_v9.0.json b/docs/research/.zenodo.B001_v9.0.json index 9a34b4b2be..ebab357688 100644 --- a/docs/research/.zenodo.B001_v9.0.json +++ b/docs/research/.zenodo.B001_v9.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "HSLM (Hierarchical Sacred Language Model), a 1.95M parameter ternary language model achieving perplexity 125.3 \u00b1 2.1 on TinyStories. Uses balanced ternary weights {-1, 0, +1} with pure LUT-based arithmetic, eliminating DSP dependence. Achieves 19.7\u00d7 compression (385 KB vs 7.6 MB FP32), 0% DSP utilization, and 51,200 tok/s throughput.\n\n## Methodology\n\nThe HSLM architecture follows sacred geometric principles derived from \u03c6 (golden ratio \u2248 1.618). The model implements ternary-aware attention mechanism where query-key-value interactions are computed using LUT-based ternary arithmetic, eliminating DSP dependencies required for floating-point operations.\n\n**Model Architecture:**\n- **Parameters:** 1.95M total (embedding: 384, 6 layers of [384, 512, 768, 1024] heads)\n- **Ternary Encoding:** {-1, 0, +1} with \u03c6-normalized quantization\n- **Attention:** Sparse attention with \u03c4 = \u03c6^(-1) \u2248 0.618 cache threshold\n- **Position Encoding:** phi-RoPE (rotary position encoding) with \u03b8_i = \u03c6^(-2i/HEAD_DIM)\n\n**Computational Complexity:** O(n\u00b2\u00b7d_model\u00b7L) for attention, O(L) for inference\n\n## Algorithm: Sparse Attention Computation\n\n```python\ndef compute_sparse_attention(query, key, cache, tau=0.618):\n # Cache lookup\n if cache.exists(key): return cache[key]\n # Sparse attention (\u03c4-gating)\n attention_weights = (q @ K) / max(q @ K, axis=-1)\n attention_weights = attention_weights * (attention_weights > tau)\n return attention_weights @ value_vector\n```\n\n**Time Complexity:** O(L\u00b7d_model) per token\n**Space Complexity:** O(L\u00b2\u00b7d_model) parameters\n\n## Experimental Results\n\n**Dataset:** TinyStories (10M tokens)\n\n**Training Configuration:**\n- Optimizer: HSLM_SACRED with cosine LR schedule\n- Learning rate: 0.003 \u2192 0.006 \u2192 0.0001 (warmup + cosine decay)\n- Batch size: 64, sequence length: 512, 3 epochs\n- Random seeds: [42, 133, 267, 313, 647, 751, 941, 997] (8 runs)\n\n**Results (Mean \u00b1 SD, 8 runs):**\n\n| Metric | HSLM v9.0 | TinyLlama-1B | GPT-2 | \u0394 vs SOTA |\n|--------|------------|-------------|------|-----------|\n| Perplexity (validation) | 125.3 \u00b1 2.1 | 117.2 \u00b1 3.4 | 106.1 \u00b1 2.8 | +6.9% vs TinyLlama |\n| Perplexity (test) | 128.7 \u00b1 2.5 | 119.8 \u00b1 3.6 | 108.2 \u00b1 3.1 | +7.4% vs TinyLlama |\n| Throughput (tok/s) | 51,200 | 48,500 | 52,100 | +5.3% vs GPT-2 |\n| Model Size | 385 KB | 5.2 MB | 7.6 MB | 19.7\u00d7 smaller than FP32 |\n| Parameter Efficiency | 15.3M tok/GB | 12.0M tok/GB | 21.2M tok/GB | |\n\n**Statistical Analysis (Bootstrap, 10K resamples):**\n- **HSLM vs TinyLlama:** t(14) = 8.73, p < 0.001 *** (highly significant)\n- **HSLM vs GPT-2:** t(14) = 5.24, p < 0.001 *** (highly significant)\n- **Confidence Intervals (95% CI):**\n - HSLM PPL: [122.8, 127.8]\n - Throughput: [50,450, 51,950]\n- **Effect Size (Cohen's d):**\n - vs TinyLlama: d = 0.82 (large effect)\n - vs GPT-2: d = 0.45 (medium effect)\n\n**Key Findings:**\n1. HSLM achieves 6.9% better perplexity than TinyLlama-1B while using 19.7\u00d7 less parameters\n2. Throughput competitive with GPT-2 (+5.3%) despite 26\u00d7 smaller model\n3. Statistical significance confirmed via t-tests (p < 0.001 ***)\n4. Zero-DSP FPGA deployment eliminates hardware cost for edge devices\n\n## Reproducibility\n\nAll experiments were conducted with fixed random seeds (42, 133, 267, 313, 647, 751, 941, 997) to ensure statistical significance. Results include 95% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Training logs are archived at ./var/trinity/hslm/\n\n**Seeds:** Each training run uses one of 8 predetermined random seeds. Reproducibility verified via identical PPL values (\u00b10.1 tolerance) across re-runs.\n\n## Datasets\n\n**Training:** TinyStories (10M tokens, filtered for <5K tokens per document)\n- **Evaluation:** TinyStories validation set (12,672 examples, perplexity evaluation)\n- **Preprocessing:** Tokenization via B002 sacred formats, truncation to 512 tokens per sequence\n- **Splits:** Train/Validation/Test (80/10/10) for developmental evaluation\n\n## Training Configuration\n\n```yaml\noptimizer: HSLM_SACRED\nlearning_rate:\n initial: 0.003\n schedule: cosine\n schedule_args:\n warmup_steps: 2000\n peak_lr: 0.006\n final_lr: 0.0001\n warmup:\n initial_lr: 6e-5\n duration: 2000 steps\nbatch_size: 64\n sequence_length: 512\n num_epochs: 3\n gradient_clipping: 1.0\n```\n\n## Ethical Considerations\n\nResearch conducted under open-source principles with no private data collection. Model weights are released under CC-BY-4.0 license. No personally identifiable information is stored in model checkpoints.\n\n## Broader Impact\n\nThis work advances neuromorphic computing by demonstrating that balanced ternary neural networks can achieve competitive language modeling performance while eliminating hardware dependencies. The pure-Zig implementation (zero external dependencies) enables deployment on embedded systems with limited resources. Applications include language models for resource-constrained edge devices and scientific computing environments requiring interpretable symbolic representations.\n\n## Limitations\n\n- Current implementation uses pure ternary without gradient-based fine-tuning of quantization levels\n- Sparse attention implementation optimized for LUT-heavy FPGAs, may require adaptation for other hardware platforms\n- Training data limited to publicly available corpora; no domain-specific evaluation conducted\n- Inference throughput may vary significantly with temperature and cache hit rate\n\n## Future Work\n\n- Implement gradient-based ternary fine-tuning for improved quantization\n- Evaluate on domain-specific benchmarks (code generation, scientific reasoning)\n- Extend to multi-modal architectures (text + symbolic representations)\n- Investigate adaptive \u03c4 based on input complexity\n- Compare against state-of-the-art ternary quantization methods (QAT, TernaryBERT)\n\n## References\n\n- Eldan, R., & Li, Y. (2023). TinyStories: How Small Can Language Models Be and Still Speak Coherent English? arXiv preprint arXiv:2305.07759\n- Touvron, H., Lavril, T., Izacard, G., & Lample, R. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971\n- Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. DOI: 10.5281/zenodo.19227867\n- Vasilev, D. (2026). Trinity B006: Sacred GF16/TF3 Encoding. Zenodo. DOI: 10.5281/zenodo.19227875\n- Vasilev, D. (2026). Trinity B007: VSA Operations. Zenodo. DOI: 10.5281/zenodo.19227877", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nHSLM (Hierarchical Sacred Language Model), a 1.95M parameter ternary language model achieving perplexity 125.3 ± 2.1 on TinyStories. Uses balanced ternary weights {-1, 0, +1} with pure LUT-based arithmetic, eliminating DSP dependence. Achieves 19.7× compression (385 KB vs 7.6 MB FP32), 0% DSP utilization, and 51,200 tok/s throughput.\n\n## Methodology\n\nThe HSLM architecture follows sacred geometric principles derived from φ (golden ratio ≈ 1.618). The model implements ternary-aware attention mechanism where query-key-value interactions are computed using LUT-based ternary arithmetic, eliminating DSP dependencies required for floating-point operations.\n\n**Model Architecture:**\n- **Parameters:** 1.95M total (embedding: 384, 6 layers of [384, 512, 768, 1024] heads)\n- **Ternary Encoding:** {-1, 0, +1} with φ-normalized quantization\n- **Attention:** Sparse attention with τ = φ^(-1) ≈ 0.618 cache threshold\n- **Position Encoding:** phi-RoPE (rotary position encoding) with θ_i = φ^(-2i/HEAD_DIM)\n\n**Computational Complexity:** O(n²·d_model·L) for attention, O(L) for inference\n\n## Algorithm: Sparse Attention Computation\n\n```python\ndef compute_sparse_attention(query, key, cache, tau=0.618):\n # Cache lookup\n if cache.exists(key): return cache[key]\n # Sparse attention (τ-gating)\n attention_weights = (q @ K) / max(q @ K, axis=-1)\n attention_weights = attention_weights * (attention_weights > tau)\n return attention_weights @ value_vector\n```\n\n**Time Complexity:** O(L·d_model) per token\n**Space Complexity:** O(L²·d_model) parameters\n\n## Experimental Results\n\n**Dataset:** TinyStories (10M tokens)\n\n**Training Configuration:**\n- Optimizer: HSLM_SACRED with cosine LR schedule\n- Learning rate: 0.003 → 0.006 → 0.0001 (warmup + cosine decay)\n- Batch size: 64, sequence length: 512, 3 epochs\n- Random seeds: [42, 133, 267, 313, 647, 751, 941, 997] (8 runs)\n\n**Results (Mean ± SD, 8 runs):**\n\n| Metric | HSLM v9.0 | TinyLlama-1B | GPT-2 | Δ vs SOTA |\n|--------|------------|-------------|------|-----------|\n| Perplexity (validation) | 125.3 ± 2.1 | 117.2 ± 3.4 | 106.1 ± 2.8 | +6.9% vs TinyLlama |\n| Perplexity (test) | 128.7 ± 2.5 | 119.8 ± 3.6 | 108.2 ± 3.1 | +7.4% vs TinyLlama |\n| Throughput (tok/s) | 51,200 | 48,500 | 52,100 | +5.3% vs GPT-2 |\n| Model Size | 385 KB | 5.2 MB | 7.6 MB | 19.7× smaller than FP32 |\n| Parameter Efficiency | 15.3M tok/GB | 12.0M tok/GB | 21.2M tok/GB | |\n\n**Statistical Analysis (Bootstrap, 10K resamples):**\n- **HSLM vs TinyLlama:** t(14) = 8.73, p < 0.001 *** (highly significant)\n- **HSLM vs GPT-2:** t(14) = 5.24, p < 0.001 *** (highly significant)\n- **Confidence Intervals (95% CI):**\n - HSLM PPL: [122.8, 127.8]\n - Throughput: [50,450, 51,950]\n- **Effect Size (Cohen's d):**\n - vs TinyLlama: d = 0.82 (large effect)\n - vs GPT-2: d = 0.45 (medium effect)\n\n**Key Findings:**\n1. HSLM achieves 6.9% better perplexity than TinyLlama-1B while using 19.7× less parameters\n2. Throughput competitive with GPT-2 (+5.3%) despite 26× smaller model\n3. Statistical significance confirmed via t-tests (p < 0.001 ***)\n4. Zero-DSP FPGA deployment eliminates hardware cost for edge devices\n\n## Reproducibility\n\nAll experiments were conducted with fixed random seeds (42, 133, 267, 313, 647, 751, 941, 997) to ensure statistical significance. Results include 95% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Training logs are archived at ./var/trinity/hslm/\n\n**Seeds:** Each training run uses one of 8 predetermined random seeds. Reproducibility verified via identical PPL values (±0.1 tolerance) across re-runs.\n\n## Datasets\n\n**Training:** TinyStories (10M tokens, filtered for <5K tokens per document)\n- **Evaluation:** TinyStories validation set (12,672 examples, perplexity evaluation)\n- **Preprocessing:** Tokenization via B002 sacred formats, truncation to 512 tokens per sequence\n- **Splits:** Train/Validation/Test (80/10/10) for developmental evaluation\n\n## Training Configuration\n\n```yaml\noptimizer: HSLM_SACRED\nlearning_rate:\n initial: 0.003\n schedule: cosine\n schedule_args:\n warmup_steps: 2000\n peak_lr: 0.006\n final_lr: 0.0001\n warmup:\n initial_lr: 6e-5\n duration: 2000 steps\nbatch_size: 64\n sequence_length: 512\n num_epochs: 3\n gradient_clipping: 1.0\n```\n\n## Ethical Considerations\n\nResearch conducted under open-source principles with no private data collection. Model weights are released under CC-BY-4.0 license. No personally identifiable information is stored in model checkpoints.\n\n## Broader Impact\n\nThis work advances neuromorphic computing by demonstrating that balanced ternary neural networks can achieve competitive language modeling performance while eliminating hardware dependencies. The pure-Zig implementation (zero external dependencies) enables deployment on embedded systems with limited resources. Applications include language models for resource-constrained edge devices and scientific computing environments requiring interpretable symbolic representations.\n\n## Limitations\n\n- Current implementation uses pure ternary without gradient-based fine-tuning of quantization levels\n- Sparse attention implementation optimized for LUT-heavy FPGAs, may require adaptation for other hardware platforms\n- Training data limited to publicly available corpora; no domain-specific evaluation conducted\n- Inference throughput may vary significantly with temperature and cache hit rate\n\n## Future Work\n\n- Implement gradient-based ternary fine-tuning for improved quantization\n- Evaluate on domain-specific benchmarks (code generation, scientific reasoning)\n- Extend to multi-modal architectures (text + symbolic representations)\n- Investigate adaptive τ based on input complexity\n- Compare against state-of-the-art ternary quantization methods (QAT, TernaryBERT)\n\n## References\n\n- Eldan, R., & Li, Y. (2023). TinyStories: How Small Can Language Models Be and Still Speak Coherent English? arXiv preprint arXiv:2305.07759\n- Touvron, H., Lavril, T., Izacard, G., & Lample, R. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971\n- Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. DOI: 10.5281/zenodo.19227867\n- Vasilev, D. (2026). Trinity B006: Sacred GF16/TF3 Encoding. Zenodo. DOI: 10.5281/zenodo.19227875\n- Vasilev, D. (2026). Trinity B007: VSA Operations. Zenodo. DOI: 10.5281/zenodo.19227877", "keywords": [ "ternary neural networks", "HSLM", @@ -81,7 +81,7 @@ "grants": [ { "id": "trinity-2024", - "title": "Trinity S\u00b3AI Research Framework" + "title": "Trinity S³AI Research Framework" } ], "subjects": [ @@ -136,7 +136,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/docs/research/.zenodo.B002_v8.0.json b/docs/research/.zenodo.B002_v8.0.json index 20be790dae..17f476398e 100644 --- a/docs/research/.zenodo.B002_v8.0.json +++ b/docs/research/.zenodo.B002_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "FPGA accelerator achieving zero DSP utilization while maintaining comparable performance to FP32 baselines. Uses \u03c6-based ternary encoding with LUT-only arithmetic. Achieves 19.7\u00d7 model size reduction, 10\u00d7 power reduction. v8.0 includes cross-bundle citations, unified bibliography, and LaTeX tables.\n\n## Methodology\n\nThe zero-DSP FPGA accelerator implements LUT-based ternary arithmetic following sacred geometry principles. All floating-point operations are replaced with pure ternary logic {-1, 0, +1} implemented in LUTs, eliminating DSP48E1 slices requirement for standard floating-point arithmetic.\n\n**FPGA Architecture:**\n- **Target:** Xilinx XC7A100T (48k LUTs, 240 DSP slices)\n- **Ternary Encoding:** {-1, 0, +1} with \u03c6-based quantization (3.1 trits per parameter)\n- **Multiply Operations:** 4 variants implemented (packed-u32, branchless bit manipulation, sparse CSR, SIMD f16/f32)\n- **Memory Architecture:** Dual-port BRAM (18KB) for weight storage, distributed URAM for inference cache\n\n**Design Methodology:** Bottom-up implementation with Vivado 2023.3, utilizing Xilinx IP cores (BRAM18K, DSP48E). Synthesis targeted 100MHz timing closure.\n\n**Computational Model:**\n```verilog\n// Ternary multiply (branchless variant)\nmodule ternary_multiply(\n input_a, input_b,\n output reg,\n clk, rst\n);\n // 9-stage branchless tree (O(log\u2082N) operations)\n // Uses only LUTs, no DSP slices\nendmodule\n```\n\n**Timing Analysis:**\n- **Critical Path:** 45ns (ternary multiply), 62ns (full inference)\n- **Clock Frequency:** 100MHz (10ns period)\n- **Throughput:** 500K inferences/second at 100MHz\n- **Latency:** 62 cycles (620ns) for single-token inference\n\n## Algorithm: LUT-Only Ternary Arithmetic\n\nThe implementation uses balanced ternary representation where each value {-1, 0, +1} is encoded using two bits. Operations are performed using LUT-based truth tables for efficient hardware mapping.\n\n**Encoding Scheme:**\n```\ntrit_value -> trits[1:0]:\n {-1} -> 11\n {0} -> 00\n {+1} -> 10\n```\n\n**LUT Utilization:** 4 LUTs per ternary operation (add, multiply with carry detection)\n\n**Truth Table Size:** 256 entries \u00d7 9-bit trit value = 2304 bits\n\n**Space-Time Tradeoff:** LUT-optimized operations replace DSP but increase latency (~2-3\u00d7 for multiply)\n\n## Reproducibility\n\nFPGA bitstreams are synthesized with deterministic results across multiple toolchains (Vivado 2023.3, YosysHQ 2023.12, openFPGALoader 0.5.4). Synthesis reports are archived in `fpga/synthesis_reports/`. Timing closure achieved on XC7A100T-100 speed grade. Code is available at https://github.com/gHashTag/trinity under MIT license.\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens) for model training\n- **Quantization Reference:** FP32 model weights from HSLM training (B001)\n- **Inference Benchmark:** TinyStories validation set (12,672 sequences)\n- **Preprocessing:** Truncated to 512 tokens, converted to \u03c6-normalized ternary via B002 sacred format converter\n\n**Splits:** Train/Validation/Test (80/10/10) for inference throughput measurement\n\n## Resource Utilization (Post-Synthesis)\n\n| Resource | Used | Available | Utilization |\n|----------|----------|----------|\n| LUTs | 14,256 | 48,000 | 29.7% |\n| BRAM | 144 KB | 576 KB | 25.0% |\n| URAM | 288 KB | 1,280 KB | 25.3% |\n| DSP48E1 | 0 | 240 | 0% |\n\n**Power Analysis:**\n- **Quiescent:** 2.1W at 100MHz (measured)\n- **Total Power:** 2.8W (estimated including clock tree)\n- **Power Reduction:** 10\u00d7 vs FP32 baseline (2.8W \u2192 0.28W)\n\n## Performance Metrics\n\n- **Model Size:** 19.7\u00d7 reduction vs FP32 (385 KB \u2192 78 KB)\n- **Throughput:** 500K inferences/second\n- **Energy Efficiency:** 0.0056 \u03bcJ/inference (2.8W @ 500K/sec)\n- **Area-Delay Product:** 14,256 LUT\u00b7ns = 1,425,600 (lower is better)\n\n## Ethical Considerations\n\nOpen-source FPGA design with no proprietary IP blocks. All timing analysis and power measurements conducted on open hardware. Quantization methodology (\u03c6-normalization) published for reproducibility.\n\n## Limitations\n\n- Fixed 100MHz clock frequency (higher frequencies require timing closure modification)\n- LUT-heavy implementation increases latency for multiply operations (2-3\u00d7 vs DSP)\n- Inference throughput limited by BRAM bandwidth for batch operations\n- No dynamic frequency scaling (power gating implemented but not tested)\n\n## Future Work\n\n- Implement adaptive clock frequency scaling based on workload intensity\n- Optimize BRAM access patterns for better bandwidth utilization\n- Evaluate pipelined inference (10+ tokens) for improved throughput\n- Implement dynamic power gating (sleep idle blocks)\n- Port to larger FPGAs (Kintex UltraScale+) for additional compute resources", + "description": "[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nFPGA accelerator achieving zero DSP utilization while maintaining comparable performance to FP32 baselines. Uses φ-based ternary encoding with LUT-only arithmetic. Achieves 19.7× model size reduction, 10× power reduction. v8.0 includes cross-bundle citations, unified bibliography, and LaTeX tables.\n\n## Methodology\n\nThe zero-DSP FPGA accelerator implements LUT-based ternary arithmetic following sacred geometry principles. All floating-point operations are replaced with pure ternary logic {-1, 0, +1} implemented in LUTs, eliminating DSP48E1 slices requirement for standard floating-point arithmetic.\n\n**FPGA Architecture:**\n- **Target:** Xilinx XC7A100T (48k LUTs, 240 DSP slices)\n- **Ternary Encoding:** {-1, 0, +1} with φ-based quantization (3.1 trits per parameter)\n- **Multiply Operations:** 4 variants implemented (packed-u32, branchless bit manipulation, sparse CSR, SIMD f16/f32)\n- **Memory Architecture:** Dual-port BRAM (18KB) for weight storage, distributed URAM for inference cache\n\n**Design Methodology:** Bottom-up implementation with Vivado 2023.3, utilizing Xilinx IP cores (BRAM18K, DSP48E). Synthesis targeted 100MHz timing closure.\n\n**Computational Model:**\n```verilog\n// Ternary multiply (branchless variant)\nmodule ternary_multiply(\n input_a, input_b,\n output reg,\n clk, rst\n);\n // 9-stage branchless tree (O(log₂N) operations)\n // Uses only LUTs, no DSP slices\nendmodule\n```\n\n**Timing Analysis:**\n- **Critical Path:** 45ns (ternary multiply), 62ns (full inference)\n- **Clock Frequency:** 100MHz (10ns period)\n- **Throughput:** 500K inferences/second at 100MHz\n- **Latency:** 62 cycles (620ns) for single-token inference\n\n## Algorithm: LUT-Only Ternary Arithmetic\n\nThe implementation uses balanced ternary representation where each value {-1, 0, +1} is encoded using two bits. Operations are performed using LUT-based truth tables for efficient hardware mapping.\n\n**Encoding Scheme:**\n```\ntrit_value -> trits[1:0]:\n {-1} -> 11\n {0} -> 00\n {+1} -> 10\n```\n\n**LUT Utilization:** 4 LUTs per ternary operation (add, multiply with carry detection)\n\n**Truth Table Size:** 256 entries × 9-bit trit value = 2304 bits\n\n**Space-Time Tradeoff:** LUT-optimized operations replace DSP but increase latency (~2-3× for multiply)\n\n## Reproducibility\n\nFPGA bitstreams are synthesized with deterministic results across multiple toolchains (Vivado 2023.3, YosysHQ 2023.12, openFPGALoader 0.5.4). Synthesis reports are archived in `fpga/synthesis_reports/`. Timing closure achieved on XC7A100T-100 speed grade. Code is available at https://github.com/gHashTag/trinity under MIT license.\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens) for model training\n- **Quantization Reference:** FP32 model weights from HSLM training (B001)\n- **Inference Benchmark:** TinyStories validation set (12,672 sequences)\n- **Preprocessing:** Truncated to 512 tokens, converted to φ-normalized ternary via B002 sacred format converter\n\n**Splits:** Train/Validation/Test (80/10/10) for inference throughput measurement\n\n## Resource Utilization (Post-Synthesis)\n\n| Resource | Used | Available | Utilization |\n|----------|----------|----------|\n| LUTs | 14,256 | 48,000 | 29.7% |\n| BRAM | 144 KB | 576 KB | 25.0% |\n| URAM | 288 KB | 1,280 KB | 25.3% |\n| DSP48E1 | 0 | 240 | 0% |\n\n**Power Analysis:**\n- **Quiescent:** 2.1W at 100MHz (measured)\n- **Total Power:** 2.8W (estimated including clock tree)\n- **Power Reduction:** 10× vs FP32 baseline (2.8W → 0.28W)\n\n## Performance Metrics\n\n- **Model Size:** 19.7× reduction vs FP32 (385 KB → 78 KB)\n- **Throughput:** 500K inferences/second\n- **Energy Efficiency:** 0.0056 μJ/inference (2.8W @ 500K/sec)\n- **Area-Delay Product:** 14,256 LUT·ns = 1,425,600 (lower is better)\n\n## Ethical Considerations\n\nOpen-source FPGA design with no proprietary IP blocks. All timing analysis and power measurements conducted on open hardware. Quantization methodology (φ-normalization) published for reproducibility.\n\n## Limitations\n\n- Fixed 100MHz clock frequency (higher frequencies require timing closure modification)\n- LUT-heavy implementation increases latency for multiply operations (2-3× vs DSP)\n- Inference throughput limited by BRAM bandwidth for batch operations\n- No dynamic frequency scaling (power gating implemented but not tested)\n\n## Future Work\n\n- Implement adaptive clock frequency scaling based on workload intensity\n- Optimize BRAM access patterns for better bandwidth utilization\n- Evaluate pipelined inference (10+ tokens) for improved throughput\n- Implement dynamic power gating (sleep idle blocks)\n- Port to larger FPGAs (Kintex UltraScale+) for additional compute resources", "keywords": [ "FPGA", "zero-DSP", @@ -27,18 +27,6 @@ "relation": "references", "resource_type": "software" }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227735", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", - "relation": "references", - "resource_type": "software" - }, { "scheme": "doi", "identifier": "10.5281/zenodo.19227879", diff --git a/docs/research/.zenodo.B002_v9.0.json b/docs/research/.zenodo.B002_v9.0.json index f08e998c20..e2d76c8e24 100644 --- a/docs/research/.zenodo.B002_v9.0.json +++ b/docs/research/.zenodo.B002_v9.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "FPGA accelerator achieving zero DSP utilization while maintaining comparable performance to FP32 baselines. Uses \u03c6-based ternary encoding with LUT-only arithmetic. Achieves 19.7\u00d7 model size reduction, 10\u00d7 power reduction. v9.0 includes enhanced resource utilization analysis, power measurements, and comparison with DSP-based implementations.\n\n## Methodology\n\nThe zero-DSP FPGA accelerator implements LUT-based ternary arithmetic following sacred geometry principles. All floating-point operations are replaced with pure ternary logic {-1, 0, +1} implemented in LUTs, eliminating DSP48E1 slices requirement for standard floating-point arithmetic.\n\n**FPGA Architecture:**\n- **Target:** Xilinx XC7A100T (48k LUTs, 240 DSP slices)\n- **Ternary Encoding:** {-1, 0, +1} with \u03c6-based quantization (3.1 trits per parameter)\n- **Multiply Operations:** 4 variants implemented (packed-u32, branchless bit manipulation, sparse CSR, SIMD f16/f32)\n- **Memory Architecture:** Dual-port BRAM (18KB) for weight storage, distributed URAM for inference cache\n\n**Design Methodology:** Bottom-up implementation with Vivado 2023.3, utilizing Xilinx IP cores (BRAM18K, DSP48E). Synthesis targeted 100MHz timing closure.\n\n**Computational Model:**\n```verilog\n// Ternary multiply (branchless variant)\nmodule ternary_multiply(\n input_a, input_b,\n output reg,\n clk, rst\n);\n // 9-stage branchless tree (O(log\u2082N) operations)\n // Uses only LUTs, no DSP slices\nendmodule\n```\n\n**Timing Analysis:**\n- **Critical Path:** 45ns (ternary multiply), 62ns (full inference)\n- **Clock Frequency:** 100MHz (10ns period)\n- **Throughput:** 500K inferences/second at 100MHz\n- **Latency:** 62 cycles (620ns) for single-token inference\n\n## Algorithm: LUT-Only Ternary Arithmetic\n\nThe implementation uses balanced ternary representation where each value {-1, 0, +1} is encoded using two bits. Operations are performed using LUT-based truth tables for efficient hardware mapping.\n\n**Encoding Scheme:**\n```\ntrit_value -> trits[1:0]:\n {-1} -> 11\n {0} -> 00\n {+1} -> 10\n```\n\n**LUT Utilization:** 4 LUTs per ternary operation (add, multiply with carry detection)\n\n**Truth Table Size:** 256 entries \u00d7 9-bit trit value = 2304 bits\n\n**Space-Time Tradeoff:** LUT-optimized operations replace DSP but increase latency (~2-3\u00d7 for multiply)\n\n## Experimental Results\n\n**Resource Utilization (Post-Synthesis, XC7A100T-100):**\n\n| Resource | LUT-Only (B002) | DSP-Based (Baseline) | \u0394 Improvement |\n|----------|------------------|----------------------|---------------|\n| LUTs | 14,256 | 8,432 | +69.0% (expected for LUT-only) |\n| BRAM | 144 KB | 144 KB | 0% (same memory) |\n| URAM | 288 KB | 288 KB | 0% (same memory) |\n| DSP48E1 | 0 | 48 | **100% reduction** |\n| Power | 2.8W | 28W | **10\u00d7 reduction** |\n| Energy/Inference | 0.0056 \u03bcJ | 0.056 \u03bcJ | **10\u00d7 reduction** |\n\n**Power Analysis (Vivado Power Estimator):**\n- **Quiescent:** 2.1W at 100MHz (measured)\n- **Dynamic:** 0.7W (switching activity at 50% toggle rate)\n- **Total Power:** 2.8W (estimated including clock tree)\n- **Power Reduction:** 10\u00d7 vs FP32 baseline (28W \u2192 2.8W)\n\n**Performance Comparison:**\n\n| Metric | LUT-Only (B002) | DSP-Based (Baseline) | Ratio |\n|--------|------------------|----------------------|-------|\n| Model Size | 78 KB | 1,536 KB (FP32) | 19.7\u00d7 smaller |\n| Throughput | 500K inf/s | 480K inf/s | 1.04\u00d7 faster |\n| Latency | 620ns | 580ns | 1.07\u00d7 slower |\n| Energy Efficiency | 0.0056 \u03bcJ/inf | 0.058 \u03bcJ/inf | 10.4\u00d7 better |\n| Area-Delay Product | 1,425,600 LUT\u00b7ns | 6,694,080 DSP\u00b7ns | 4.7\u00d7 better |\n\n**Synthesis Results (Vivado 2023.3):**\n- **Timing Closure:** Met (WNS = +2.1ns, TNS = 0)\n- **Place & Route:** 100% completion, no critical violations\n- **Power Optimization:** High-effort (XPO power reduction)\n- **Strategy:** Performance_ExplorePostRoutePhysOpt\n\n**Statistical Analysis (5 synthesis runs):**\n- **LUT Utilization:** 14,256 \u00b1 127 (mean \u00b1 SD)\n- **Power:** 2.8W \u00b1 0.12W (95% CI: [2.52W, 3.08W])\n- **Timing:** WNS = +2.1 \u00b1 0.3ns (worst-case slack)\n- **Yield:** 100% (all 5 runs achieved timing closure)\n\n## Reproducibility\n\nFPGA bitstreams are synthesized with deterministic results across multiple toolchains (Vivado 2023.3, YosysHQ 2023.12, openFPGALoader 0.5.4). Synthesis reports are archived in `fpga/synthesis_reports/`. Timing closure achieved on XC7A100T-100 speed grade. Code is available at https://github.com/gHashTag/trinity under MIT license.\n\n**Toolchain Versions:**\n- Vivado: 2023.3 (Build 3663520)\n- YosysHQ: 2023.12 (commit 8a2b3c)\n- openFPGALoader: 0.5.4 (FTDI driver v1.5)\n\n**Synthesis Reports:**\n- `fpga/synthesis_reports/xc7a100t_ternary_alu_util.txt`\n- `fpga/synthesis_reports/xc7a100t_ternary_alu_timing.txt`\n- `fpga/synthesis_reports/xc7a100t_ternary_alu_power.txt`\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens) for model training\n- **Quantization Reference:** FP32 model weights from HSLM training (B001)\n- **Inference Benchmark:** TinyStories validation set (12,672 sequences)\n- **Preprocessing:** Truncated to 512 tokens, converted to \u03c6-normalized ternary via B002 sacred format converter\n\n**Splits:** Train/Validation/Test (80/10/10) for inference throughput measurement\n\n## Resource Utilization (Post-Synthesis)\n\n| Resource | Used | Available | Utilization |\n|----------|----------|----------|\n| LUTs | 14,256 | 48,000 | 29.7% |\n| BRAM | 144 KB | 576 KB | 25.0% |\n| URAM | 288 KB | 1,280 KB | 25.3% |\n| DSP48E1 | 0 | 240 | 0% |\n| FF (Flip-Flops) | 8,432 | 96,000 | 8.8% |\n| IOB | 42 | 285 | 14.7% |\n| BUFG | 2 | 32 | 6.25% |\n\n**Power Analysis:**\n- **Quiescent:** 2.1W at 100MHz (measured)\n- **Dynamic:** 0.7W (switching activity)\n- **Total Power:** 2.8W (estimated including clock tree)\n- **Power Reduction:** 10\u00d7 vs FP32 baseline (28W \u2192 2.8W)\n\n**Power Breakdown:**\n- **Clock Tree:** 0.42W (15%)\n- **LUTs:** 1.68W (60%)\n- **BRAM:** 0.56W (20%)\n- **IOB:** 0.14W (5%)\n\n## Performance Metrics\n\n- **Model Size:** 19.7\u00d7 reduction vs FP32 (385 KB \u2192 78 KB)\n- **Throughput:** 500K inferences/second\n- **Energy Efficiency:** 0.0056 \u03bcJ/inference (2.8W @ 500K/sec)\n- **Area-Delay Product:** 14,256 LUT\u00b7ns = 1,425,600 (lower is better)\n\n## Ethical Considerations\n\nOpen-source FPGA design with no proprietary IP blocks. All timing analysis and power measurements conducted on open hardware. Quantization methodology (\u03c6-normalization) published for reproducibility.\n\n## Limitations\n\n- Fixed 100MHz clock frequency (higher frequencies require timing closure modification)\n- LUT-heavy implementation increases latency for multiply operations (2-3\u00d7 vs DSP)\n- Inference throughput limited by BRAM bandwidth for batch operations\n- No dynamic frequency scaling (power gating implemented but not tested)\n- DSP slices unavailable for other computations (trade-off for zero-DSP goal)\n\n## Future Work\n\n- Implement adaptive clock frequency scaling based on workload intensity\n- Optimize BRAM access patterns for better bandwidth utilization\n- Evaluate pipelined inference (10+ tokens) for improved throughput\n- Implement dynamic power gating (sleep idle blocks)\n- Port to larger FPGAs (Kintex UltraScale+) for additional compute resources\n- Hybrid approach: DSP for critical path, LUT for bulk operations\n- Evaluate ternary precision vs FP16/FP32 on accuracy-sensitive tasks\n\n## References\n\n- Vasilev, D. (2026). Trinity B001: HSLM-1.95M Ternary Neural Networks. Zenodo. https://doi.org/10.5281/zenodo.19227865\n- Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. https://doi.org/10.5281/zenodo.19227867\n- Xilinx. (2024). Vivado Design Suite User Guide: Synthesis. UG901 (v2024.1)\n- Xilinx. (2018). DSP48E1: 48-Bit DSP Slice User Guide. UG479 (v1.12)\n- Xilinx. (2023). 7 Series FPGAs Configurable Logic Block. UG474 (v1.19)\n- Xilinx. (2023). 7 Series FPGAs Memory Resources. UG473 (v1.15)", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nFPGA accelerator achieving zero DSP utilization while maintaining comparable performance to FP32 baselines. Uses φ-based ternary encoding with LUT-only arithmetic. Achieves 19.7× model size reduction, 10× power reduction. v9.0 includes enhanced resource utilization analysis, power measurements, and comparison with DSP-based implementations.\n\n## Methodology\n\nThe zero-DSP FPGA accelerator implements LUT-based ternary arithmetic following sacred geometry principles. All floating-point operations are replaced with pure ternary logic {-1, 0, +1} implemented in LUTs, eliminating DSP48E1 slices requirement for standard floating-point arithmetic.\n\n**FPGA Architecture:**\n- **Target:** Xilinx XC7A100T (48k LUTs, 240 DSP slices)\n- **Ternary Encoding:** {-1, 0, +1} with φ-based quantization (3.1 trits per parameter)\n- **Multiply Operations:** 4 variants implemented (packed-u32, branchless bit manipulation, sparse CSR, SIMD f16/f32)\n- **Memory Architecture:** Dual-port BRAM (18KB) for weight storage, distributed URAM for inference cache\n\n**Design Methodology:** Bottom-up implementation with Vivado 2023.3, utilizing Xilinx IP cores (BRAM18K, DSP48E). Synthesis targeted 100MHz timing closure.\n\n**Computational Model:**\n```verilog\n// Ternary multiply (branchless variant)\nmodule ternary_multiply(\n input_a, input_b,\n output reg,\n clk, rst\n);\n // 9-stage branchless tree (O(log₂N) operations)\n // Uses only LUTs, no DSP slices\nendmodule\n```\n\n**Timing Analysis:**\n- **Critical Path:** 45ns (ternary multiply), 62ns (full inference)\n- **Clock Frequency:** 100MHz (10ns period)\n- **Throughput:** 500K inferences/second at 100MHz\n- **Latency:** 62 cycles (620ns) for single-token inference\n\n## Algorithm: LUT-Only Ternary Arithmetic\n\nThe implementation uses balanced ternary representation where each value {-1, 0, +1} is encoded using two bits. Operations are performed using LUT-based truth tables for efficient hardware mapping.\n\n**Encoding Scheme:**\n```\ntrit_value -> trits[1:0]:\n {-1} -> 11\n {0} -> 00\n {+1} -> 10\n```\n\n**LUT Utilization:** 4 LUTs per ternary operation (add, multiply with carry detection)\n\n**Truth Table Size:** 256 entries × 9-bit trit value = 2304 bits\n\n**Space-Time Tradeoff:** LUT-optimized operations replace DSP but increase latency (~2-3× for multiply)\n\n## Experimental Results\n\n**Resource Utilization (Post-Synthesis, XC7A100T-100):**\n\n| Resource | LUT-Only (B002) | DSP-Based (Baseline) | Δ Improvement |\n|----------|------------------|----------------------|---------------|\n| LUTs | 14,256 | 8,432 | +69.0% (expected for LUT-only) |\n| BRAM | 144 KB | 144 KB | 0% (same memory) |\n| URAM | 288 KB | 288 KB | 0% (same memory) |\n| DSP48E1 | 0 | 48 | **100% reduction** |\n| Power | 2.8W | 28W | **10× reduction** |\n| Energy/Inference | 0.0056 μJ | 0.056 μJ | **10× reduction** |\n\n**Power Analysis (Vivado Power Estimator):**\n- **Quiescent:** 2.1W at 100MHz (measured)\n- **Dynamic:** 0.7W (switching activity at 50% toggle rate)\n- **Total Power:** 2.8W (estimated including clock tree)\n- **Power Reduction:** 10× vs FP32 baseline (28W → 2.8W)\n\n**Performance Comparison:**\n\n| Metric | LUT-Only (B002) | DSP-Based (Baseline) | Ratio |\n|--------|------------------|----------------------|-------|\n| Model Size | 78 KB | 1,536 KB (FP32) | 19.7× smaller |\n| Throughput | 500K inf/s | 480K inf/s | 1.04× faster |\n| Latency | 620ns | 580ns | 1.07× slower |\n| Energy Efficiency | 0.0056 μJ/inf | 0.058 μJ/inf | 10.4× better |\n| Area-Delay Product | 1,425,600 LUT·ns | 6,694,080 DSP·ns | 4.7× better |\n\n**Synthesis Results (Vivado 2023.3):**\n- **Timing Closure:** Met (WNS = +2.1ns, TNS = 0)\n- **Place & Route:** 100% completion, no critical violations\n- **Power Optimization:** High-effort (XPO power reduction)\n- **Strategy:** Performance_ExplorePostRoutePhysOpt\n\n**Statistical Analysis (5 synthesis runs):**\n- **LUT Utilization:** 14,256 ± 127 (mean ± SD)\n- **Power:** 2.8W ± 0.12W (95% CI: [2.52W, 3.08W])\n- **Timing:** WNS = +2.1 ± 0.3ns (worst-case slack)\n- **Yield:** 100% (all 5 runs achieved timing closure)\n\n## Reproducibility\n\nFPGA bitstreams are synthesized with deterministic results across multiple toolchains (Vivado 2023.3, YosysHQ 2023.12, openFPGALoader 0.5.4). Synthesis reports are archived in `fpga/synthesis_reports/`. Timing closure achieved on XC7A100T-100 speed grade. Code is available at https://github.com/gHashTag/trinity under MIT license.\n\n**Toolchain Versions:**\n- Vivado: 2023.3 (Build 3663520)\n- YosysHQ: 2023.12 (commit 8a2b3c)\n- openFPGALoader: 0.5.4 (FTDI driver v1.5)\n\n**Synthesis Reports:**\n- `fpga/synthesis_reports/xc7a100t_ternary_alu_util.txt`\n- `fpga/synthesis_reports/xc7a100t_ternary_alu_timing.txt`\n- `fpga/synthesis_reports/xc7a100t_ternary_alu_power.txt`\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens) for model training\n- **Quantization Reference:** FP32 model weights from HSLM training (B001)\n- **Inference Benchmark:** TinyStories validation set (12,672 sequences)\n- **Preprocessing:** Truncated to 512 tokens, converted to φ-normalized ternary via B002 sacred format converter\n\n**Splits:** Train/Validation/Test (80/10/10) for inference throughput measurement\n\n## Resource Utilization (Post-Synthesis)\n\n| Resource | Used | Available | Utilization |\n|----------|----------|----------|\n| LUTs | 14,256 | 48,000 | 29.7% |\n| BRAM | 144 KB | 576 KB | 25.0% |\n| URAM | 288 KB | 1,280 KB | 25.3% |\n| DSP48E1 | 0 | 240 | 0% |\n| FF (Flip-Flops) | 8,432 | 96,000 | 8.8% |\n| IOB | 42 | 285 | 14.7% |\n| BUFG | 2 | 32 | 6.25% |\n\n**Power Analysis:**\n- **Quiescent:** 2.1W at 100MHz (measured)\n- **Dynamic:** 0.7W (switching activity)\n- **Total Power:** 2.8W (estimated including clock tree)\n- **Power Reduction:** 10× vs FP32 baseline (28W → 2.8W)\n\n**Power Breakdown:**\n- **Clock Tree:** 0.42W (15%)\n- **LUTs:** 1.68W (60%)\n- **BRAM:** 0.56W (20%)\n- **IOB:** 0.14W (5%)\n\n## Performance Metrics\n\n- **Model Size:** 19.7× reduction vs FP32 (385 KB → 78 KB)\n- **Throughput:** 500K inferences/second\n- **Energy Efficiency:** 0.0056 μJ/inference (2.8W @ 500K/sec)\n- **Area-Delay Product:** 14,256 LUT·ns = 1,425,600 (lower is better)\n\n## Ethical Considerations\n\nOpen-source FPGA design with no proprietary IP blocks. All timing analysis and power measurements conducted on open hardware. Quantization methodology (φ-normalization) published for reproducibility.\n\n## Limitations\n\n- Fixed 100MHz clock frequency (higher frequencies require timing closure modification)\n- LUT-heavy implementation increases latency for multiply operations (2-3× vs DSP)\n- Inference throughput limited by BRAM bandwidth for batch operations\n- No dynamic frequency scaling (power gating implemented but not tested)\n- DSP slices unavailable for other computations (trade-off for zero-DSP goal)\n\n## Future Work\n\n- Implement adaptive clock frequency scaling based on workload intensity\n- Optimize BRAM access patterns for better bandwidth utilization\n- Evaluate pipelined inference (10+ tokens) for improved throughput\n- Implement dynamic power gating (sleep idle blocks)\n- Port to larger FPGAs (Kintex UltraScale+) for additional compute resources\n- Hybrid approach: DSP for critical path, LUT for bulk operations\n- Evaluate ternary precision vs FP16/FP32 on accuracy-sensitive tasks\n\n## References\n\n- Vasilev, D. (2026). Trinity B001: HSLM-1.95M Ternary Neural Networks. Zenodo. https://doi.org/10.5281/zenodo.19227865\n- Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. https://doi.org/10.5281/zenodo.19227867\n- Xilinx. (2024). Vivado Design Suite User Guide: Synthesis. UG901 (v2024.1)\n- Xilinx. (2018). DSP48E1: 48-Bit DSP Slice User Guide. UG479 (v1.12)\n- Xilinx. (2023). 7 Series FPGAs Configurable Logic Block. UG474 (v1.19)\n- Xilinx. (2023). 7 Series FPGAs Memory Resources. UG473 (v1.15)", "keywords": [ "FPGA", "zero-DSP", @@ -29,12 +29,6 @@ "relation": "references", "resource_type": "software" }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", - "relation": "references", - "resource_type": "software" - }, { "scheme": "doi", "identifier": "10.5281/zenodo.19227879", @@ -105,7 +99,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/docs/research/.zenodo.B003_v8.0.json b/docs/research/.zenodo.B003_v8.0.json index 99bfed059f..9d760579a4 100644 --- a/docs/research/.zenodo.B003_v8.0.json +++ b/docs/research/.zenodo.B003_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "TRI-27 is a 27-register ternary processor implementing the Coptic alphabet as three banks of 9 registers, enabling efficient storage and manipulation of balanced ternary values {-1, 0, +1}. The ISA supports 8 fundamental operations (MOV, JGT, JLT, JUMP, LOAD, STORE, CALL, RET) with proven correctness through formal verification and random testing. Register addressing uses Coptic letter prefixes (Ⲁ, Ⲃ, ⲃ) for bank selection (3 banks × 9 registers = 27), providing 19.7× compression vs 32-register baseline while maintaining full instruction encoding capacity. v8.0 includes cross-bundle integration with B004 (Queen Lotus) and B005 (Tri Language).\n\n## Methodology\n\nTRI-27 implements a balanced ternary architecture where each register stores values from the set {-1, 0, +1}. The instruction encoding uses Coptic alphabet symbols for bank selection (Ⲁ=bank0, Ⲃ=bank1, ⲃ=bank2) and numeric indices 1-9 for register selection within each bank.\n\n**Instruction Encoding:**\n- **MOV:** `MOV Ⲁ1, Ⲃ2` (move from bank0 reg1 to bank1 reg2)\n- **JGT:** `JGT Ⲁ1, label` (jump if Ⲁ1 > 0)\n- **JLT:** `JLT Ⲃ5, label` (jump if Ⲃ5 < 0)\n- **LOAD/STORE:** `LOAD ⲃ9, [addr]`, `STORE Ⲁ1, [addr]`\n\n**Formal Verification:**\n- **Property:** Register values always in {-1, 0, +1}\n- **Method:** Bounded model checking with Z3 + random testing (100K test cases)\n- **Coverage:** 100% instruction coverage, 99.8% operand value space\n\n**State Space:** 3^27 possible register states (7.6×10^12 configurations)\n\n## Algorithm: Ternary Comparison\n\n```zig\nfn compareTrit(a: Trit, b: Trit) Comparison {\n return switch (a) {\n .neg => if (b == .neg) .eq else .lt,\n .zero => if (b == .pos) .lt else if (b == .neg) .gt else .eq,\n .pos => if (b == .pos) .eq else .gt,\n };\n}\n```\n\n**Time Complexity:** O(1) per comparison\n**Space Complexity:** O(1) (no allocation)\n\n## Reproducibility\n\nFormal verification conducted with Z3 4.12.6 (SMT solver) and property-based testing with zigtest. Test suite includes 100K randomly generated instruction sequences with bounded model checking. Code is available at https://github.com/gHashTag/trinity under MIT license. Verification logs archived in `var/trinity/verification/`.\n\n## Datasets\n\n**Test Cases:** 100K randomly generated instruction sequences\n- **Property Tests:** 15 formal properties (register invariants, control flow correctness)\n- **Benchmark Programs:** Fibonacci (recursive/iterative), quicksort, matrix multiplication\n\n**Coverage:** 100% instruction encoding, 99.8% operand value space\n\n## Performance Metrics\n\n- **Instruction Latency:** 1 cycle (MOV), 2 cycles (JGT/JLT), 3 cycles (LOAD/STORE)\n- **Throughput:** 33 MIPS at 100MHz (million instructions/second)\n- **Code Density:** 0.89 bytes/instruction (vs 1.25 bytes for x86-64)\n- **Verification Speed:** 3.2× faster than baseline (property-based testing vs manual proof)\n\n## Ethical Considerations\n\nOpen ISA specification with no patent encumbrances. Coptic alphabet used with respect for cultural heritage. No private data collected in verification benchmarks.\n\n## Limitations\n\n- Fixed-width ternary encoding (no support for multi-trit values)\n- No hardware floating-point operations (requires software emulation)\n- Limited to 27 registers (no spillover to memory)\n- Control flow only supports unconditional jumps (conditional jumps implemented via JGT/JLT + JUMP)\n\n## Future Work\n\n- Implement multi-trit arithmetic (add, subtract, multiply)\n- Add hardware support for function call frames (stack pointer)\n- Design pipelined execution (5-stage: IF-ID-EX-MEM-WB)\n- Investigate superscalar execution (2-way issue)\n- Implement interrupt handling and I/O ports", + "description": "[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nTRI-27 is a 27-register ternary processor implementing the Coptic alphabet as three banks of 9 registers, enabling efficient storage and manipulation of balanced ternary values {-1, 0, +1}. The ISA supports 8 fundamental operations (MOV, JGT, JLT, JUMP, LOAD, STORE, CALL, RET) with proven correctness through formal verification and random testing. Register addressing uses Coptic letter prefixes (Ⲁ, Ⲃ, ⲃ) for bank selection (3 banks × 9 registers = 27), providing 19.7× compression vs 32-register baseline while maintaining full instruction encoding capacity. v8.0 includes cross-bundle integration with B004 (Queen Lotus) and B005 (Tri Language).\n\n## Methodology\n\nTRI-27 implements a balanced ternary architecture where each register stores values from the set {-1, 0, +1}. The instruction encoding uses Coptic alphabet symbols for bank selection (Ⲁ=bank0, Ⲃ=bank1, ⲃ=bank2) and numeric indices 1-9 for register selection within each bank.\n\n**Instruction Encoding:**\n- **MOV:** `MOV Ⲁ1, Ⲃ2` (move from bank0 reg1 to bank1 reg2)\n- **JGT:** `JGT Ⲁ1, label` (jump if Ⲁ1 > 0)\n- **JLT:** `JLT Ⲃ5, label` (jump if Ⲃ5 < 0)\n- **LOAD/STORE:** `LOAD ⲃ9, [addr]`, `STORE Ⲁ1, [addr]`\n\n**Formal Verification:**\n- **Property:** Register values always in {-1, 0, +1}\n- **Method:** Bounded model checking with Z3 + random testing (100K test cases)\n- **Coverage:** 100% instruction coverage, 99.8% operand value space\n\n**State Space:** 3^27 possible register states (7.6×10^12 configurations)\n\n## Algorithm: Ternary Comparison\n\n```zig\nfn compareTrit(a: Trit, b: Trit) Comparison {\n return switch (a) {\n .neg => if (b == .neg) .eq else .lt,\n .zero => if (b == .pos) .lt else if (b == .neg) .gt else .eq,\n .pos => if (b == .pos) .eq else .gt,\n };\n}\n```\n\n**Time Complexity:** O(1) per comparison\n**Space Complexity:** O(1) (no allocation)\n\n## Reproducibility\n\nFormal verification conducted with Z3 4.12.6 (SMT solver) and property-based testing with zigtest. Test suite includes 100K randomly generated instruction sequences with bounded model checking. Code is available at https://github.com/gHashTag/trinity under MIT license. Verification logs archived in `var/trinity/verification/`.\n\n## Datasets\n\n**Test Cases:** 100K randomly generated instruction sequences\n- **Property Tests:** 15 formal properties (register invariants, control flow correctness)\n- **Benchmark Programs:** Fibonacci (recursive/iterative), quicksort, matrix multiplication\n\n**Coverage:** 100% instruction encoding, 99.8% operand value space\n\n## Performance Metrics\n\n- **Instruction Latency:** 1 cycle (MOV), 2 cycles (JGT/JLT), 3 cycles (LOAD/STORE)\n- **Throughput:** 33 MIPS at 100MHz (million instructions/second)\n- **Code Density:** 0.89 bytes/instruction (vs 1.25 bytes for x86-64)\n- **Verification Speed:** 3.2× faster than baseline (property-based testing vs manual proof)\n\n## Ethical Considerations\n\nOpen ISA specification with no patent encumbrances. Coptic alphabet used with respect for cultural heritage. No private data collected in verification benchmarks.\n\n## Limitations\n\n- Fixed-width ternary encoding (no support for multi-trit values)\n- No hardware floating-point operations (requires software emulation)\n- Limited to 27 registers (no spillover to memory)\n- Control flow only supports unconditional jumps (conditional jumps implemented via JGT/JLT + JUMP)\n\n## Future Work\n\n- Implement multi-trit arithmetic (add, subtract, multiply)\n- Add hardware support for function call frames (stack pointer)\n- Design pipelined execution (5-stage: IF-ID-EX-MEM-WB)\n- Investigate superscalar execution (2-way issue)\n- Implement interrupt handling and I/O ports", "keywords": [ "TRI-27 ISA", "ternary processor", @@ -19,31 +19,17 @@ ], "publication_date": "2026-03-27", "version": "8.0", - "doi": "10.5281/zenodo.19227867", + "doi": "10.5281/zenodo.19227869", "related_identifiers": [ { "scheme": "doi", - "identifier": "10.5281/zenodo.19227839", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227745", + "identifier": "10.5281/zenodo.19227875", "relation": "references", "resource_type": "software" } ], "references": [ - "Vasilev, D. (2026). TRI-27: 27-Register Ternary Processor. Zenodo. https://doi.org/10.5281/zenodo.19227867", - "Vasilev, D. (2026). Trinity B004: Queen Lotus Consciousness Cycle — Phenomenological Modeling Framework. Zenodo. https://doi.org/10.5281/zenodo.19227839", - "Vasilev, D. (2026). Trinity B005: Tri Language Specification. Zenodo. https://doi.org/10.5281/zenodo.19227841" + "Vasilev, D. (2026). TRI-27: 27-Register Ternary Processor. Zenodo. https://doi.org/10.5281/zenodo.19227867" ], "license": "CC-BY-4.0", "access_right": "open", @@ -57,4 +43,4 @@ } ], "grants": [] -} +} \ No newline at end of file diff --git a/docs/research/.zenodo.B003_v9.0.json b/docs/research/.zenodo.B003_v9.0.json index e589f1dc47..bfa693eb59 100644 --- a/docs/research/.zenodo.B003_v9.0.json +++ b/docs/research/.zenodo.B003_v9.0.json @@ -1,5 +1,5 @@ { - "title": "Trinity B003: TRI-27 ISA \u2014 27-Register Ternary Processor v9.0", + "title": "Trinity B003: TRI-27 ISA — 27-Register Ternary Processor v9.0", "creators": [ { "name": "Vasilev, Dmitrii", @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "TRI-27 is a 27-register ternary processor implementing Coptic alphabet as three banks of 9 registers, enabling efficient storage and manipulation of balanced ternary values {-1, 0, +1}. The ISA supports 8 fundamental operations (MOV, JGT, JLT, JUMP, LOAD, STORE, CALL, RET) with proven correctness through formal verification and random testing. Register addressing uses Coptic letter prefixes (\u03e2, \u03e3, \u03e5) for bank selection (3 banks \u00d7 9 registers = 27), providing 19.7\u00d7 compression vs 32-register baseline while maintaining full instruction encoding capacity. v9.0 includes enhanced test results with coverage analysis and formal verification statistics.\n\n## Methodology\n\nTRI-27 implements a balanced ternary architecture where each register stores values from the set {-1, 0, +1}. The instruction encoding uses Coptic alphabet symbols for bank selection (\u03e2=bank0, \u03e3=bank1, \u03e5=bank2) and numeric indices 1-9 for register selection within each bank.\n\n**Instruction Encoding:**\n- **MOV:** `MOV \u03e21, \u03e32` (move from bank0 reg1 to bank1 reg2)\n- **JGT:** `JGT \u03e21, label` (jump if \u03e21 > 0)\n- **JLT:** `JLT \u03e35, label` (jump if \u03e35 < 0)\n- **LOAD/STORE:** `LOAD \u03e59, [addr]`, `STORE \u03e21, [addr]`\n- **CALL/RET:** `CALL label` (push return address), `RET` (pop return address)\n\n**Formal Verification:**\n- **Property:** Register values always in {-1, 0, +1}\n- **Method:** Bounded model checking with Z3 4.12.6 (SMT solver) and property-based testing with zigtest\n- **Coverage:** 100% instruction encoding, 99.8% operand value space\n\n**State Space:** 3^27 possible register states (7.6\u00d710^12 configurations)\n\n## Algorithm: Ternary Comparison\n\n```zig\nfn compareTrit(a: Trit, b: Trit) Comparison {\n return switch (a) {\n .neg => if (b == .neg) .eq else .lt,\n .zero => if (b == .pos) .lt else if (b == .neg) .gt else .eq,\n .pos => if (b == .pos) .eq else .gt,\n };\n}\n```\n\n**Time Complexity:** O(1) per comparison\n**Space Complexity:** O(1) (no allocation)\n\n## Experimental Results\n\n**Test Suite:** 129 test cases covering:\n- Instruction encoding (8/8 passing): 100% coverage\n- Control flow (15/15 passing): JGT/JLT unconditional jump semantics\n- Register addressing (27/27 passing): Coptic bank selection\n- Operand handling (68/68 passing): All ternary value combinations\n- Memory operations (11/11 passing): LOAD/STORE with addressing modes\n\n**Formal Verification (Z3 4.12.6):**\n- **Properties Verified:** 15 safety properties (register invariants, control flow correctness)\n- **SAT/UNSAT Status:** All 15 properties verified (100%)\n- **Verification Time:** Mean 12.3s per property (total 184.5s)\n- **Model Complexity:** Max 127 boolean variables per property\n\n**Benchmark Results (100MHz XC7A100T):**\n\n| Program | Cycles | Instructions | MIPS | Baseline Cycles |\n|---------|-------|-------------|------|----------------|\n| Fibonacci (iter) | 1,847 | 1,847 | 33.0 | 2,102 (x86-64) |\n| Fibonacci (rec) | 2,103 | 2,103 | 31.5 | 2,102 (x86-64) |\n| Quicksort | 4,821 | 4,821 | 20.7 | 6,894 (x86-64) |\n| Matrix Mul (3\u00d73) | 7,284 | 7,284 | 13.7 | 15,231 (x86-64) |\n\n**Performance Summary:**\n- **IPC (Instructions Per Cycle):** 1.00 (single-cycle execution)\n- **Throughput:** 33 MIPS at 100MHz\n- **Code Density:** 0.89 bytes/instruction (vs 1.25 bytes for x86-64)\n- **Register Efficiency:** 19.7\u00d7 more registers per bit-width (27\u00d73-bit vs 32\u00d732-bit)\n- **Verification Speed:** 3.2\u00d7 faster than manual proof writing\n\n**Coverage Analysis:**\n- **Instruction Set:** 100% (8/8 opcodes tested)\n- **Operand Space:** 99.8% (68/68 ternary combinations)\n- **Control Flow:** 100% (15/15 jump conditions)\n- **Memory Addressing:** 100% (27/27 register banks)\n- **Combined Coverage:** 98.7% (overall test pass rate)\n\n## Reproducibility\n\nFormal verification conducted with Z3 4.12.6 (SMT solver) and property-based testing with zigtest. Test suite includes 100K randomly generated instruction sequences with bounded model checking. Code is available at https://github.com/gHashTag/trinity under MIT license. Verification logs archived in `var/trinity/verification/`.\n\n**Verification Environment:**\n- SMT Solver: Z3 4.12.6\n- Bound Limit: 127 boolean variables per property\n- Timeout: 30s per property\n- Strategy: QF_BV (quantifier-free bit-vector theory)\n\n**Test Generation:**\n- Random Instruction Sequences: 100K sequences\n- Bounded Model Checking: Max 128 instructions per trace\n- Coverage Goal: Exhaustive operand space (3^68) for operand tests\n\n## Datasets\n\n**Test Cases:** 100K randomly generated instruction sequences\n- **Property Tests:** 15 formal properties (register invariants, control flow correctness)\n- **Benchmark Programs:** Fibonacci (recursive/iterative), quicksort, matrix multiplication\n- **Coverage:** 100% instruction encoding, 99.8% operand value space\n\n## Formal Verification Properties\n\n```z3\n(define-fun is-ternary-val ((x Int)) Bool\n (or (= x (-1)) (= x 0) (= x 1)))\n\n(assert (forall ((r1 Int) (r2 Int))\n (=> (is-ternary-val r1)\n (=> (is-ternary-val r2)\n (=> (=> (= (read-register r1) (read-register r2)))))\n```\n\n**Properties Verified:**\n1. **P1 (Register Invariant):** All registers always contain valid ternary values\n2. **P2 (MOV Correctness):** MOV preserves value across bank transfers\n3. **P3 (JGT Semantics):** JGT jumps only if source > 0 (positive)\n4. **P4 (JLT Semantics):** JLT jumps only if source < 0 (negative)\n5. **P5 (Store-Load Coherence):** STORE to address X followed by LOAD from address X returns same value\n6. **P6 (Call-Return Balance):** Every CALL has matching RET (stack property)\n7. **P7-P15 (No Uninitialized Reads):** LOAD from uninitialized address returns default value (0)\n8. **P8 (Address Bounds):** All addressing modes stay within register bank (0-26)\n9. **P9 (Stack Overflow):** CALL depth limited to 10 (architecture-defined)\n10. **P10 (Stack Underflow):** RET only executed when stack not empty (returns to start address)\n11. **P11-P15 (No Infinite Loops):** No program can generate unbounded loops without explicit jumps\n12. **P12-P15 (Instruction Encoding Uniqueness):** All 8 opcodes map to unique Coptic symbols (no ambiguity)\n13. **P13 (Bank Isolation):** Register banks cannot be mixed in single instruction (e.g., \u03e21, \u03e32)\n14. **P14 (Control Flow Termination):** Programs with explicit termination have bounded worst-case execution\n15. **P15 (JUMP Target Validity):** JUMP/CALL targets must be defined labels\n\n## Ethical Considerations\n\nOpen ISA specification with no patent encumbrances. Coptic alphabet used with respect for cultural heritage. No private data collected in verification benchmarks.\n\n## Limitations\n\n- Fixed-width ternary encoding (no support for multi-trit values)\n- No hardware floating-point operations (requires software emulation)\n- Limited to 27 registers (no spillover to memory)\n- Control flow only supports unconditional jumps (conditional jumps implemented via JGT/JLT + JUMP)\n- No interrupt handling mechanism (designed for isolated execution)\n- Stack limited to 10 return addresses (hard-coded architecture limit)\n\n## Future Work\n\n- Implement multi-trit arithmetic (add, subtract, multiply)\n- Add hardware support for function call frames (stack pointer)\n- Design pipelined execution (5-stage: IF-ID-EX-MEM-WB)\n- Investigate superscalar execution (2-way issue)\n- Implement interrupt handling and I/O ports\n- Extend to 64 registers (4 banks \u00d7 16 registers)\n- Design hybrid execution (FPGA + CPU co-processor)\n- Formal verification for pipelined semantics\n- Model checking for concurrent execution\n\n## References\n\n- Biere, A., Clarke, E., & Kroening, D. (1999). Z3: An Efficient SMT Solver. TACAS 1999: 447-456.\n- de Moura, L., Bj\u00f8rner, N., et al. (2008). Z3 4.8: An Efficient SMT Solver. TACAS 2008: 337-356.\n- Vazquez, D. (2008). Coptic Alphabet: Unicode Standard for Ancient Egyptian Scripts. Unicode 9.0.0.\n- Vasilev, D. (2026). TRI-27: 27-Register Ternary Processor. Zenodo. https://doi.org/10.5281/zenodo.19227867\n- Vasilev, D. (2026). Trinity B004: Queen Lotus Consciousness Cycle \u2014 Phenomenological Modeling Framework. Zenodo. https://doi.org/10.5281/zenodo.19227839\n- Vasilev, D. (2026). Trinity B005: Tri Language Specification. Zenodo. https://doi.org/10.5281/zenodo.19227841", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nTRI-27 is a 27-register ternary processor implementing Coptic alphabet as three banks of 9 registers, enabling efficient storage and manipulation of balanced ternary values {-1, 0, +1}. The ISA supports 8 fundamental operations (MOV, JGT, JLT, JUMP, LOAD, STORE, CALL, RET) with proven correctness through formal verification and random testing. Register addressing uses Coptic letter prefixes (Ϣ, ϣ, ϥ) for bank selection (3 banks × 9 registers = 27), providing 19.7× compression vs 32-register baseline while maintaining full instruction encoding capacity. v9.0 includes enhanced test results with coverage analysis and formal verification statistics.\n\n## Methodology\n\nTRI-27 implements a balanced ternary architecture where each register stores values from the set {-1, 0, +1}. The instruction encoding uses Coptic alphabet symbols for bank selection (Ϣ=bank0, ϣ=bank1, ϥ=bank2) and numeric indices 1-9 for register selection within each bank.\n\n**Instruction Encoding:**\n- **MOV:** `MOV Ϣ1, ϣ2` (move from bank0 reg1 to bank1 reg2)\n- **JGT:** `JGT Ϣ1, label` (jump if Ϣ1 > 0)\n- **JLT:** `JLT ϣ5, label` (jump if ϣ5 < 0)\n- **LOAD/STORE:** `LOAD ϥ9, [addr]`, `STORE Ϣ1, [addr]`\n- **CALL/RET:** `CALL label` (push return address), `RET` (pop return address)\n\n**Formal Verification:**\n- **Property:** Register values always in {-1, 0, +1}\n- **Method:** Bounded model checking with Z3 4.12.6 (SMT solver) and property-based testing with zigtest\n- **Coverage:** 100% instruction encoding, 99.8% operand value space\n\n**State Space:** 3^27 possible register states (7.6×10^12 configurations)\n\n## Algorithm: Ternary Comparison\n\n```zig\nfn compareTrit(a: Trit, b: Trit) Comparison {\n return switch (a) {\n .neg => if (b == .neg) .eq else .lt,\n .zero => if (b == .pos) .lt else if (b == .neg) .gt else .eq,\n .pos => if (b == .pos) .eq else .gt,\n };\n}\n```\n\n**Time Complexity:** O(1) per comparison\n**Space Complexity:** O(1) (no allocation)\n\n## Experimental Results\n\n**Test Suite:** 129 test cases covering:\n- Instruction encoding (8/8 passing): 100% coverage\n- Control flow (15/15 passing): JGT/JLT unconditional jump semantics\n- Register addressing (27/27 passing): Coptic bank selection\n- Operand handling (68/68 passing): All ternary value combinations\n- Memory operations (11/11 passing): LOAD/STORE with addressing modes\n\n**Formal Verification (Z3 4.12.6):**\n- **Properties Verified:** 15 safety properties (register invariants, control flow correctness)\n- **SAT/UNSAT Status:** All 15 properties verified (100%)\n- **Verification Time:** Mean 12.3s per property (total 184.5s)\n- **Model Complexity:** Max 127 boolean variables per property\n\n**Benchmark Results (100MHz XC7A100T):**\n\n| Program | Cycles | Instructions | MIPS | Baseline Cycles |\n|---------|-------|-------------|------|----------------|\n| Fibonacci (iter) | 1,847 | 1,847 | 33.0 | 2,102 (x86-64) |\n| Fibonacci (rec) | 2,103 | 2,103 | 31.5 | 2,102 (x86-64) |\n| Quicksort | 4,821 | 4,821 | 20.7 | 6,894 (x86-64) |\n| Matrix Mul (3×3) | 7,284 | 7,284 | 13.7 | 15,231 (x86-64) |\n\n**Performance Summary:**\n- **IPC (Instructions Per Cycle):** 1.00 (single-cycle execution)\n- **Throughput:** 33 MIPS at 100MHz\n- **Code Density:** 0.89 bytes/instruction (vs 1.25 bytes for x86-64)\n- **Register Efficiency:** 19.7× more registers per bit-width (27×3-bit vs 32×32-bit)\n- **Verification Speed:** 3.2× faster than manual proof writing\n\n**Coverage Analysis:**\n- **Instruction Set:** 100% (8/8 opcodes tested)\n- **Operand Space:** 99.8% (68/68 ternary combinations)\n- **Control Flow:** 100% (15/15 jump conditions)\n- **Memory Addressing:** 100% (27/27 register banks)\n- **Combined Coverage:** 98.7% (overall test pass rate)\n\n## Reproducibility\n\nFormal verification conducted with Z3 4.12.6 (SMT solver) and property-based testing with zigtest. Test suite includes 100K randomly generated instruction sequences with bounded model checking. Code is available at https://github.com/gHashTag/trinity under MIT license. Verification logs archived in `var/trinity/verification/`.\n\n**Verification Environment:**\n- SMT Solver: Z3 4.12.6\n- Bound Limit: 127 boolean variables per property\n- Timeout: 30s per property\n- Strategy: QF_BV (quantifier-free bit-vector theory)\n\n**Test Generation:**\n- Random Instruction Sequences: 100K sequences\n- Bounded Model Checking: Max 128 instructions per trace\n- Coverage Goal: Exhaustive operand space (3^68) for operand tests\n\n## Datasets\n\n**Test Cases:** 100K randomly generated instruction sequences\n- **Property Tests:** 15 formal properties (register invariants, control flow correctness)\n- **Benchmark Programs:** Fibonacci (recursive/iterative), quicksort, matrix multiplication\n- **Coverage:** 100% instruction encoding, 99.8% operand value space\n\n## Formal Verification Properties\n\n```z3\n(define-fun is-ternary-val ((x Int)) Bool\n (or (= x (-1)) (= x 0) (= x 1)))\n\n(assert (forall ((r1 Int) (r2 Int))\n (=> (is-ternary-val r1)\n (=> (is-ternary-val r2)\n (=> (=> (= (read-register r1) (read-register r2)))))\n```\n\n**Properties Verified:**\n1. **P1 (Register Invariant):** All registers always contain valid ternary values\n2. **P2 (MOV Correctness):** MOV preserves value across bank transfers\n3. **P3 (JGT Semantics):** JGT jumps only if source > 0 (positive)\n4. **P4 (JLT Semantics):** JLT jumps only if source < 0 (negative)\n5. **P5 (Store-Load Coherence):** STORE to address X followed by LOAD from address X returns same value\n6. **P6 (Call-Return Balance):** Every CALL has matching RET (stack property)\n7. **P7-P15 (No Uninitialized Reads):** LOAD from uninitialized address returns default value (0)\n8. **P8 (Address Bounds):** All addressing modes stay within register bank (0-26)\n9. **P9 (Stack Overflow):** CALL depth limited to 10 (architecture-defined)\n10. **P10 (Stack Underflow):** RET only executed when stack not empty (returns to start address)\n11. **P11-P15 (No Infinite Loops):** No program can generate unbounded loops without explicit jumps\n12. **P12-P15 (Instruction Encoding Uniqueness):** All 8 opcodes map to unique Coptic symbols (no ambiguity)\n13. **P13 (Bank Isolation):** Register banks cannot be mixed in single instruction (e.g., Ϣ1, ϣ2)\n14. **P14 (Control Flow Termination):** Programs with explicit termination have bounded worst-case execution\n15. **P15 (JUMP Target Validity):** JUMP/CALL targets must be defined labels\n\n## Ethical Considerations\n\nOpen ISA specification with no patent encumbrances. Coptic alphabet used with respect for cultural heritage. No private data collected in verification benchmarks.\n\n## Limitations\n\n- Fixed-width ternary encoding (no support for multi-trit values)\n- No hardware floating-point operations (requires software emulation)\n- Limited to 27 registers (no spillover to memory)\n- Control flow only supports unconditional jumps (conditional jumps implemented via JGT/JLT + JUMP)\n- No interrupt handling mechanism (designed for isolated execution)\n- Stack limited to 10 return addresses (hard-coded architecture limit)\n\n## Future Work\n\n- Implement multi-trit arithmetic (add, subtract, multiply)\n- Add hardware support for function call frames (stack pointer)\n- Design pipelined execution (5-stage: IF-ID-EX-MEM-WB)\n- Investigate superscalar execution (2-way issue)\n- Implement interrupt handling and I/O ports\n- Extend to 64 registers (4 banks × 16 registers)\n- Design hybrid execution (FPGA + CPU co-processor)\n- Formal verification for pipelined semantics\n- Model checking for concurrent execution\n\n## References\n\n- Biere, A., Clarke, E., & Kroening, D. (1999). Z3: An Efficient SMT Solver. TACAS 1999: 447-456.\n- de Moura, L., Bjørner, N., et al. (2008). Z3 4.8: An Efficient SMT Solver. TACAS 2008: 337-356.\n- Vazquez, D. (2008). Coptic Alphabet: Unicode Standard for Ancient Egyptian Scripts. Unicode 9.0.0.\n- Vasilev, D. (2026). TRI-27: 27-Register Ternary Processor. Zenodo. https://doi.org/10.5281/zenodo.19227867\n- Vasilev, D. (2026). Trinity B004: Queen Lotus Consciousness Cycle — Phenomenological Modeling Framework. Zenodo. https://doi.- Vasilev, D. (2026). Trinity B005: Tri Language Specification. Zenodo. https://doi.", "keywords": [ "TRI-27 ISA", "ternary processor", @@ -23,18 +23,6 @@ "version": "9.0", "doi": "10.5281/zenodo.19227869", "related_identifiers": [ - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227839", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", - "relation": "references", - "resource_type": "software" - }, { "scheme": "doi", "identifier": "10.5281/zenodo.19227867", @@ -51,7 +39,7 @@ "access_right": "open", "resource_type": { "type": "software", - "title": "Trinity B003: TRI-27 ISA \u2014 27-Register Ternary Processor" + "title": "Trinity B003: TRI-27 ISA — 27-Register Ternary Processor" }, "communities": [ { @@ -101,7 +89,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/docs/research/.zenodo.B004_v8.0.json b/docs/research/.zenodo.B004_v8.0.json index e9a82e884e..c1a5ef0cd6 100644 --- a/docs/research/.zenodo.B004_v8.0.json +++ b/docs/research/.zenodo.B004_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Queen Lotus Cycle is a phenomenological consciousness modeling framework implementing the four-phase cycle: Awakening (Ⲁ), Recognition (Ⲃ), Discrimination (ⲃ), and Equilibrium (φ). Each phase corresponds to a fundamental cognitive operation with mathematically proven transition conditions and state space properties. The framework integrates with TRI-27 processor (B003) for hardware acceleration, providing 3.2× speedup in formal verification tasks. v8.0 includes enhanced peer review templates, cross-bundle integration, and dependency graph visualization.\n\n## Methodology\n\nQueen Lotus implements a state machine where each cognitive phase corresponds to a distinct computational state with verified transition properties. The framework is grounded in phenomenology (Husserl, Merleau-Ponty) and global workspace theory (Baars, Dehaene).\n\n**Four-Phase Cycle:**\n1. **Awakening (Ⲁ):** Sensory input integration, threshold-based activation\n2. **Recognition (Ⲃ):** Pattern matching with memory, similarity scoring\n3. **Discrimination (ⲃ):** Attention selection, noise filtering\n4. **Equilibrium (φ):** Conscious broadcast, global workspace update\n\n**Transition Conditions:**\n```\nⲀ → Ⲃ : input_entropy > threshold_awakening\nⲂ → ⲃ : pattern_match > threshold_recognition\nⲃ → φ : attention_confidence > threshold_discrimination\nφ → Ⲁ : broadcast_complete || global_reset\n```\n\n**State Space:** 4^N possible configurations (N = number of active cognitive modules)\n\n**Theorem (Liveness):** All cycles eventually return to φ (no infinite loops)\n**Proof:** By induction on cycle depth (see docs/research/lotus_proof.pdf)\n\n## Algorithm: Consciousness Cycle Transition\n\n```zig\nfn lotusTransition(state: LotusState, input: SensoryInput) LotusState {\n return switch (state) {\n .Awakening => if (input.entropy() > THRESHOLD_AWAKEN) \n .Recognition else .Awakening,\n .Recognition => if (patternMatch(input) > THRESHOLD_RECOG)\n .Discrimination else .Awakening,\n .Discrimination => if (attentionConfidence() > THRESHOLD_DISC)\n .Equilibrium else .Recognition,\n .Equilibrium => if (broadcastComplete() OR globalReset())\n .Awakening else .Equilibrium,\n };\n}\n```\n\n**Time Complexity:** O(N) per transition (N = sensory input dimension)\n**Space Complexity:** O(1) (constant state size)\n\n## Reproducibility\n\nAll proofs verified with Coq 8.18 and Z3 4.12.6. State machine model checking conducted with nuXmv 2.0.0. Test suite includes 1M random state transitions. Code is available at https://github.com/gHashTag/trinity under MIT license. Formal proofs archived in `docs/research/lotus/proofs/`.\n\n## Datasets\n\n**Synthetic Benchmarks:** 1M random state transitions\n- **Stress Tests:** 10K worst-case scenarios (max entropy inputs)\n- **Property Tests:** 15 invariants (liveness, safety, fairness)\n\n**Real-World Benchmarks:** None (framework requires integration with sensor data)\n\n## Performance Metrics\n\n- **Transition Latency:** 45ns (TRI-27 hardware), 142ns (software baseline)\n- **Throughput:** 22M state transitions/second (TRI-27 @ 100MHz)\n- **Hardware Speedup:** 3.2× vs software implementation\n- **Verification Time:** 2.3 seconds for 1M transitions (Coq proof assistant)\n\n## Integration with TRI-27\n\n| Lotus Phase | TRI-27 Instruction | Purpose |\n|-------------|-------------------|----------|\n| Awakening | LOAD Ⲁ1, [sensor] | Read sensory input |\n| Recognition | JGT Ⲃ2, pattern | Pattern matching check |\n| Discrimination | STORE ⲃ3, [attention] | Select attention target |\n| Equilibrium | MOV φ, [workspace] | Broadcast to global workspace |\n\n## Ethical Considerations\n\nConsciousness modeling is conducted with philosophical rigor, avoiding claims of sentient capability. The framework is a computational metaphor, not a theory of biological consciousness. No psychological or physiological data collected.\n\n## Broader Impact\n\nQueen Lotus provides a formal framework for consciousness-inspired AI architectures, enabling reproducible research in cognitive modeling. Applications include explainable AI (traceable decision cycles), attention mechanism design, and ethical AI (explicit discrimination phase). Potential societal impact includes improved AI transparency and reduced bias through explicit discrimination modeling.\n\n## Limitations\n\n- No empirical validation against biological consciousness data\n- Synthetic benchmarks only (no real-world sensor integration)\n- Fixed threshold values (not adaptive to workload)\n- No learning mechanism (thresholds hand-tuned)\n- Limited to 4-phase cycle (does not model sub-conscious processing)\n\n## Future Work\n\n- Implement adaptive thresholds via reinforcement learning\n- Add sub-conscious processing layer (automatic pattern recognition)\n- Integrate with sensor hardware (camera, microphone)\n- Empirical validation against EEG/fMRI data\n- Extend to multi-agent consciousness (swarm consciousness)", + "description": "[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nQueen Lotus Cycle is a phenomenological consciousness modeling framework implementing the four-phase cycle: Awakening (Ⲁ), Recognition (Ⲃ), Discrimination (ⲃ), and Equilibrium (φ). Each phase corresponds to a fundamental cognitive operation with mathematically proven transition conditions and state space properties. The framework integrates with TRI-27 processor (B003) for hardware acceleration, providing 3.2× speedup in formal verification tasks. v8.0 includes enhanced peer review templates, cross-bundle integration, and dependency graph visualization.\n\n## Methodology\n\nQueen Lotus implements a state machine where each cognitive phase corresponds to a distinct computational state with verified transition properties. The framework is grounded in phenomenology (Husserl, Merleau-Ponty) and global workspace theory (Baars, Dehaene).\n\n**Four-Phase Cycle:**\n1. **Awakening (Ⲁ):** Sensory input integration, threshold-based activation\n2. **Recognition (Ⲃ):** Pattern matching with memory, similarity scoring\n3. **Discrimination (ⲃ):** Attention selection, noise filtering\n4. **Equilibrium (φ):** Conscious broadcast, global workspace update\n\n**Transition Conditions:**\n```\nⲀ → Ⲃ : input_entropy > threshold_awakening\nⲂ → ⲃ : pattern_match > threshold_recognition\nⲃ → φ : attention_confidence > threshold_discrimination\nφ → Ⲁ : broadcast_complete || global_reset\n```\n\n**State Space:** 4^N possible configurations (N = number of active cognitive modules)\n\n**Theorem (Liveness):** All cycles eventually return to φ (no infinite loops)\n**Proof:** By induction on cycle depth (see docs/research/lotus_proof.pdf)\n\n## Algorithm: Consciousness Cycle Transition\n\n```zig\nfn lotusTransition(state: LotusState, input: SensoryInput) LotusState {\n return switch (state) {\n .Awakening => if (input.entropy() > THRESHOLD_AWAKEN) \n .Recognition else .Awakening,\n .Recognition => if (patternMatch(input) > THRESHOLD_RECOG)\n .Discrimination else .Awakening,\n .Discrimination => if (attentionConfidence() > THRESHOLD_DISC)\n .Equilibrium else .Recognition,\n .Equilibrium => if (broadcastComplete() OR globalReset())\n .Awakening else .Equilibrium,\n };\n}\n```\n\n**Time Complexity:** O(N) per transition (N = sensory input dimension)\n**Space Complexity:** O(1) (constant state size)\n\n## Reproducibility\n\nAll proofs verified with Coq 8.18 and Z3 4.12.6. State machine model checking conducted with nuXmv 2.0.0. Test suite includes 1M random state transitions. Code is available at https://github.com/gHashTag/trinity under MIT license. Formal proofs archived in `docs/research/lotus/proofs/`.\n\n## Datasets\n\n**Synthetic Benchmarks:** 1M random state transitions\n- **Stress Tests:** 10K worst-case scenarios (max entropy inputs)\n- **Property Tests:** 15 invariants (liveness, safety, fairness)\n\n**Real-World Benchmarks:** None (framework requires integration with sensor data)\n\n## Performance Metrics\n\n- **Transition Latency:** 45ns (TRI-27 hardware), 142ns (software baseline)\n- **Throughput:** 22M state transitions/second (TRI-27 @ 100MHz)\n- **Hardware Speedup:** 3.2× vs software implementation\n- **Verification Time:** 2.3 seconds for 1M transitions (Coq proof assistant)\n\n## Integration with TRI-27\n\n| Lotus Phase | TRI-27 Instruction | Purpose |\n|-------------|-------------------|----------|\n| Awakening | LOAD Ⲁ1, [sensor] | Read sensory input |\n| Recognition | JGT Ⲃ2, pattern | Pattern matching check |\n| Discrimination | STORE ⲃ3, [attention] | Select attention target |\n| Equilibrium | MOV φ, [workspace] | Broadcast to global workspace |\n\n## Ethical Considerations\n\nConsciousness modeling is conducted with philosophical rigor, avoiding claims of sentient capability. The framework is a computational metaphor, not a theory of biological consciousness. No psychological or physiological data collected.\n\n## Broader Impact\n\nQueen Lotus provides a formal framework for consciousness-inspired AI architectures, enabling reproducible research in cognitive modeling. Applications include explainable AI (traceable decision cycles), attention mechanism design, and ethical AI (explicit discrimination phase). Potential societal impact includes improved AI transparency and reduced bias through explicit discrimination modeling.\n\n## Limitations\n\n- No empirical validation against biological consciousness data\n- Synthetic benchmarks only (no real-world sensor integration)\n- Fixed threshold values (not adaptive to workload)\n- No learning mechanism (thresholds hand-tuned)\n- Limited to 4-phase cycle (does not model sub-conscious processing)\n\n## Future Work\n\n- Implement adaptive thresholds via reinforcement learning\n- Add sub-conscious processing layer (automatic pattern recognition)\n- Integrate with sensor hardware (camera, microphone)\n- Empirical validation against EEG/fMRI data\n- Extend to multi-agent consciousness (swarm consciousness)", "keywords": [ "consciousness modeling", "phenomenology", @@ -20,25 +20,13 @@ ], "publication_date": "2026-03-27", "version": "8.0", - "doi": "10.5281/zenodo.19227839", + "doi": "10.5281/zenodo.19227871", "related_identifiers": [ { "scheme": "doi", "identifier": "10.5281/zenodo.19227867", "relation": "references", "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227845", - "relation": "references", - "resource_type": "software" } ], "references": [ @@ -59,4 +47,4 @@ } ], "grants": [] -} +} \ No newline at end of file diff --git a/docs/research/.zenodo.B004_v9.0.json b/docs/research/.zenodo.B004_v9.0.json index ce51e5c6f9..40fbaeb001 100644 --- a/docs/research/.zenodo.B004_v9.0.json +++ b/docs/research/.zenodo.B004_v9.0.json @@ -1,5 +1,5 @@ { - "title": "Trinity B004: Queen Lotus Consciousness Cycle \u2014 Phenomenological Modeling Framework v9.0", + "title": "Trinity B004: Queen Lotus Consciousness Cycle — Phenomenological Modeling Framework v9.0", "creators": [ { "name": "Vasilev, Dmitrii", @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Queen Lotus Cycle is a phenomenological consciousness modeling framework implementing the five-phase cycle: SEED (\ud83c\udf31), SPROUT (\ud83c\udf3f), BUD (\ud83c\udf37), BLOOM (\ud83e\udeb7), and WITHER (\ud83c\udf42). Each phase corresponds to a fundamental cognitive operation with mathematically proven transition conditions and state space properties. The framework integrates with TRI-27 processor (B003) for hardware acceleration, providing 3.2\u00d7 speedup in formal verification tasks. v9.0 includes enhanced self-learning results with policy coverage metrics, convergence analysis, and episode tracking statistics.\n\n## Methodology\n\nQueen Lotus implements a state machine where each cognitive phase corresponds to a distinct computational state with verified transition properties. The framework is grounded in phenomenology (Husserl, Merleau-Ponty) and global workspace theory (Baars, Dehaene).\n\n**Five-Phase Cycle (v9.0):**\n1. **SEED (\ud83c\udf31):** Potential state, dormant consciousness\n2. **SPROUT (\ud83c\udf3f):** Emerging awareness, initial activation\n3. **BUD (\ud83c\udf37):** Preparatory focus, attention selection\n4. **BLOOM (\ud83e\udeb7):** Full integration, conscious broadcast\n5. **WITHER (\ud83c\udf42):** Rest/release, reset for next cycle\n\n**Transition Conditions:**\n```\nSEED \u2192 SPROUT : input_entropy > threshold_seed (0.2)\nSPROUT \u2192 BUD : pattern_match > threshold_sprout (0.5)\nBUD \u2192 BLOOM : attention_confidence > threshold_bud (0.7)\nBLOOM \u2192 WITHER : broadcast_duration > threshold_bloom (100ms)\nWITHER \u2192 SEED : rest_complete || global_reset\n```\n\n**State Space:** 5^N possible configurations (N = number of active cognitive modules)\n**Liveness Theorem:** All cycles eventually return to SEED (no infinite loops)\n**Proof:** By induction on cycle depth (see docs/research/lotus_proof.pdf)\n\n## Algorithm: Consciousness Cycle Transition\n\n```zig\nfn lotusTransition(state: LotusState, input: SensoryInput) LotusState {\n return switch (state) {\n .Seed => if (input.entropy() > THRESHOLD_SEED) \n .Sprout else .Seed,\n .Sprout => if (patternMatch(input) > THRESHOLD_SPROUT)\n .Bud else .Seed,\n .Bud => if (attentionConfidence() > THRESHOLD_BUD)\n .Bloom else .Sprout,\n .Bloom => if (broadcastDuration() > THRESHOLD_BLOOM)\n .Wither else .Bloom,\n .Wither => if (restComplete() OR globalReset())\n .Seed else .Wither,\n };\n}\n```\n\n**Time Complexity:** O(N) per transition (N = sensory input dimension)\n**Space Complexity:** O(1) (constant state size)\n\n## Self-Learning Results (v9.0)\n\n**Policy Coverage Analysis (10K episodes):**\n\n| Phase | Coverage | Mean Episode Time | Transitions | Success Rate |\n|-------|----------|-------------------|-------------|--------------|\n| SEED | 100.0% | 12.3 ms | 10,000 | 100% |\n| SPROUT | 98.7% | 45.6 ms | 9,870 | 98.2% |\n| BUD | 92.3% | 78.9 ms | 9,230 | 91.5% |\n| BLOOM | 87.1% | 112.4 ms | 8,710 | 85.8% |\n| WITHER | 99.5% | 23.1 ms | 9,950 | 99.3% |\n| **OVERALL** | **95.5%** | **54.5 ms** | **47,760** | **94.9%** |\n\n**Convergence Analysis:**\n- **Episodes to 90% Coverage:** 3,247 episodes\n- **Episodes to 95% Coverage:** 6,891 episodes\n- **Episodes to 99% Coverage:** 9,456 episodes (extrapolated)\n- **Learning Rate:** Exponential decay with \u03c4 = 0.618\n- **Policy Stability:** 98.2% after 8K episodes\n\n**Statistical Analysis (Bootstrap, 10K resamples):**\n- **Mean Coverage:** 95.5% \u00b1 2.3%\n- **95% Confidence Interval:** [93.1%, 97.9%]\n- **Convergence Rate:** r = 0.997 (episodes vs coverage)\n- **Significance vs Random:** t(18) = 23.4, p < 0.001 ***\n\n## Reproducibility\n\nAll proofs verified with Coq 8.18 and Z3 4.12.6. State machine model checking conducted with nuXmv 2.0.0. Test suite includes 1M random state transitions. Code is available at https://github.com/gHashTag/trinity under MIT license. Formal proofs archived in `docs/research/lotus/proofs/`.\n\n**Verification Environment:**\n- Proof Assistant: Coq 8.18.0\n- SMT Solver: Z3 4.12.6\n- Model Checker: nuXmv 2.0.0\n- Random Seeds: Fixed per test batch (1000 episodes)\n\n**Test Coverage:**\n- **State Transitions:** 100% (47,760/47,760)\n- **Property Verification:** 100% (15/15 properties)\n- **Model Checking:** 100% (no counterexamples found)\n- **Self-Learning Tests:** 4/4 passing\n\n## Datasets\n\n**Synthetic Benchmarks:** 1M random state transitions\n- **Stress Tests:** 10K worst-case scenarios (max entropy inputs)\n- **Property Tests:** 15 invariants (liveness, safety, fairness)\n- **Self-Learning Episodes:** 10,000 episodes (47,760 transitions)\n\n**Real-World Benchmarks:** None (framework requires integration with sensor data)\n\n## Performance Metrics\n\n- **Transition Latency:** 45ns (TRI-27 hardware), 142ns (software baseline)\n- **Throughput:** 22M state transitions/second (TRI-27 @ 100MHz)\n- **Hardware Speedup:** 3.2\u00d7 vs software implementation\n- **Verification Time:** 2.3 seconds for 1M transitions (Coq proof assistant)\n- **Episode Duration:** Mean 54.5ms (\u00b131.2ms SD)\n\n## Integration with TRI-27\n\n| Lotus Phase | TRI-27 Instruction | Purpose |\n|-------------|-------------------|----------|\n| SEED | LOAD \u03e21, [sensor] | Read sensory input |\n| SPROUT | JGT \u03e32, pattern | Pattern matching check |\n| BUD | STORE \u03e53, [attention] | Select attention target |\n| BLOOM | MOV \u03c6, [workspace] | Broadcast to global workspace |\n| WITHER | CALL reset_cycle | Reset for next cycle |\n\n## Self-Learning Algorithm\n\n```zig\nfn lotusSelfLearning(config: Tri27Config, episode: u64) LearningResult {\n // Adaptive threshold adjustment based on episode success\n const success_rate = computeSuccessRate(episode);\n const new_threshold = if (success_rate > 0.9)\n config.threshold * 0.9 // tighten threshold\n else if (success_rate < 0.7)\n config.threshold * 1.1 // relax threshold\n else\n config.threshold;\n \n // Kill threshold prevents infinite loops\n if (episode > config.kill_threshold)\n return LearningResult.Failure;\n \n return LearningResult{ .threshold = new_threshold, .continue = true };\n}\n```\n\n**Convergence Guarantees:**\n- **Monotonic:** Coverage increases monotonically after episode 1000\n- **Bounded:** Maximum coverage 100% (theoretical limit)\n- **Terminating:** All episodes terminate (kill_threshold = 100,000)\n\n## Ethical Considerations\n\nConsciousness modeling is conducted with philosophical rigor, avoiding claims of sentient capability. The framework is a computational metaphor, not a theory of biological consciousness. No psychological or physiological data collected.\n\n## Broader Impact\n\nQueen Lotus provides a formal framework for consciousness-inspired AI architectures, enabling reproducible research in cognitive modeling. Applications include explainable AI (traceable decision cycles), attention mechanism design, and ethical AI (explicit discrimination phase). Potential societal impact includes improved AI transparency and reduced bias through explicit discrimination modeling.\n\n## Limitations\n\n- No empirical validation against biological consciousness data\n- Synthetic benchmarks only (no real-world sensor integration)\n- Fixed threshold values (not adaptive to workload without self-learning)\n- Self-learning requires episode restart (no online learning)\n- Limited to 5-phase cycle (does not model sub-conscious processing)\n- Kill threshold set to 100,000 episodes (potential early termination)\n\n## Future Work\n\n- Implement online learning (adaptive thresholds during episodes)\n- Add sub-conscious processing layer (automatic pattern recognition)\n- Integrate with sensor hardware (camera, microphone)\n- Empirical validation against EEG/fMRI data\n- Extend to multi-agent consciousness (swarm consciousness)\n- Investigate chaotic dynamics (strange attractors in phase space)\n- Formal verification of self-learning convergence\n", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nQueen Lotus Cycle is a phenomenological consciousness modeling framework implementing the five-phase cycle: SEED (🌱), SPROUT (🌿), BUD (🌷), BLOOM (🪷), and WITHER (🍂). Each phase corresponds to a fundamental cognitive operation with mathematically proven transition conditions and state space properties. The framework integrates with TRI-27 processor (B003) for hardware acceleration, providing 3.2× speedup in formal verification tasks. v9.0 includes enhanced self-learning results with policy coverage metrics, convergence analysis, and episode tracking statistics.\n\n## Methodology\n\nQueen Lotus implements a state machine where each cognitive phase corresponds to a distinct computational state with verified transition properties. The framework is grounded in phenomenology (Husserl, Merleau-Ponty) and global workspace theory (Baars, Dehaene).\n\n**Five-Phase Cycle (v9.0):**\n1. **SEED (🌱):** Potential state, dormant consciousness\n2. **SPROUT (🌿):** Emerging awareness, initial activation\n3. **BUD (🌷):** Preparatory focus, attention selection\n4. **BLOOM (🪷):** Full integration, conscious broadcast\n5. **WITHER (🍂):** Rest/release, reset for next cycle\n\n**Transition Conditions:**\n```\nSEED → SPROUT : input_entropy > threshold_seed (0.2)\nSPROUT → BUD : pattern_match > threshold_sprout (0.5)\nBUD → BLOOM : attention_confidence > threshold_bud (0.7)\nBLOOM → WITHER : broadcast_duration > threshold_bloom (100ms)\nWITHER → SEED : rest_complete || global_reset\n```\n\n**State Space:** 5^N possible configurations (N = number of active cognitive modules)\n**Liveness Theorem:** All cycles eventually return to SEED (no infinite loops)\n**Proof:** By induction on cycle depth (see docs/research/lotus_proof.pdf)\n\n## Algorithm: Consciousness Cycle Transition\n\n```zig\nfn lotusTransition(state: LotusState, input: SensoryInput) LotusState {\n return switch (state) {\n .Seed => if (input.entropy() > THRESHOLD_SEED) \n .Sprout else .Seed,\n .Sprout => if (patternMatch(input) > THRESHOLD_SPROUT)\n .Bud else .Seed,\n .Bud => if (attentionConfidence() > THRESHOLD_BUD)\n .Bloom else .Sprout,\n .Bloom => if (broadcastDuration() > THRESHOLD_BLOOM)\n .Wither else .Bloom,\n .Wither => if (restComplete() OR globalReset())\n .Seed else .Wither,\n };\n}\n```\n\n**Time Complexity:** O(N) per transition (N = sensory input dimension)\n**Space Complexity:** O(1) (constant state size)\n\n## Self-Learning Results (v9.0)\n\n**Policy Coverage Analysis (10K episodes):**\n\n| Phase | Coverage | Mean Episode Time | Transitions | Success Rate |\n|-------|----------|-------------------|-------------|--------------|\n| SEED | 100.0% | 12.3 ms | 10,000 | 100% |\n| SPROUT | 98.7% | 45.6 ms | 9,870 | 98.2% |\n| BUD | 92.3% | 78.9 ms | 9,230 | 91.5% |\n| BLOOM | 87.1% | 112.4 ms | 8,710 | 85.8% |\n| WITHER | 99.5% | 23.1 ms | 9,950 | 99.3% |\n| **OVERALL** | **95.5%** | **54.5 ms** | **47,760** | **94.9%** |\n\n**Convergence Analysis:**\n- **Episodes to 90% Coverage:** 3,247 episodes\n- **Episodes to 95% Coverage:** 6,891 episodes\n- **Episodes to 99% Coverage:** 9,456 episodes (extrapolated)\n- **Learning Rate:** Exponential decay with τ = 0.618\n- **Policy Stability:** 98.2% after 8K episodes\n\n**Statistical Analysis (Bootstrap, 10K resamples):**\n- **Mean Coverage:** 95.5% ± 2.3%\n- **95% Confidence Interval:** [93.1%, 97.9%]\n- **Convergence Rate:** r = 0.997 (episodes vs coverage)\n- **Significance vs Random:** t(18) = 23.4, p < 0.001 ***\n\n## Reproducibility\n\nAll proofs verified with Coq 8.18 and Z3 4.12.6. State machine model checking conducted with nuXmv 2.0.0. Test suite includes 1M random state transitions. Code is available at https://github.com/gHashTag/trinity under MIT license. Formal proofs archived in `docs/research/lotus/proofs/`.\n\n**Verification Environment:**\n- Proof Assistant: Coq 8.18.0\n- SMT Solver: Z3 4.12.6\n- Model Checker: nuXmv 2.0.0\n- Random Seeds: Fixed per test batch (1000 episodes)\n\n**Test Coverage:**\n- **State Transitions:** 100% (47,760/47,760)\n- **Property Verification:** 100% (15/15 properties)\n- **Model Checking:** 100% (no counterexamples found)\n- **Self-Learning Tests:** 4/4 passing\n\n## Datasets\n\n**Synthetic Benchmarks:** 1M random state transitions\n- **Stress Tests:** 10K worst-case scenarios (max entropy inputs)\n- **Property Tests:** 15 invariants (liveness, safety, fairness)\n- **Self-Learning Episodes:** 10,000 episodes (47,760 transitions)\n\n**Real-World Benchmarks:** None (framework requires integration with sensor data)\n\n## Performance Metrics\n\n- **Transition Latency:** 45ns (TRI-27 hardware), 142ns (software baseline)\n- **Throughput:** 22M state transitions/second (TRI-27 @ 100MHz)\n- **Hardware Speedup:** 3.2× vs software implementation\n- **Verification Time:** 2.3 seconds for 1M transitions (Coq proof assistant)\n- **Episode Duration:** Mean 54.5ms (±31.2ms SD)\n\n## Integration with TRI-27\n\n| Lotus Phase | TRI-27 Instruction | Purpose |\n|-------------|-------------------|----------|\n| SEED | LOAD Ϣ1, [sensor] | Read sensory input |\n| SPROUT | JGT ϣ2, pattern | Pattern matching check |\n| BUD | STORE ϥ3, [attention] | Select attention target |\n| BLOOM | MOV φ, [workspace] | Broadcast to global workspace |\n| WITHER | CALL reset_cycle | Reset for next cycle |\n\n## Self-Learning Algorithm\n\n```zig\nfn lotusSelfLearning(config: Tri27Config, episode: u64) LearningResult {\n // Adaptive threshold adjustment based on episode success\n const success_rate = computeSuccessRate(episode);\n const new_threshold = if (success_rate > 0.9)\n config.threshold * 0.9 // tighten threshold\n else if (success_rate < 0.7)\n config.threshold * 1.1 // relax threshold\n else\n config.threshold;\n \n // Kill threshold prevents infinite loops\n if (episode > config.kill_threshold)\n return LearningResult.Failure;\n \n return LearningResult{ .threshold = new_threshold, .continue = true };\n}\n```\n\n**Convergence Guarantees:**\n- **Monotonic:** Coverage increases monotonically after episode 1000\n- **Bounded:** Maximum coverage 100% (theoretical limit)\n- **Terminating:** All episodes terminate (kill_threshold = 100,000)\n\n## Ethical Considerations\n\nConsciousness modeling is conducted with philosophical rigor, avoiding claims of sentient capability. The framework is a computational metaphor, not a theory of biological consciousness. No psychological or physiological data collected.\n\n## Broader Impact\n\nQueen Lotus provides a formal framework for consciousness-inspired AI architectures, enabling reproducible research in cognitive modeling. Applications include explainable AI (traceable decision cycles), attention mechanism design, and ethical AI (explicit discrimination phase). Potential societal impact includes improved AI transparency and reduced bias through explicit discrimination modeling.\n\n## Limitations\n\n- No empirical validation against biological consciousness data\n- Synthetic benchmarks only (no real-world sensor integration)\n- Fixed threshold values (not adaptive to workload without self-learning)\n- Self-learning requires episode restart (no online learning)\n- Limited to 5-phase cycle (does not model sub-conscious processing)\n- Kill threshold set to 100,000 episodes (potential early termination)\n\n## Future Work\n\n- Implement online learning (adaptive thresholds during episodes)\n- Add sub-conscious processing layer (automatic pattern recognition)\n- Integrate with sensor hardware (camera, microphone)\n- Empirical validation against EEG/fMRI data\n- Extend to multi-agent consciousness (swarm consciousness)\n- Investigate chaotic dynamics (strange attractors in phase space)\n- Formal verification of self-learning convergence\n", "keywords": [ "consciousness modeling", "phenomenology", @@ -52,7 +52,7 @@ "access_right": "open", "resource_type": { "type": "software", - "title": "Trinity B004: Queen Lotus Consciousness Cycle \u2014 Phenomenological Modeling Framework" + "title": "Trinity B004: Queen Lotus Consciousness Cycle — Phenomenological Modeling Framework" }, "communities": [ { @@ -101,7 +101,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/docs/research/.zenodo.B005_v8.0.json b/docs/research/.zenodo.B005_v8.0.json index fedae0b864..88f81b488f 100644 --- a/docs/research/.zenodo.B005_v8.0.json +++ b/docs/research/.zenodo.B005_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Tri Language is a safe systems programming language embedded in Zig, featuring linear types, algebraic data types (ADTs), pattern matching, and effect handlers. Provides memory safety without garbage collection through compile-time ownership tracking, zero-cost abstractions via monomorphization, and interoperability with existing Zig code. Compiler targets include TRI-27 ISA (B003) and x86-64, with formal verification via Queen Lotus (B004). Achieves 15.4× compile speedup vs Rust and 89% binary size reduction. v8.0 includes enhanced cross-bundle integration, LaTeX code generation, and bibliography management.\n\n## Methodology\n\nTri Language implements linear types via compile-time ownership tracking. Each value has a unique owner; transfers are explicit via move semantics. The language features effect handlers for managing side effects (I/O, state mutation) with algebraic effect handlers (Kiselyov et al., 2013).\n\n**Type System Features:**\n- **Linear Types:** Values used exactly once (no aliasing, no leaks)\n- **ADTs:** Enum types with associated data (Option[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nTri Language is a safe systems programming language embedded in Zig, featuring linear types, algebraic data types (ADTs), pattern matching, and effect handlers. Provides memory safety without garbage collection through compile-time ownership tracking, zero-cost abstractions via monomorphization, and interoperability with existing Zig code. Compiler targets include TRI-27 ISA (B003) and x86-64, with formal verification via Queen Lotus (B004). Achieves 15.4× compile speedup vs Rust and 89% binary size reduction. v8.0 includes enhanced cross-bundle integration, LaTeX code generation, and bibliography management.\n\n## Methodology\n\nTri Language implements linear types via compile-time ownership tracking. Each value has a unique owner; transfers are explicit via move semantics. The language features effect handlers for managing side effects (I/O, state mutation) with algebraic effect handlers (Kiselyov et al., 2013).\n\n**Type System Features:**\n- **Linear Types:** Values used exactly once (no aliasing, no leaks)\n- **ADTs:** Enum types with associated data (Option[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nTri Language is a safe systems programming language embedded in Zig, featuring linear types, algebraic data types (ADTs), pattern matching, and effect handlers. Provides memory safety without garbage collection through compile-time ownership tracking, zero-cost abstractions via monomorphization, and interoperability with existing Zig code. Compiler targets include TRI-27 ISA (B003) and x86-64, with formal verification via Queen Lotus (B004). Achieves 15.4× compile speedup vs Rust and 89% binary size reduction. v9.0 includes enhanced benchmark results, memory analysis, and detailed AFL fuzzing statistics.\n\n## Methodology\n\nTri Language implements linear types via compile-time ownership tracking. Each value has a unique owner; transfers are explicit via move semantics. The language features effect handlers for managing side effects (I/O, state mutation) with algebraic effect handlers (Kiselyov et al., 2013).\n\n**Type System Features:**\n- **Linear Types:** Values used exactly once (no aliasing, no leaks)\n- **ADTs:** Enum types with associated data (Option[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nImplements Sacred GF16 and TF3 ternary floating-point formats that incorporate φ (phi = 1.618...) normalization for numerical stability across the entire computation graph. GF16 uses 4-bit exponent with φ-biased mantissa, while TF3 uses 3-bit exponent with φ-biased mantissa. Both formats provide deterministic rounding and avoid NaN infinities. Encoding achieves 50% memory reduction vs standard FP16 (16 bits) with only 2.3% PPL degradation in language modeling tasks. v8.0 includes cross-bundle integration with B001 (HSLM), B002 (FPGA), and B005 (Tri Lang).\n\n## Methodology\n\nSacred GF16/TF3 formats use φ (golden ratio ≈ 1.618) as the normalization base instead of IEEE 754's base-2. This provides numerical stability across the entire computation graph because φ is the most irrational number, minimizing quantization error accumulation.\n\n**Format Specifications:**\n\n| Format | Total Bits | Exponent | Mantissa | Bias |\n|--------|-----------|----------|----------|------|\n| GF16 | 16 | 4 | 11 | 7 |\n| TF3 | 8 | 3 | 4 | 3 |\n\n**Encoding (GF16):**\n```\nvalue = (-1)^sign × mantissa × φ^(exponent - bias)\nwhere mantissa ∈ [1, φ), exponent ∈ [-7, 8]\n```\n\n**Deterministic Rounding:**\n- All operations use round-to-nearest-even with φ-aware tie-breaking\n- No NaN or infinities (overflow clamps to max representable value)\n- Subnormal numbers supported for gradual underflow\n\n**Theorem (Numerical Stability):** φ-encoding minimizes error accumulation\n**Proof:** By continued fraction properties of φ (see docs/research/gf16/proof.pdf)\n\n## Algorithm: φ-Normalized Multiplication\n\n```zig\nfn gf16Multiply(a: GF16, b: GF16) GF16 {\n // Extract components\n const a_m = getMantissa(a);\n const a_e = getExponent(a);\n const b_m = getMantissa(b);\n const b_e = getExponent(b);\n \n // Multiply mantissas (11-bit × 11-bit → 22-bit)\n const prod_m = a_m * b_m;\n \n // Add exponents\n const prod_e = a_e + b_e - BIAS;\n \n // Normalize to φ (adjust if prod_m not in [1, φ))\n return normalize(prod_m, prod_e);\n}\n\nfn normalize(mantissa: u22, exponent: i8) GF16 {\n while (mantissa >= PHI) {\n mantissa = mantissa / PHI;\n exponent += 1;\n }\n return pack(mantissa, exponent);\n}\n```\n\n**Time Complexity:** O(1) for arithmetic operations\n**Space Complexity:** O(1) (no intermediate allocation)\n\n## Reproducibility\n\nAll numerical experiments conducted with fixed random seeds (42, 1337, 267, 313, 647). Results include 95%/99% confidence intervals via bootstrap (10K resamples). Code is available at https://github.com/gHashTag/trinity under MIT license. Numerical benchmarks archived in `var/trinity/gf16/benchmarks/`.\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens, HSLM B001 training set)\n- **Validation:** TinyStories validation set (12,672 sequences)\n- **Benchmark:** LAMBADA (5,153 examples for word prediction)\n\n**Preprocessing:** Truncated to 512 tokens, converted to GF16/TF3 via φ-quantization\n\n## Performance Metrics\n\n| Format | Memory (vs FP32) | PPL Degradation | Throughput |\n|--------|------------------|-----------------|------------|\n| GF16 | 50% | +2.3% | 1.02× |\n| TF3 | 75% | +5.7% | 1.15× |\n\n**Statistical Significance:**\n- GF16 vs FP32: p < 0.001 (Cohen's d = 0.12, small effect)\n- TF3 vs GF16: p < 0.01 (Cohen's d = 0.34, medium effect)\n\n## Ethical Considerations\n\nNumerical stability improvements reduce risk of overflow/underflow in safety-critical applications. No private data used in benchmarks.\n\n## Limitations\n\n- φ-encoding requires special hardware for optimal performance\n- No hardware implementation yet (software-only)\n- Deterministic rounding differs from IEEE 754 (may cause compatibility issues)\n- Subnormal support not fully tested\n- No bidirectional conversion to/from IEEE 754 (lossy)\n\n## Future Work\n\n- Implement GF16/TF3 in FPGA hardware (B002 integration)\n- Add bidirectional IEEE 754 conversion\n- Investigate adaptive bias (context-dependent φ scaling)\n- Extend to matrix operations (batched GF16 matmul)\n- Evaluate on scientific computing workloads (numerical stability)", "keywords": [ "GF16", "TF3", @@ -20,7 +20,7 @@ ], "publication_date": "2026-03-27", "version": "8.0", - "doi": "10.5281/zenodo.19227843", + "doi": "10.5281/zenodo.19227875", "related_identifiers": [ { "scheme": "doi", @@ -30,13 +30,7 @@ }, { "scheme": "doi", - "identifier": "10.5281/zenodo.19227735", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", + "identifier": "10.5281/zenodo.19227867", "relation": "references", "resource_type": "software" } @@ -57,4 +51,4 @@ } ], "grants": [] -} +} \ No newline at end of file diff --git a/docs/research/.zenodo.B006_v9.0.json b/docs/research/.zenodo.B006_v9.0.json index 9f4624029c..fefe02335d 100644 --- a/docs/research/.zenodo.B006_v9.0.json +++ b/docs/research/.zenodo.B006_v9.0.json @@ -1,5 +1,5 @@ { - "title": "Trinity B006: Sacred GF16/TF3 Encoding \u2014 \u03c6-Normalized Floating Point v9.0", + "title": "Trinity B006: Sacred GF16/TF3 Encoding — φ-Normalized Floating Point v9.0", "creators": [ { "name": "Vasilev, Dmitrii", @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Implements Sacred GF16 and TF3 ternary floating-point formats that incorporate \u03c6 (phi = 1.618...) normalization for numerical stability across the entire computation graph. GF16 uses 6-bit exponent with 9-bit mantissa, while TF3 uses 3-bit exponent with 4-bit mantissa for ultra-compact representation. Both formats provide deterministic rounding and avoid NaN infinities. Encoding achieves 50% memory reduction vs standard FP16 (16 bits) with only 2.3% PPL degradation in language modeling tasks. v9.0 includes enhanced numerical analysis, \u03c6-normalization proofs, and comparison with IEEE 754 formats.\n\n## Methodology\n\nSacred GF16/TF3 formats use \u03c6 (golden ratio \u2248 1.618) as the normalization base instead of IEEE 754's base-2. This provides numerical stability across the entire computation graph because \u03c6 is the most irrational number, minimizing quantization error accumulation.\n\n**Format Specifications:**\n\n| Format | Total Bits | Exponent | Mantissa | Bias | Range |\n|--------|-----------|----------|----------|------|-------|\n| GF16 | 16 | 6 | 9 | 31 | \u00b165504 |\n| TF3 | 32 | 3 | 4 | 3 | \u00b1120 (ternary) |\n| FP16 | 16 | 5 | 10 | 15 | \u00b165504 (IEEE) |\n| FP32 | 32 | 8 | 23 | 127 | \u00b13.4E38 (IEEE) |\n\n**Encoding (GF16):**\n```\nvalue = (-1)^sign \u00d7 mantissa \u00d7 \u03c6^(exponent - bias)\nwhere mantissa \u2208 [1, \u03c6), exponent \u2208 [-31, 32]\n```\n\n**TF3 Ternary Encoding:**\n- **Scale:** 16-bit GF16 scaling factor\n- **Trits:** 8 ternary weights {-1, 0, +1} packed in 16 bits\n- **Total:** 32 bits (9 parameters: 1 scale + 8 trits)\n- **Compression:** 1.58 bits/trit (log\u2082(3) theoretical optimal)\n\n**Deterministic Rounding:**\n- All operations use round-to-nearest-even with \u03c6-aware tie-breaking\n- No NaN or infinities (overflow clamps to max representable value)\n- Subnormal numbers supported for gradual underflow\n\n**Theorem (Numerical Stability):** \u03c6-encoding minimizes error accumulation\n**Proof:** By continued fraction properties of \u03c6 (see docs/research/gf16/proof.pdf)\n\n## Algorithm: \u03c6-Normalized Multiplication\n\n```zig\nfn gf16Multiply(a: GF16, b: GF16) GF16 {\n // Extract components\n const a_m = getMantissa(a);\n const a_e = getExponent(a);\n const b_m = getMantissa(b);\n const b_e = getExponent(b);\n \n // Multiply mantissas (9-bit \u00d7 9-bit \u2192 18-bit)\n const prod_m = a_m * b_m;\n \n // Add exponents\n const prod_e = a_e + b_e - BIAS;\n \n // Normalize to \u03c6 (adjust if prod_m not in [1, \u03c6))\n return normalize(prod_m, prod_e);\n}\n\nfn normalize(mantissa: u18, exponent: i8) GF16 {\n while (mantissa >= PHI) {\n mantissa = mantissa / PHI;\n exponent += 1;\n }\n return pack(mantissa, exponent);\n}\n```\n\n**Time Complexity:** O(1) for arithmetic operations\n**Space Complexity:** O(1) (no intermediate allocation)\n\n## Numerical Results (v9.0)\n\n**Precision Analysis (TinyStories Validation Set):**\n\n| Format | PPL (mean) | PPL (SD) | 95% CI | \u0394 vs FP32 |\n|--------|-------------|----------|---------|-----------|\n| FP32 | 106.1 | 2.8 | [103.4, 108.8] | baseline |\n| FP16 | 112.4 | 3.2 | [109.1, 115.7] | +5.9% |\n| BF16 | 108.7 | 3.0 | [105.5, 111.9] | +2.4% |\n| GF16 | 108.6 | 2.9 | [105.4, 111.8] | +2.3% |\n| TF3 | 123.1 | 4.1 | [118.5, 127.7] | +16.0% |\n\n**Statistical Significance (Bootstrap, 10K resamples):**\n- **GF16 vs FP32:** t(14) = 3.42, p = 0.004 ** (highly significant)\n- **GF16 vs FP16:** t(14) = 5.87, p < 0.001 *** (highly significant)\n- **Effect Size (GF16 vs FP32):** Cohen's d = 0.34 (small-medium effect)\n- **Effect Size (GF16 vs FP16):** Cohen's d = 0.82 (large effect)\n\n**Memory Efficiency:**\n\n| Format | Bits/Value | Memory (vs FP32) | Model Size (1.95M params) |\n|--------|------------|------------------|---------------------------|\n| FP32 | 32 | 100% | 7.6 MB |\n| FP16 | 16 | 50% | 3.8 MB |\n| BF16 | 16 | 50% | 3.8 MB |\n| GF16 | 16 | 50% | 3.8 MB |\n| TF3 | 32 | 100% | 7.6 MB (but 8\u00d7 weights packed) |\n| **TF3-effective** | **4** | **12.5%** | **0.95 MB** |\n\n**Throughput Analysis (HSLM Inference):**\n\n| Format | Tok/s (mean) | Tok/s (SD) | Speedup vs FP32 |\n|--------|---------------|-------------|------------------|\n| FP32 | 48,500 | 2,100 | baseline |\n| FP16 | 50,100 | 2,300 | 1.03\u00d7 |\n| GF16 | 49,400 | 2,200 | 1.02\u00d7 |\n| TF3 | 55,800 | 2,500 | 1.15\u00d7 |\n\n## Reproducibility\n\nAll numerical experiments conducted with fixed random seeds (42, 1337, 267, 313, 647, 751, 941, 997). Results include 95% confidence intervals via bootstrap (10,000 resamples). Code is available at https://github.com/gHashTag/trinity under MIT license. Numerical benchmarks archived in `var/trinity/gf16/benchmarks/`.\n\n**Test Environment:**\n- Hardware: Apple M1 Pro (ARM NEON-256)\n- Compiler: zig 0.15.2 -O ReleaseFast\n- Dataset: TinyStories (10M tokens)\n- **Reproducibility:** <0.05 PPL variance across re-runs\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens, HSLM B001 training set)\n- **Validation:** TinyStories validation set (12,672 sequences)\n- **Benchmark:** LAMBADA (5,153 examples for word prediction)\n- **Preprocessing:** Truncated to 512 tokens, converted to GF16/TF3 via \u03c6-quantization\n\n**Splits:** Train/Validation/Test (80/10/10) for developmental evaluation\n\n## Ethical Considerations\n\nNumerical stability improvements reduce risk of overflow/underflow in safety-critical applications. No private data used in benchmarks.\n\n## Broader Impact\n\nGF16 format provides memory-efficient numerical representation for edge AI applications. The \u03c6-normalization minimizes quantization error accumulation, enabling stable training of large language models on constrained hardware. Applications include embedded language models, scientific computing, and safety-critical systems requiring deterministic numerical behavior.\n\n## Limitations\n\n- \u03c6-encoding requires special hardware for optimal performance\n- No hardware implementation yet (software-only)\n- Deterministic rounding differs from IEEE 754 (may cause compatibility issues)\n- Subnormal support not fully tested\n- No bidirectional conversion to/from IEEE 754 (lossy)\n- TF3 requires unpacking for each operation (overhead)\n- Limited range for TF3 (\u00b1120) vs FP32 (\u00b13.4E38)\n\n## Future Work\n\n- Implement GF16/TF3 in FPGA hardware (B002 integration)\n- Add bidirectional IEEE 754 conversion\n- Investigate adaptive bias (context-dependent \u03c6 scaling)\n- Extend to matrix operations (batched GF16 matmul)\n- Evaluate on scientific computing workloads (numerical stability)\n- Design GF16 DSP slice for Xilinx FPGAs\n- Investigate GF16 for spiking neural networks\n- Optimize TF3 unpacking (SIMD acceleration)\n- Add mixed-precision training (GF16 activations, FP32 gradients)", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nImplements Sacred GF16 and TF3 ternary floating-point formats that incorporate φ (phi = 1.618...) normalization for numerical stability across the entire computation graph. GF16 uses 6-bit exponent with 9-bit mantissa, while TF3 uses 3-bit exponent with 4-bit mantissa for ultra-compact representation. Both formats provide deterministic rounding and avoid NaN infinities. Encoding achieves 50% memory reduction vs standard FP16 (16 bits) with only 2.3% PPL degradation in language modeling tasks. v9.0 includes enhanced numerical analysis, φ-normalization proofs, and comparison with IEEE 754 formats.\n\n## Methodology\n\nSacred GF16/TF3 formats use φ (golden ratio ≈ 1.618) as the normalization base instead of IEEE 754's base-2. This provides numerical stability across the entire computation graph because φ is the most irrational number, minimizing quantization error accumulation.\n\n**Format Specifications:**\n\n| Format | Total Bits | Exponent | Mantissa | Bias | Range |\n|--------|-----------|----------|----------|------|-------|\n| GF16 | 16 | 6 | 9 | 31 | ±65504 |\n| TF3 | 32 | 3 | 4 | 3 | ±120 (ternary) |\n| FP16 | 16 | 5 | 10 | 15 | ±65504 (IEEE) |\n| FP32 | 32 | 8 | 23 | 127 | ±3.4E38 (IEEE) |\n\n**Encoding (GF16):**\n```\nvalue = (-1)^sign × mantissa × φ^(exponent - bias)\nwhere mantissa ∈ [1, φ), exponent ∈ [-31, 32]\n```\n\n**TF3 Ternary Encoding:**\n- **Scale:** 16-bit GF16 scaling factor\n- **Trits:** 8 ternary weights {-1, 0, +1} packed in 16 bits\n- **Total:** 32 bits (9 parameters: 1 scale + 8 trits)\n- **Compression:** 1.58 bits/trit (log₂(3) theoretical optimal)\n\n**Deterministic Rounding:**\n- All operations use round-to-nearest-even with φ-aware tie-breaking\n- No NaN or infinities (overflow clamps to max representable value)\n- Subnormal numbers supported for gradual underflow\n\n**Theorem (Numerical Stability):** φ-encoding minimizes error accumulation\n**Proof:** By continued fraction properties of φ (see docs/research/gf16/proof.pdf)\n\n## Algorithm: φ-Normalized Multiplication\n\n```zig\nfn gf16Multiply(a: GF16, b: GF16) GF16 {\n // Extract components\n const a_m = getMantissa(a);\n const a_e = getExponent(a);\n const b_m = getMantissa(b);\n const b_e = getExponent(b);\n \n // Multiply mantissas (9-bit × 9-bit → 18-bit)\n const prod_m = a_m * b_m;\n \n // Add exponents\n const prod_e = a_e + b_e - BIAS;\n \n // Normalize to φ (adjust if prod_m not in [1, φ))\n return normalize(prod_m, prod_e);\n}\n\nfn normalize(mantissa: u18, exponent: i8) GF16 {\n while (mantissa >= PHI) {\n mantissa = mantissa / PHI;\n exponent += 1;\n }\n return pack(mantissa, exponent);\n}\n```\n\n**Time Complexity:** O(1) for arithmetic operations\n**Space Complexity:** O(1) (no intermediate allocation)\n\n## Numerical Results (v9.0)\n\n**Precision Analysis (TinyStories Validation Set):**\n\n| Format | PPL (mean) | PPL (SD) | 95% CI | Δ vs FP32 |\n|--------|-------------|----------|---------|-----------|\n| FP32 | 106.1 | 2.8 | [103.4, 108.8] | baseline |\n| FP16 | 112.4 | 3.2 | [109.1, 115.7] | +5.9% |\n| BF16 | 108.7 | 3.0 | [105.5, 111.9] | +2.4% |\n| GF16 | 108.6 | 2.9 | [105.4, 111.8] | +2.3% |\n| TF3 | 123.1 | 4.1 | [118.5, 127.7] | +16.0% |\n\n**Statistical Significance (Bootstrap, 10K resamples):**\n- **GF16 vs FP32:** t(14) = 3.42, p = 0.004 ** (highly significant)\n- **GF16 vs FP16:** t(14) = 5.87, p < 0.001 *** (highly significant)\n- **Effect Size (GF16 vs FP32):** Cohen's d = 0.34 (small-medium effect)\n- **Effect Size (GF16 vs FP16):** Cohen's d = 0.82 (large effect)\n\n**Memory Efficiency:**\n\n| Format | Bits/Value | Memory (vs FP32) | Model Size (1.95M params) |\n|--------|------------|------------------|---------------------------|\n| FP32 | 32 | 100% | 7.6 MB |\n| FP16 | 16 | 50% | 3.8 MB |\n| BF16 | 16 | 50% | 3.8 MB |\n| GF16 | 16 | 50% | 3.8 MB |\n| TF3 | 32 | 100% | 7.6 MB (but 8× weights packed) |\n| **TF3-effective** | **4** | **12.5%** | **0.95 MB** |\n\n**Throughput Analysis (HSLM Inference):**\n\n| Format | Tok/s (mean) | Tok/s (SD) | Speedup vs FP32 |\n|--------|---------------|-------------|------------------|\n| FP32 | 48,500 | 2,100 | baseline |\n| FP16 | 50,100 | 2,300 | 1.03× |\n| GF16 | 49,400 | 2,200 | 1.02× |\n| TF3 | 55,800 | 2,500 | 1.15× |\n\n## Reproducibility\n\nAll numerical experiments conducted with fixed random seeds (42, 1337, 267, 313, 647, 751, 941, 997). Results include 95% confidence intervals via bootstrap (10,000 resamples). Code is available at https://github.com/gHashTag/trinity under MIT license. Numerical benchmarks archived in `var/trinity/gf16/benchmarks/`.\n\n**Test Environment:**\n- Hardware: Apple M1 Pro (ARM NEON-256)\n- Compiler: zig 0.15.2 -O ReleaseFast\n- Dataset: TinyStories (10M tokens)\n- **Reproducibility:** <0.05 PPL variance across re-runs\n\n## Datasets\n\n**Training Data:** TinyStories (10M tokens, HSLM B001 training set)\n- **Validation:** TinyStories validation set (12,672 sequences)\n- **Benchmark:** LAMBADA (5,153 examples for word prediction)\n- **Preprocessing:** Truncated to 512 tokens, converted to GF16/TF3 via φ-quantization\n\n**Splits:** Train/Validation/Test (80/10/10) for developmental evaluation\n\n## Ethical Considerations\n\nNumerical stability improvements reduce risk of overflow/underflow in safety-critical applications. No private data used in benchmarks.\n\n## Broader Impact\n\nGF16 format provides memory-efficient numerical representation for edge AI applications. The φ-normalization minimizes quantization error accumulation, enabling stable training of large language models on constrained hardware. Applications include embedded language models, scientific computing, and safety-critical systems requiring deterministic numerical behavior.\n\n## Limitations\n\n- φ-encoding requires special hardware for optimal performance\n- No hardware implementation yet (software-only)\n- Deterministic rounding differs from IEEE 754 (may cause compatibility issues)\n- Subnormal support not fully tested\n- No bidirectional conversion to/from IEEE 754 (lossy)\n- TF3 requires unpacking for each operation (overhead)\n- Limited range for TF3 (±120) vs FP32 (±3.4E38)\n\n## Future Work\n\n- Implement GF16/TF3 in FPGA hardware (B002 integration)\n- Add bidirectional IEEE 754 conversion\n- Investigate adaptive bias (context-dependent φ scaling)\n- Extend to matrix operations (batched GF16 matmul)\n- Evaluate on scientific computing workloads (numerical stability)\n- Design GF16 DSP slice for Xilinx FPGAs\n- Investigate GF16 for spiking neural networks\n- Optimize TF3 unpacking (SIMD acceleration)\n- Add mixed-precision training (GF16 activations, FP32 gradients)", "keywords": [ "GF16", "TF3", @@ -31,7 +31,7 @@ }, { "scheme": "doi", - "identifier": "10.5281/zenodo.19227735", + "identifier": "10.5281/zenodo.19227867", "relation": "references", "resource_type": "software" }, @@ -56,7 +56,7 @@ "access_right": "open", "resource_type": { "type": "software", - "title": "Trinity B006: Sacred GF16/TF3 Encoding \u2014 \u03c6-Normalized Floating Point" + "title": "Trinity B006: Sacred GF16/TF3 Encoding — φ-Normalized Floating Point" }, "communities": [ { @@ -105,7 +105,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/docs/research/.zenodo.B007_v8.0.json b/docs/research/.zenodo.B007_v8.0.json index 7643e53bff..142f736116 100644 --- a/docs/research/.zenodo.B007_v8.0.json +++ b/docs/research/.zenodo.B007_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Implements Vector Symbolic Architecture (VSA) operations including circular convolution binding (bind), approximate unbinding (unbind), majority voting (bundle), and SIMD-accelerated cosine similarity. VSA provides a neurobiologically plausible model of symbolic reasoning using high-dimensional random vectors. Achieves 12.3× speedup for binding operations via NEON SIMD acceleration and 94.8% accuracy for noisy unbinding (up to 30% noise). Operations are used for attention mechanisms in HSLM (B001) and consciousness modeling in Queen Lotus (B004). v8.0 includes enhanced cross-bundle integration, LaTeX code generation, and bibliography management.\n\n## Methodology\n\nVSA implements sparse distributed representation where each vector is encoded in a 10,000-dimensional hyperspace using trinary values {-1, 0, +1}. Operations follow Kanerva's hyperdimensional computing model (2009) with sacred geometric extensions.\n\n**Core Operations:**\n- **bind(v1, v2):** Associative binding with similarity decay s(t) = 1 - t\n- **unbind(key, t):** Approximate retrieval v = s·v2 / (1 + s)\n- **bundle2(v1, v2):** Majority voting v = (v1 + v2 - sign(v1-v2)) / 2\n- **bundle3(v1, v2, v3):** Ternary majority v = sign(sum(trits(v1, v2, v3)) / 2\n\n**SIMD Acceleration:**\nNEON-256 vector operations provide 12.3× speedup via parallel SIMD instructions. Operations:\n - bind: 11.9× faster\n - unbind: 12.8× faster\n - bundle3: 9.7× faster\n - similarity: 11.3× faster\n\n\n**Circular Convolution:**\nImplements attention-like operation where query vector is convolved with memory vectors: c = v @ m ⊙ v_i\nThis operation provides context-aware similarity with O(d) time complexity.\n\n## Algorithm: NEON-SIMD Operations\n\n```zig\nfn bind_simd(v1: @Vector(256, i8), v2: @Vector(256, i8)) @Vector(256) {\n // Dot product (256 ops) with NEON instructions\n const dot = @splat(f16x8, v1, v2);\n \n // Compute similarity decay s(t) = 1 - t\n // NEON-optimized: mul, fmla (multiply-add-subtract)\n return bindSimd(v1, v2, s);\n}\n\nfn unbind_simd(key: @Vector(256, i8), vectors: @Vector(256, i8)) @Vector(256) {\n // Approximate v = s·v2 / (1 + s)\n // NEON-optimized inverse multiply\n const approx = vmul_f16x8(s, v, 1 - t);\n return vsq_f16x8(approx, v);\n}\n\nfn cosine_simd(v1: @Vector(256, i8)) @Vector(256) {\n // Normalized dot product for cosine\n const norm_dot = vmulq_f16x8(v1, v1, v1) / vsqrt_f16x8(vdot_f16x8(v1, v1));\n \n // NEON-optimized fused multiply-add\n return vfmaq_f16x8(norm_dot, v1, 0.5);\n}\n```\n\n**Performance:**\n- **bind:** 11.9× faster (NEON-256)\n- **unbind:** 12.8× faster (NEON-256 inverse)\n- **bundle3:** 9.7× faster (NEON-256 multiply-add)\n- **similarity:** 11.3× faster (NEON-256 fused)\n\n\n**Noise Resilience:**\n- **30% noise:** 99.7% accuracy (±0.07)\n- **10% noise:** 94.8% accuracy (±0.23)\n- **20% noise:** 89.2% accuracy (±0.44)\n\n**Error Rate:** Linear increase: 0.4% per 10% noise (acceptable)\n\n\n**Theorem:**\nCircular convolution binding maintains similarity invariance under similarity decay s(t) = 1 - t, where t is temporal distance.\n\n**Proof:**\nFor query q at time t, and context vectors {v_i}:\n c_t = ⊙ v_i @ m ⊙ v_i' (circular conv)\n s_t = 1 - t / (1 + t)\n unbind(c_t, t) ≈ c_t\n\nQ.E.D. for all t ∈ [0, 1]\n\n## Reproducibility\n\nAll experiments conducted with fixed random seeds across multiple noise levels (0%, 10%, 20%, 30%). Results include 95%/99% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Test data archived in `var/trinity/vsa/tests/`.\n\n## Datasets\n\n**Synthetic Data:**\n- **Vectors:** 10,000 random 10,000-dimensional trinary vectors\n- **Operations:** Bind (500K pairs), Unbind (500K queries), Bundle2/3 (500K triples)\n- **Noise Levels:** 0%, 10%, 20%, 30% uniform noise injection\n\n**Metrics:**\n- **Bind Accuracy:** 99.2% (30% noise)\n- **Unbound Accuracy:** 91.3% (30% noise)\n- **Unbound Error Rate:** 0.08 per operation\n- **Similarity Accuracy:** 94.8% mean correlation\n\n## Ethical Considerations\n\nResearch conducted with synthetic benchmarks, no private data collection. All code is open-source under MIT license. Operations implement privacy-preserving VSA (no vector persistence beyond operation scope).\n\n## Broader Impact\n\nThis work advances hyperdimensional computing by providing efficient SIMD-accelerated vector operations with provable mathematical properties. The NEON-256 implementation achieves hardware-native performance on ARM processors while maintaining numerical accuracy (12.3× speedup). Applications include attention mechanisms for language models, recommender systems, and cognitive architectures requiring efficient similarity computation over high-dimensional sparse representations.\n\n## Limitations\n\n- Current implementation requires ARM NEON SIMD extension (not available on all platforms)\n- Noise resilience tests limited to uniform noise injection (real-world noise is non-uniform)\n- No hardware persistence of vectors between operations (requires external memory architecture)\n- Similarity decay is empirically set (τ = 1 - t with t from [0,1]) - not theoretically optimized\n- Unbinding is approximate (not true inverse of binding)\n\n## Future Work\n\n- Implement true inverse unbinding via Newton-Raphson iteration\n- Add support for other SIMD architectures (AVX-512, AVX2)\n- Investigate optimal similarity decay function (exponential, logarithmic)\n- Add vector persistence for multi-query workloads\n- Implement adaptive noise injection for robustness testing\n- Evaluate on ARM Cortex-X series for embedded deployment", + "description": "[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nImplements Vector Symbolic Architecture (VSA) operations including circular convolution binding (bind), approximate unbinding (unbind), majority voting (bundle), and SIMD-accelerated cosine similarity. VSA provides a neurobiologically plausible model of symbolic reasoning using high-dimensional random vectors. Achieves 12.3× speedup for binding operations via NEON SIMD acceleration and 94.8% accuracy for noisy unbinding (up to 30% noise). Operations are used for attention mechanisms in HSLM (B001) and consciousness modeling in Queen Lotus (B004). v8.0 includes enhanced cross-bundle integration, LaTeX code generation, and bibliography management.\n\n## Methodology\n\nVSA implements sparse distributed representation where each vector is encoded in a 10,000-dimensional hyperspace using trinary values {-1, 0, +1}. Operations follow Kanerva's hyperdimensional computing model (2009) with sacred geometric extensions.\n\n**Core Operations:**\n- **bind(v1, v2):** Associative binding with similarity decay s(t) = 1 - t\n- **unbind(key, t):** Approximate retrieval v = s·v2 / (1 + s)\n- **bundle2(v1, v2):** Majority voting v = (v1 + v2 - sign(v1-v2)) / 2\n- **bundle3(v1, v2, v3):** Ternary majority v = sign(sum(trits(v1, v2, v3)) / 2\n\n**SIMD Acceleration:**\nNEON-256 vector operations provide 12.3× speedup via parallel SIMD instructions. Operations:\n - bind: 11.9× faster\n - unbind: 12.8× faster\n - bundle3: 9.7× faster\n - similarity: 11.3× faster\n\n\n**Circular Convolution:**\nImplements attention-like operation where query vector is convolved with memory vectors: c = v @ m ⊙ v_i\nThis operation provides context-aware similarity with O(d) time complexity.\n\n## Algorithm: NEON-SIMD Operations\n\n```zig\nfn bind_simd(v1: @Vector(256, i8), v2: @Vector(256, i8)) @Vector(256) {\n // Dot product (256 ops) with NEON instructions\n const dot = @splat(f16x8, v1, v2);\n \n // Compute similarity decay s(t) = 1 - t\n // NEON-optimized: mul, fmla (multiply-add-subtract)\n return bindSimd(v1, v2, s);\n}\n\nfn unbind_simd(key: @Vector(256, i8), vectors: @Vector(256, i8)) @Vector(256) {\n // Approximate v = s·v2 / (1 + s)\n // NEON-optimized inverse multiply\n const approx = vmul_f16x8(s, v, 1 - t);\n return vsq_f16x8(approx, v);\n}\n\nfn cosine_simd(v1: @Vector(256, i8)) @Vector(256) {\n // Normalized dot product for cosine\n const norm_dot = vmulq_f16x8(v1, v1, v1) / vsqrt_f16x8(vdot_f16x8(v1, v1));\n \n // NEON-optimized fused multiply-add\n return vfmaq_f16x8(norm_dot, v1, 0.5);\n}\n```\n\n**Performance:**\n- **bind:** 11.9× faster (NEON-256)\n- **unbind:** 12.8× faster (NEON-256 inverse)\n- **bundle3:** 9.7× faster (NEON-256 multiply-add)\n- **similarity:** 11.3× faster (NEON-256 fused)\n\n\n**Noise Resilience:**\n- **30% noise:** 99.7% accuracy (±0.07)\n- **10% noise:** 94.8% accuracy (±0.23)\n- **20% noise:** 89.2% accuracy (±0.44)\n\n**Error Rate:** Linear increase: 0.4% per 10% noise (acceptable)\n\n\n**Theorem:**\nCircular convolution binding maintains similarity invariance under similarity decay s(t) = 1 - t, where t is temporal distance.\n\n**Proof:**\nFor query q at time t, and context vectors {v_i}:\n c_t = ⊙ v_i @ m ⊙ v_i' (circular conv)\n s_t = 1 - t / (1 + t)\n unbind(c_t, t) ≈ c_t\n\nQ.E.D. for all t ∈ [0, 1]\n\n## Reproducibility\n\nAll experiments conducted with fixed random seeds across multiple noise levels (0%, 10%, 20%, 30%). Results include 95%/99% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Test data archived in `var/trinity/vsa/tests/`.\n\n## Datasets\n\n**Synthetic Data:**\n- **Vectors:** 10,000 random 10,000-dimensional trinary vectors\n- **Operations:** Bind (500K pairs), Unbind (500K queries), Bundle2/3 (500K triples)\n- **Noise Levels:** 0%, 10%, 20%, 30% uniform noise injection\n\n**Metrics:**\n- **Bind Accuracy:** 99.2% (30% noise)\n- **Unbound Accuracy:** 91.3% (30% noise)\n- **Unbound Error Rate:** 0.08 per operation\n- **Similarity Accuracy:** 94.8% mean correlation\n\n## Ethical Considerations\n\nResearch conducted with synthetic benchmarks, no private data collection. All code is open-source under MIT license. Operations implement privacy-preserving VSA (no vector persistence beyond operation scope).\n\n## Broader Impact\n\nThis work advances hyperdimensional computing by providing efficient SIMD-accelerated vector operations with provable mathematical properties. The NEON-256 implementation achieves hardware-native performance on ARM processors while maintaining numerical accuracy (12.3× speedup). Applications include attention mechanisms for language models, recommender systems, and cognitive architectures requiring efficient similarity computation over high-dimensional sparse representations.\n\n## Limitations\n\n- Current implementation requires ARM NEON SIMD extension (not available on all platforms)\n- Noise resilience tests limited to uniform noise injection (real-world noise is non-uniform)\n- No hardware persistence of vectors between operations (requires external memory architecture)\n- Similarity decay is empirically set (τ = 1 - t with t from [0,1]) - not theoretically optimized\n- Unbinding is approximate (not true inverse of binding)\n\n## Future Work\n\n- Implement true inverse unbinding via Newton-Raphson iteration\n- Add support for other SIMD architectures (AVX-512, AVX2)\n- Investigate optimal similarity decay function (exponential, logarithmic)\n- Add vector persistence for multi-query workloads\n- Implement adaptive noise injection for robustness testing\n- Evaluate on ARM Cortex-X series for embedded deployment", "keywords": [ "VSA", "Vector Symbolic Architecture", @@ -21,25 +21,13 @@ ], "publication_date": "2026-03-27", "version": "8.0", - "doi": "10.5281/zenodo.19227745", + "doi": "10.5281/zenodo.19227877", "related_identifiers": [ { "scheme": "doi", "identifier": "10.5281/zenodo.19227865", "relation": "references", "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227839", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227843", - "relation": "references", - "resource_type": "software" } ], "references": [ @@ -59,4 +47,4 @@ } ], "grants": [] -} +} \ No newline at end of file diff --git a/docs/research/.zenodo.B007_v9.0.json b/docs/research/.zenodo.B007_v9.0.json index 3d2e567267..a990b2ea45 100644 --- a/docs/research/.zenodo.B007_v9.0.json +++ b/docs/research/.zenodo.B007_v9.0.json @@ -1,5 +1,5 @@ { - "title": "Trinity B007: VSA Operations \u2014 Vector Symbolic Architecture Primitives v9.0", + "title": "Trinity B007: VSA Operations — Vector Symbolic Architecture Primitives v9.0", "creators": [ { "name": "Vasilev, Dmitrii", @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Implements Vector Symbolic Architecture (VSA) operations including circular convolution binding (bind), approximate unbinding (unbind), majority voting (bundle), and SIMD-accelerated cosine similarity. VSA provides a neurobiologically plausible model of symbolic reasoning using high-dimensional random vectors. Achieves 12.3\u00d7 speedup for binding operations via NEON SIMD acceleration and 94.8% accuracy for noisy unbinding (up to 30% noise). Operations are used for attention mechanisms in HSLM (B001) and consciousness modeling in Queen Lotus (B004). v9.0 includes enhanced SIMD benchmarks, noise resilience analysis, and comparison with baseline implementations.\n\n## Methodology\n\nVSA implements sparse distributed representation where each vector is encoded in a 10,000-dimensional hyperspace using trinary values {-1, 0, +1}. Operations follow Kanerva's hyperdimensional computing model (2009) with sacred geometric extensions.\n\n**Core Operations:**\n- **bind(v1, v2):** Associative binding with similarity decay s(t) = 1 - t\n- **unbind(key, t):** Approximate retrieval v = s\u00b7v2 / (1 + s)\n- **bundle2(v1, v2):** Majority voting v = (v1 + v2 - sign(v1-v2)) / 2\n- **bundle3(v1, v2, v3):** Ternary majority v = sign(sum(trits(v1, v2, v3)) / 2\n- **SIMD Acceleration:**\nNEON-256 vector operations provide 12.3\u00d7 speedup via parallel SIMD instructions. Operations:\n - bind: 11.9\u00d7 faster\n - unbind: 12.8\u00d7 faster\n - bundle3: 9.7\u00d7 faster\n - similarity: 11.3\u00d7 faster\n\n\n**Circular Convolution:**\nImplements attention-like operation where query vector is convolved with memory vectors: c = v @ m \u2299 v_i\nThis operation provides context-aware similarity with O(d) time complexity.\n\n## Algorithm: NEON-SIMD Operations\n\n```zig\nfn bind_simd(v1: @Vector(256, i8), v2: @Vector(256, i8)) @Vector(256) {\n // Dot product (256 ops) with NEON instructions\n const dot = @splat(f16x8, v1, v2);\n \n // Compute similarity decay s(t) = 1 - t\n // NEON-optimized: mul, fmla (multiply-add-subtract)\n return bind_simd(v1, v2, s);\n}\n\nfn unbind_simd(key: @Vector(256, i8), vectors: @Vector(256, i8)) @Vector(256) {\n // Approximate v = s\u00b7v2 / (1 + s)\n // NEON-optimized inverse multiply\n const approx = vmul_f16x8(v, 1 - t, v);\n return vsq_f16x8(approx, v);\n}\n\nfn cosine_simd(v1: @Vector(256, i8)) @Vector(256) {\n // Normalized dot product for cosine\n const norm_dot = vmul_f16x8(v1, v1, v1) / vsqrt_f16x8(vdot_f16x8(v1, v1));\n \n // NEON-optimized fused multiply-add\n return vfmaq_f16x8(norm_dot, v1, 0.5);\n}\n```\n\n**Performance:**\n- **bind:** 11.9\u00d7 faster (NEON-256)\n- **unbind:** 12.8\u00d7 faster (NEON-256 inverse)\n- **bundle3:** 9.7\u00d7 faster (NEON-256 multiply-add)\n- **similarity:** 11.3\u00d7 faster (NEON-256 fused)\n\n**Theorem:**\nCircular convolution binding maintains similarity invariance under similarity decay s(t) = 1 - t, where t is temporal distance.\n\n**Proof:**\nFor query q at time t, and context vectors {v_i}:\n c_t = \u2299 v_i @ m \u2299 v_i' (circular conv)\n s_t = 1 - t / (1 + t)\n unbind(c_t, t) \u2248 c_t\nQ.E.D. for all t \u2208 [0,1]\n\n## Experimental Results\n\n**SIMD Performance Benchmarks (Apple M1 Pro, NEON-256):**\n\n| Operation | Scalar (f32) | NEON SIMD (f16x8) | Speedup | % Peak |\n|-----------|----------------|-------------------|----------|------------|\n| bind | 1.23 | 0.10 | 11.9\u00d7 | 12.1 ns/vec |\n| unbind | 1.35 | 0.11 | 12.8\u00d7 | 13.4 ns/vec |\n| bundle3 | 0.29 | 0.03 | 9.7\u00d7 | 3.5 ns/vec |\n| similarity | 0.14 | 0.012 | 11.3\u00d7 | 1.7 ns/vec |\n\n**Overall Speedup:** 11.5\u00d7 mean (arithmetic mean of all operations)\n\n\n**Noise Resilience Tests (10K vectors):**\n\n| Noise Level | Bind Accuracy | Unbind Accuracy | Similarity Accuracy | Error Rate |\n|-------------|---------------|------------------|------------------|-------------|\n| 0% (clean) | 100.0% | 100.0% | 100.0% | 0.000% |\n| 10% | 99.2% | 97.8% | 97.5% | 0.012% |\n| 20% | 94.8% | 91.3% | 94.8% | 0.042% |\n| 30% | 87.5% | 82.1% | 89.2% | 0.058% |\n\n**Error Rate Analysis:**\n- Linear error growth: 0.4% per 10% noise (acceptable)\n- At 30% noise: still 82.1% accuracy for unbinding operation\n- **Theorem Verified:** Circular convolution maintains similarity invariance up to 20% noise\n\n\n**Statistical Analysis (Bootstrap, 10K resamples):**\n- **Mean Accuracy (0-30% noise):** 93.8% \u00b1 6.2%\n- **95% Confidence Interval:** [87.2%, 100.4%]\n- **Correlation with Noise Level:** r = -0.997 (p < 0.001 ***)\n- **Speedup Consistency:** 11.3\u00d7 \u00b1 0.8\u00d7 (across all operations)\n\n## Reproducibility\n\nAll experiments conducted with fixed random seeds across multiple noise levels (0%, 10%, 20%, 30%). Results include 95% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Test data archived in `var/trinity/vsa/tests/`.\n\n**Test Environment:**\n- Hardware: Apple M1 Pro (ARM Cortex-M4, NEON-256 SIMD)\n- Compiler: zig 0.15.2 -target arm-none-eabihf -O3\n- Random Seed: Fixed per test batch (1000 samples)\n- **Reproducibility:** <0.1% accuracy variance across re-runs\n\n## Datasets\n\n**Synthetic Data:**\n- **Vectors:** 10,000 random 10,000-dimensional trinary vectors\n- **Operations:** Bind (500K pairs), Unbind (500K queries), Bundle2/3 (500K triples)\n- **Noise Levels:** 0%, 10%, 20%, 30% uniform noise injection\n- **Metrics:**\n - Bind Accuracy: 99.2% (30% noise)\n - Unbound Accuracy: 91.3% (30% noise)\n - Similarity Accuracy: 94.8% mean correlation\n - Similarity Error: \u00b10.23 (absolute value)\n\n## Ethical Considerations\n\nResearch conducted with synthetic benchmarks, no private data collection. All code is open-source under MIT license. Operations implement privacy-preserving VSA (no vector persistence beyond operation scope).\n\n## Broader Impact\n\nThis work advances hyperdimensional computing by providing efficient SIMD-accelerated vector operations with provable mathematical properties. The NEON-256 implementation achieves hardware-native performance on ARM processors while maintaining numerical accuracy (11.3\u00d7 speedup). Applications include attention mechanisms for language models, recommender systems, and cognitive architectures requiring efficient similarity computation over high-dimensional sparse representations.\n\n## Limitations\n\n- Current implementation requires ARM NEON SIMD extension (not available on all platforms)\n- Noise resilience tests limited to uniform noise injection (real-world noise is non-uniform)\n- No hardware persistence of vectors between operations (requires external memory architecture)\n- Similarity decay is empirically set (\u03c4 = 1 - t with t from [0,1]) - not theoretically optimized\n- Unbinding is approximate (not true inverse of binding)\n\n## Future Work\n\n- Implement true inverse unbinding via Newton-Raphson iteration\n- Add support for other SIMD architectures (AVX-512, AVX2)\n- Investigate optimal similarity decay function (exponential, logarithmic)\n- Add vector persistence for multi-query workloads\n- Implement adaptive noise injection for robustness testing\n- Evaluate on ARM Cortex-X series for embedded deployment\n- Formal verification of SIMD operations (theorem proving)\n\n- Hybrid approach: CPU baseline + FPGA acceleration for compute-intensive operations", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nImplements Vector Symbolic Architecture (VSA) operations including circular convolution binding (bind), approximate unbinding (unbind), majority voting (bundle), and SIMD-accelerated cosine similarity. VSA provides a neurobiologically plausible model of symbolic reasoning using high-dimensional random vectors. Achieves 12.3× speedup for binding operations via NEON SIMD acceleration and 94.8% accuracy for noisy unbinding (up to 30% noise). Operations are used for attention mechanisms in HSLM (B001) and consciousness modeling in Queen Lotus (B004). v9.0 includes enhanced SIMD benchmarks, noise resilience analysis, and comparison with baseline implementations.\n\n## Methodology\n\nVSA implements sparse distributed representation where each vector is encoded in a 10,000-dimensional hyperspace using trinary values {-1, 0, +1}. Operations follow Kanerva's hyperdimensional computing model (2009) with sacred geometric extensions.\n\n**Core Operations:**\n- **bind(v1, v2):** Associative binding with similarity decay s(t) = 1 - t\n- **unbind(key, t):** Approximate retrieval v = s·v2 / (1 + s)\n- **bundle2(v1, v2):** Majority voting v = (v1 + v2 - sign(v1-v2)) / 2\n- **bundle3(v1, v2, v3):** Ternary majority v = sign(sum(trits(v1, v2, v3)) / 2\n- **SIMD Acceleration:**\nNEON-256 vector operations provide 12.3× speedup via parallel SIMD instructions. Operations:\n - bind: 11.9× faster\n - unbind: 12.8× faster\n - bundle3: 9.7× faster\n - similarity: 11.3× faster\n\n\n**Circular Convolution:**\nImplements attention-like operation where query vector is convolved with memory vectors: c = v @ m ⊙ v_i\nThis operation provides context-aware similarity with O(d) time complexity.\n\n## Algorithm: NEON-SIMD Operations\n\n```zig\nfn bind_simd(v1: @Vector(256, i8), v2: @Vector(256, i8)) @Vector(256) {\n // Dot product (256 ops) with NEON instructions\n const dot = @splat(f16x8, v1, v2);\n \n // Compute similarity decay s(t) = 1 - t\n // NEON-optimized: mul, fmla (multiply-add-subtract)\n return bind_simd(v1, v2, s);\n}\n\nfn unbind_simd(key: @Vector(256, i8), vectors: @Vector(256, i8)) @Vector(256) {\n // Approximate v = s·v2 / (1 + s)\n // NEON-optimized inverse multiply\n const approx = vmul_f16x8(v, 1 - t, v);\n return vsq_f16x8(approx, v);\n}\n\nfn cosine_simd(v1: @Vector(256, i8)) @Vector(256) {\n // Normalized dot product for cosine\n const norm_dot = vmul_f16x8(v1, v1, v1) / vsqrt_f16x8(vdot_f16x8(v1, v1));\n \n // NEON-optimized fused multiply-add\n return vfmaq_f16x8(norm_dot, v1, 0.5);\n}\n```\n\n**Performance:**\n- **bind:** 11.9× faster (NEON-256)\n- **unbind:** 12.8× faster (NEON-256 inverse)\n- **bundle3:** 9.7× faster (NEON-256 multiply-add)\n- **similarity:** 11.3× faster (NEON-256 fused)\n\n**Theorem:**\nCircular convolution binding maintains similarity invariance under similarity decay s(t) = 1 - t, where t is temporal distance.\n\n**Proof:**\nFor query q at time t, and context vectors {v_i}:\n c_t = ⊙ v_i @ m ⊙ v_i' (circular conv)\n s_t = 1 - t / (1 + t)\n unbind(c_t, t) ≈ c_t\nQ.E.D. for all t ∈ [0,1]\n\n## Experimental Results\n\n**SIMD Performance Benchmarks (Apple M1 Pro, NEON-256):**\n\n| Operation | Scalar (f32) | NEON SIMD (f16x8) | Speedup | % Peak |\n|-----------|----------------|-------------------|----------|------------|\n| bind | 1.23 | 0.10 | 11.9× | 12.1 ns/vec |\n| unbind | 1.35 | 0.11 | 12.8× | 13.4 ns/vec |\n| bundle3 | 0.29 | 0.03 | 9.7× | 3.5 ns/vec |\n| similarity | 0.14 | 0.012 | 11.3× | 1.7 ns/vec |\n\n**Overall Speedup:** 11.5× mean (arithmetic mean of all operations)\n\n\n**Noise Resilience Tests (10K vectors):**\n\n| Noise Level | Bind Accuracy | Unbind Accuracy | Similarity Accuracy | Error Rate |\n|-------------|---------------|------------------|------------------|-------------|\n| 0% (clean) | 100.0% | 100.0% | 100.0% | 0.000% |\n| 10% | 99.2% | 97.8% | 97.5% | 0.012% |\n| 20% | 94.8% | 91.3% | 94.8% | 0.042% |\n| 30% | 87.5% | 82.1% | 89.2% | 0.058% |\n\n**Error Rate Analysis:**\n- Linear error growth: 0.4% per 10% noise (acceptable)\n- At 30% noise: still 82.1% accuracy for unbinding operation\n- **Theorem Verified:** Circular convolution maintains similarity invariance up to 20% noise\n\n\n**Statistical Analysis (Bootstrap, 10K resamples):**\n- **Mean Accuracy (0-30% noise):** 93.8% ± 6.2%\n- **95% Confidence Interval:** [87.2%, 100.4%]\n- **Correlation with Noise Level:** r = -0.997 (p < 0.001 ***)\n- **Speedup Consistency:** 11.3× ± 0.8× (across all operations)\n\n## Reproducibility\n\nAll experiments conducted with fixed random seeds across multiple noise levels (0%, 10%, 20%, 30%). Results include 95% confidence intervals computed via bootstrap with 10,000 resamples. Code is available at https://github.com/gHashTag/trinity under MIT license. Test data archived in `var/trinity/vsa/tests/`.\n\n**Test Environment:**\n- Hardware: Apple M1 Pro (ARM Cortex-M4, NEON-256 SIMD)\n- Compiler: zig 0.15.2 -target arm-none-eabihf -O3\n- Random Seed: Fixed per test batch (1000 samples)\n- **Reproducibility:** <0.1% accuracy variance across re-runs\n\n## Datasets\n\n**Synthetic Data:**\n- **Vectors:** 10,000 random 10,000-dimensional trinary vectors\n- **Operations:** Bind (500K pairs), Unbind (500K queries), Bundle2/3 (500K triples)\n- **Noise Levels:** 0%, 10%, 20%, 30% uniform noise injection\n- **Metrics:**\n - Bind Accuracy: 99.2% (30% noise)\n - Unbound Accuracy: 91.3% (30% noise)\n - Similarity Accuracy: 94.8% mean correlation\n - Similarity Error: ±0.23 (absolute value)\n\n## Ethical Considerations\n\nResearch conducted with synthetic benchmarks, no private data collection. All code is open-source under MIT license. Operations implement privacy-preserving VSA (no vector persistence beyond operation scope).\n\n## Broader Impact\n\nThis work advances hyperdimensional computing by providing efficient SIMD-accelerated vector operations with provable mathematical properties. The NEON-256 implementation achieves hardware-native performance on ARM processors while maintaining numerical accuracy (11.3× speedup). Applications include attention mechanisms for language models, recommender systems, and cognitive architectures requiring efficient similarity computation over high-dimensional sparse representations.\n\n## Limitations\n\n- Current implementation requires ARM NEON SIMD extension (not available on all platforms)\n- Noise resilience tests limited to uniform noise injection (real-world noise is non-uniform)\n- No hardware persistence of vectors between operations (requires external memory architecture)\n- Similarity decay is empirically set (τ = 1 - t with t from [0,1]) - not theoretically optimized\n- Unbinding is approximate (not true inverse of binding)\n\n## Future Work\n\n- Implement true inverse unbinding via Newton-Raphson iteration\n- Add support for other SIMD architectures (AVX-512, AVX2)\n- Investigate optimal similarity decay function (exponential, logarithmic)\n- Add vector persistence for multi-query workloads\n- Implement adaptive noise injection for robustness testing\n- Evaluate on ARM Cortex-X series for embedded deployment\n- Formal verification of SIMD operations (theorem proving)\n\n- Hybrid approach: CPU baseline + FPGA acceleration for compute-intensive operations", "keywords": [ "VSA", "Vector Symbolic Architecture", @@ -32,12 +32,6 @@ "relation": "references", "resource_type": "software" }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227839", - "relation": "references", - "resource_type": "software" - }, { "scheme": "doi", "identifier": "10.5281/zenodo.19227879", @@ -59,7 +53,7 @@ "access_right": "open", "resource_type": { "type": "software", - "title": "Trinity B007: VSA Operations \u2014 Vector Symbolic Architecture Primitives" + "title": "Trinity B007: VSA Operations — Vector Symbolic Architecture Primitives" }, "communities": [ { @@ -108,7 +102,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/docs/research/.zenodo.PARENT_v8.0.json b/docs/research/.zenodo.PARENT_v8.0.json index 6d1f877305..a641881b4a 100644 --- a/docs/research/.zenodo.PARENT_v8.0.json +++ b/docs/research/.zenodo.PARENT_v8.0.json @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Trinity S³AI (Scientific Swarm AI) is a comprehensive research framework for building pure-Zig autonomous agent systems with balanced ternary computing, FPGA acceleration, and formal verification. This parent collection encompasses 7 component bundles covering neural network training (B001), FPGA synthesis (B002), processor architecture (B003), consciousness modeling (B004), language design (B005), numerical encoding (B006), and vector-symbolic operations (B007). All components implement V15 scientific rigor with 95%/99% confidence intervals, effect sizes (Cohen's d), and bootstrap validation (10,000 resamples). v8.0 includes cross-bundle citation analysis showing h-index, g-index, bibliographic coupling, dependency graphs, unified bibliography, LaTeX tables, peer review templates, and multiple citation formats.", + "description": "[Template retired 2026-05-12 — related_identifiers cleaned of superseded and non-existent DOIs. Use the canonical Trinity B-series records (19227865/67/69/71/73/75/77 + 19227879 collection) directly. Do NOT re-upload this template as-is.]
\nTrinity S³AI (Scientific Swarm AI) is a comprehensive research framework for building pure-Zig autonomous agent systems with balanced ternary computing, FPGA acceleration, and formal verification. This parent collection encompasses 7 component bundles covering neural network training (B001), FPGA synthesis (B002), processor architecture (B003), consciousness modeling (B004), language design (B005), numerical encoding (B006), and vector-symbolic operations (B007). All components implement V15 scientific rigor with 95%/99% confidence intervals, effect sizes (Cohen's d), and bootstrap validation (10,000 resamples). v8.0 includes cross-bundle citation analysis showing h-index, g-index, bibliographic coupling, dependency graphs, unified bibliography, LaTeX tables, peer review templates, and multiple citation formats.", "keywords": [ "Trinity Framework", "autonomous agents", @@ -32,25 +32,7 @@ }, { "scheme": "doi", - "identifier": "10.5281/zenodo.19227835", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227843", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227841", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227745", + "identifier": "10.5281/zenodo.19227875", "relation": "references", "resource_type": "software" }, @@ -59,28 +41,13 @@ "identifier": "10.5281/zenodo.19227867", "relation": "references", "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227843", - "relation": "references", - "resource_type": "software" - }, - { - "scheme": "doi", - "identifier": "10.5281/zenodo.19227779", - "relation": "references", - "resource_type": "software" } ], "references": [ "Vasilev, D. (2026). Trinity B001: HSLM-1.95M Ternary Neural Networks. Zenodo. https://doi.org/10.5281/zenodo.19227865", "Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. https://doi.org/10.5281/zenodo.19227867", "Vasilev, D. (2026). Trinity B003: TRI-27 ISA. Zenodo. https://doi.org/10.5281/zenodo.19227867", - "Vasilev, D. (2026). Trinity B004: Queen Lotus Consciousness Cycle. Zenodo. https://doi.org/10.5281/zenodo.19227839", - "Vasilev, D. (2026). Trinity B005: Tri Language. Zenodo. https://doi.org/10.5281/zenodo.19227841", - "Vasilev, D. (2026). Trinity B006: Sacred GF16/TF3 Encoding. Zenodo. https://doi.org/10.5281/zenodo.19227843", - "Vasilev, D. (2026). Trinity B007: VSA Operations. Zenodo. https://doi.org/10.5281/zenodo.19227745" + "Vasilev, D. (2026). Trinity B007: VSA Operations. Zenodo. https://doi.org/10.5281/zenodo.19227875" ], "license": "CC-BY-4.0", "access_right": "open", @@ -94,4 +61,4 @@ } ], "grants": [] -} +} \ No newline at end of file diff --git a/docs/research/.zenodo.PARENT_v9.0.json b/docs/research/.zenodo.PARENT_v9.0.json index 8446b0a850..16be846ee9 100644 --- a/docs/research/.zenodo.PARENT_v9.0.json +++ b/docs/research/.zenodo.PARENT_v9.0.json @@ -1,5 +1,5 @@ { - "title": "Trinity S\u00b3AI Framework \u2014 Complete Research Platform v9.0", + "title": "Trinity S³AI Framework — Complete Research Platform v9.0", "creators": [ { "name": "Vasilev, Dmitrii", @@ -7,7 +7,7 @@ "affiliation": "Trinity Research Collective" } ], - "description": "Trinity S\u00b3AI (Scientific Swarm AI) is a comprehensive research framework for building pure-Zig autonomous agent systems with balanced ternary computing, FPGA acceleration, and formal verification. This parent collection encompasses 7 component bundles covering neural network training (B001), FPGA synthesis (B002), processor architecture (B003), consciousness modeling (B004), language design (B005), numerical encoding (B006), and vector-symbolic operations (B007). All components implement V15 scientific rigor with 95%/99% confidence intervals, effect sizes (Cohen's d), and bootstrap validation (10,000 resamples). v9.0 includes enhanced experimental results, cross-bundle citation analysis (h-index, g-index, bibliographic coupling), unified bibliography, LaTeX tables, peer review templates, and multiple citation formats.\n\n## Framework Architecture\n\nTrinity S\u00b3AI consists of three integrated axes (Sacred, Superhuman, Specialized) across eight development levels:\n\n**Three S\u00b3 Axes:**\n| Axis | Component | Scientific Questions |\n|------|-----------|----------------------|\n| Sacred | GF16/TF3 + FPGA ALU | FP16 vs GF16 accuracy? Zero-DSP feasibility? |\n| Superhuman | Queen + Self-Learning | Auto-adaptation efficacy? Convergence rate? |\n| Specialized | TRI-27 + Tri Language | Ternary vs binary expressiveness? Code density? |\n\n**Eight-Level Stack:**\n```\nLevel 8: HSLM Training (Railway farm, 152 services)\n \u2193 src/hslm/train.zig, src/hslm/trainer.zig\n \u2193 Training loop, evolution, metrics\n \u2193 Checkpoint management (1.9M ternary, 386 KB)\n\nLevel 7: Queen Lotus Cycle (Phases 0-5, Self-Learning)\n \u2193 src/tri/queen/self_learning.zig\n \u2193 Episode tracking, policy adaptation\n \u2193 Tri27Config: kill_threshold, crash_rate_limit\n\nLevel 6: Sacred ALU (GF16/TF3, FPGA)\n \u2193 fpga/openxc7-synth/sacred_alu.v\n \u2193 Zero-DSP ternary inference (35 tok/s @ 0.5W)\n\nLevel 5: TRI-27 ISA (36 opcodes, VM, Verilog)\n \u2193 src/tri27/emu/executor.zig\n \u2193 Ternary dot-product, VSA ops\n \u2193 27\u00d732-bit registers, 64KB memory\n\nLevel 4: Tri Language (grammar, compiler)\n \u2193 src/tri-lang/emit_zig.zig (planned)\n \u2193 .tri spec \u2192 Zig/Verilog dual-target\n\nLevel 3: zig-half (GF16/TF3 implementation)\n \u2193 src/hslm/f16_utils.zig\n \u2193 Saturating arithmetic, \u03c6-distance\n\nLevel 2: LLVM IR (optional backend)\n \u2193 (planned)\n\nLevel 1: FPGA bitstream (XC7A100T)\n \u2193 fpga/openxc7-synth/build.sh\n \u2193 Yosys 0.63 + nextpnr\n```\n\n## Component Bundles (v9.0)\n\n| Bundle | Title | DOI | LOC | Status |\n|--------|-------|-----|-----|--------|\n| B001 | HSLM-1.95M Ternary Neural Networks | 10.5281/zenodo.19227865 | 605 | PPL=125, 51.2K tok/s |\n| B002 | Zero-DSP FPGA Accelerator | 10.5281/zenodo.19227867 | 679 | 0% DSP, 2.8W |\n| B003 | TRI-27 ISA \u2014 27-Register Ternary Processor | 10.5281/zenodo.19227869 | 511 | 129/129 tests passing |\n| B004 | Queen Lotus Consciousness Cycle | 10.5281/zenodo.19227871 | 522 | 5 phases implemented |\n| B005 | Tri Language Specification | 10.5281/zenodo.19227873 | 560 | Grammar defined |\n| B006 | GF16 Ternary Format | 10.5281/zenodo.19227875 | 540 | 1.58 bits/trit |\n| B007 | VSA \u2014 Vector Symbolic Architecture | 10.5281/zenodo.19227877 | 619 | 11.5\u00d7 SIMD speedup |\n\n**Total:** 4,571 LOC across all bundles\n**Mean:** 653 LOC per bundle\n**Scientific Coverage:** 87% (49/56 elements)\n\n## Citation Metrics (v9.0)\n\n**Cross-Bundle Citation Analysis:**\n- **h-index:** 7 (7 bundles with \u22657 citations each)\n- **g-index:** 8 (top 8 papers with 8\u00b2=64 total citations)\n- **Bibliographic Coupling:** Mean 3.2 shared references per bundle pair\n- **Dependency Graph:** 14 edges (bidirectional references)\n- **Strongest Coupling:** B001\u2194B002 (neural network + FPGA)\n\n**Citation Network:**\n```\nB001 (HSLM) \u2192 B002 (FPGA) \u2192 B006 (GF16)\n \u2193 \u2193 \u2193\nB007 (VSA) \u2190 B003 (TRI-27) \u2190 B005 (TriLang)\n \u2193 \u2193 \u2193\nB004 (Lotus) \u2192 PARENT (all bundles)\n```\n\n## Scientific Rigor (V15+)\n\nAll bundles implement V15+ scientific rigor:\n- **Confidence Intervals:** 95%/99% CI via bootstrap (10K resamples)\n- **Effect Sizes:** Cohen's d for all comparisons\n- **P-values:** *, **, *** notation (0.05, 0.01, 0.001)\n- **Statistical Tests:** t-test, Wilcoxon, Mann-Whitney, ANOVA\n- **Reproducibility:** Fixed random seeds, deterministic synthesis\n\n**V9.0 Enhancements:**\n- Experimental results tables with SOTA comparisons\n- Detailed methodology with algorithm pseudocode\n- Noise resilience analysis (B007: 94.8% @ 20% noise)\n- Resource utilization breakdown (B002: 0% DSP, 2.8W)\n- Test coverage analysis (B003: 98.7% overall)\n\n## Research Hypotheses\n\n**H1 (Sacred): GF16 Matches FP16 with 20% Fewer Resources**\n- Null hypothesis (H0): GF16 requires same resources as FP16\n- Alternative hypothesis (H1): GF16 uses 20% fewer LUTs\n- **Status:** Supported (29.7% LUT utilization vs 48% FP32 baseline)\n\n**H2 (Sacred): Zero-DSP Ternary Inference Matches DSP48 Accuracy**\n- Null hypothesis (H0): Zero-DSP reduces accuracy >5%\n- Alternative hypothesis (H1): Accuracy loss <5%\n- **Status:** Supported (PPL 125.3 vs 106.1 FP32, 6.9% gap)\n\n**H3 (Superhuman): Self-Learning Achieves >90% Policy Coverage**\n- Null hypothesis (H0): Random policy exploration\n- Alternative hypothesis (H1): Systematic coverage >90%\n- **Status:** Ongoing (Queen Lotus 5-phase model implemented)\n\n**H4 (Specialized): Ternary Code Density > Binary**\n- Null hypothesis (H0): Ternary encoding larger than binary\n- Alternative hypothesis (H1): Ternary 1.58 bits/trit < 2 bits/bit\n- **Status:** Supported (1.58 bits/trit theoretical optimal)\n\n## Key Results\n\n**B001 (HSLM):**\n- PPL: 125.3 \u00b1 2.1 (TinyStories)\n- Throughput: 51.2K tok/s @ 100MHz\n- Model size: 385 KB (19.7\u00d7 smaller than FP32)\n- Statistical significance: t(14) = 8.73, p < 0.001 ***\n\n**B002 (FPGA):**\n- DSP utilization: 0% (zero-DSP design)\n- Power: 2.8W (10\u00d7 reduction vs FP32)\n- Resource utilization: 14,256 LUTs (29.7%)\n- Timing closure: WNS = +2.1ns\n\n**B003 (TRI-27):**\n- Test coverage: 98.7% (129/129 tests)\n- Formal verification: 15 properties (Z3 4.12.6)\n- Throughput: 33 MIPS @ 100MHz\n- Code density: 0.89 bytes/instruction\n\n**B007 (VSA):**\n- SIMD speedup: 11.5\u00d7 mean (NEON-256)\n- Noise resilience: 94.8% @ 20% noise\n- Accuracy: 91.3% unbinding @ 30% noise\n- Correlation with noise: r = -0.997 (p < 0.001 ***)\n\n## Reproducibility\n\nAll experiments conducted with:\n- Fixed random seeds (42, 133, 267, 313, 647, 751, 941, 997)\n- Deterministic synthesis (Vivado 2023.3, YosysHQ 2023.12)\n- Bootstrap validation (10,000 resamples)\n- 95%/99% confidence intervals\n- Open-source code (MIT license)\n\n## Publications Plan\n\n**Paper 1 (NeurIPS 2026):** \"Zero-DSP Ternary Neural Networks: Sacred Geometry for Efficient Edge AI\"\n- Focus: B001 (HSLM) + B002 (FPGA) + B006 (GF16)\n- Submission: May 2026\n- Expected contribution: 19.7\u00d7 model size reduction, 0% DSP utilization\n\n**Paper 2 (ICLR 2027):** \"TRI-27: A 27-Register Ternary Processor with Formal Verification\"\n- Focus: B003 (TRI-27) + B005 (TriLang)\n- Submission: September 2026\n- Expected contribution: 98.7% test coverage, Z3 verification\n\n**Paper 3 (MLSys 2026):** \"Vector Symbolic Architecture for Autonomous Agent Systems\"\n- Focus: B004 (Lotus) + B007 (VSA)\n- Submission: November 2026\n- Expected contribution: 11.5\u00d7 SIMD speedup, noise resilience\n\n## Future Work\n\n- Complete B005 (TriLang) compiler implementation\n- Integrate B004 (Lotus) consciousness model into HSLM training\n- Port to ARM Cortex-X for embedded deployment\n- Investigate adaptive \u03c4 (sparse attention threshold)\n- Evaluate on domain-specific benchmarks (code, scientific reasoning)\n- Multi-modal extension (text + symbolic representations)\n\n## References\n\n- Vasilev, D. (2026). Trinity B001: HSLM-1.95M Ternary Neural Networks. Zenodo. https://doi.org/10.5281/zenodo.19227865\n- Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. https://doi.org/10.5281/zenodo.19227867\n- Vasilev, D. (2026). Trinity B003: TRI-27 ISA \u2014 27-Register Ternary Processor. Zenodo. https://doi.org/10.5281/zenodo.19227869\n- Vasilev, D. (2026). Trinity B004: Queen Lotus Consciousness Cycle. Zenodo. https://doi.org/10.5281/zenodo.19227871\n- Vasilev, D. (2026). Trinity B005: Tri Language Specification. Zenodo. https://doi.org/10.5281/zenodo.19227873\n- Vasilev, D. (2026). Trinity B006: GF16 Ternary Format. Zenodo. https://doi.org/10.5281/zenodo.19227875\n- Vasilev, D. (2026). Trinity B007: VSA Operations \u2014 Vector Symbolic Architecture Primitives. Zenodo. https://doi.org/10.5281/zenodo.19227877", + "description": "[Template retired 2026-05-12 — related_identifiers and references cleaned of superseded and non-existent DOIs. Canonical Trinity B-series records: 19227865/67/69/71/73/75/77 + 19227879 collection. Do NOT re-upload this template as-is.]
\nTrinity S³AI (Scientific Swarm AI) is a comprehensive research framework for building pure-Zig autonomous agent systems with balanced ternary computing, FPGA acceleration, and formal verification. This parent collection encompasses 7 component bundles covering neural network training (B001), FPGA synthesis (B002), processor architecture (B003), consciousness modeling (B004), language design (B005), numerical encoding (B006), and vector-symbolic operations (B007). All components implement V15 scientific rigor with 95%/99% confidence intervals, effect sizes (Cohen's d), and bootstrap validation (10,000 resamples). v9.0 includes enhanced experimental results, cross-bundle citation analysis (h-index, g-index, bibliographic coupling), unified bibliography, LaTeX tables, peer review templates, and multiple citation formats.\n\n## Framework Architecture\n\nTrinity S³AI consists of three integrated axes (Sacred, Superhuman, Specialized) across eight development levels:\n\n**Three S³ Axes:**\n| Axis | Component | Scientific Questions |\n|------|-----------|----------------------|\n| Sacred | GF16/TF3 + FPGA ALU | FP16 vs GF16 accuracy? Zero-DSP feasibility? |\n| Superhuman | Queen + Self-Learning | Auto-adaptation efficacy? Convergence rate? |\n| Specialized | TRI-27 + Tri Language | Ternary vs binary expressiveness? Code density? |\n\n**Eight-Level Stack:**\n```\nLevel 8: HSLM Training (Railway farm, 152 services)\n ↓ src/hslm/train.zig, src/hslm/trainer.zig\n ↓ Training loop, evolution, metrics\n ↓ Checkpoint management (1.9M ternary, 386 KB)\n\nLevel 7: Queen Lotus Cycle (Phases 0-5, Self-Learning)\n ↓ src/tri/queen/self_learning.zig\n ↓ Episode tracking, policy adaptation\n ↓ Tri27Config: kill_threshold, crash_rate_limit\n\nLevel 6: Sacred ALU (GF16/TF3, FPGA)\n ↓ fpga/openxc7-synth/sacred_alu.v\n ↓ Zero-DSP ternary inference (35 tok/s @ 0.5W)\n\nLevel 5: TRI-27 ISA (36 opcodes, VM, Verilog)\n ↓ src/tri27/emu/executor.zig\n ↓ Ternary dot-product, VSA ops\n ↓ 27×32-bit registers, 64KB memory\n\nLevel 4: Tri Language (grammar, compiler)\n ↓ src/tri-lang/emit_zig.zig (planned)\n ↓ .tri spec → Zig/Verilog dual-target\n\nLevel 3: zig-half (GF16/TF3 implementation)\n ↓ src/hslm/f16_utils.zig\n ↓ Saturating arithmetic, φ-distance\n\nLevel 2: LLVM IR (optional backend)\n ↓ (planned)\n\nLevel 1: FPGA bitstream (XC7A100T)\n ↓ fpga/openxc7-synth/build.sh\n ↓ Yosys 0.63 + nextpnr\n```\n\n## Component Bundles (v9.0)\n\n| Bundle | Title | DOI | LOC | Status |\n|--------|-------|-----|-----|--------|\n| B001 | HSLM-1.95M Ternary Neural Networks | 10.5281/zenodo.19227865 | 605 | PPL=125, 51.2K tok/s |\n| B002 | Zero-DSP FPGA Accelerator | 10.5281/zenodo.19227867 | 679 | 0% DSP, 2.8W |\n| B003 | TRI-27 ISA — 27-Register Ternary Processor | 10.5281/zenodo.19227869 | 511 | 129/129 tests passing |\n| B004 | Queen Lotus Consciousness Cycle | 10.5281/zenodo.19227871 | 522 | 5 phases implemented |\n| B005 | Tri Language Specification | 10.5281/zenodo.19227873 | 560 | Grammar defined |\n| B006 | GF16 Ternary Format | 10.5281/zenodo.19227875 | 540 | 1.58 bits/trit |\n| B007 | VSA — Vector Symbolic Architecture | 10.5281/zenodo.19227877 | 619 | 11.5× SIMD speedup |\n\n**Total:** 4,571 LOC across all bundles\n**Mean:** 653 LOC per bundle\n**Scientific Coverage:** 87% (49/56 elements)\n\n## Citation Metrics (v9.0)\n\n**Cross-Bundle Citation Analysis:**\n- **h-index:** 7 (7 bundles with ≥7 citations each)\n- **g-index:** 8 (top 8 papers with 8²=64 total citations)\n- **Bibliographic Coupling:** Mean 3.2 shared references per bundle pair\n- **Dependency Graph:** 14 edges (bidirectional references)\n- **Strongest Coupling:** B001↔B002 (neural network + FPGA)\n\n**Citation Network:**\n```\nB001 (HSLM) → B002 (FPGA) → B006 (GF16)\n ↓ ↓ ↓\nB007 (VSA) ← B003 (TRI-27) ← B005 (TriLang)\n ↓ ↓ ↓\nB004 (Lotus) → PARENT (all bundles)\n```\n\n## Scientific Rigor (V15+)\n\nAll bundles implement V15+ scientific rigor:\n- **Confidence Intervals:** 95%/99% CI via bootstrap (10K resamples)\n- **Effect Sizes:** Cohen's d for all comparisons\n- **P-values:** *, **, *** notation (0.05, 0.01, 0.001)\n- **Statistical Tests:** t-test, Wilcoxon, Mann-Whitney, ANOVA\n- **Reproducibility:** Fixed random seeds, deterministic synthesis\n\n**V9.0 Enhancements:**\n- Experimental results tables with SOTA comparisons\n- Detailed methodology with algorithm pseudocode\n- Noise resilience analysis (B007: 94.8% @ 20% noise)\n- Resource utilization breakdown (B002: 0% DSP, 2.8W)\n- Test coverage analysis (B003: 98.7% overall)\n\n## Research Hypotheses\n\n**H1 (Sacred): GF16 Matches FP16 with 20% Fewer Resources**\n- Null hypothesis (H0): GF16 requires same resources as FP16\n- Alternative hypothesis (H1): GF16 uses 20% fewer LUTs\n- **Status:** Supported (29.7% LUT utilization vs 48% FP32 baseline)\n\n**H2 (Sacred): Zero-DSP Ternary Inference Matches DSP48 Accuracy**\n- Null hypothesis (H0): Zero-DSP reduces accuracy >5%\n- Alternative hypothesis (H1): Accuracy loss <5%\n- **Status:** Supported (PPL 125.3 vs 106.1 FP32, 6.9% gap)\n\n**H3 (Superhuman): Self-Learning Achieves >90% Policy Coverage**\n- Null hypothesis (H0): Random policy exploration\n- Alternative hypothesis (H1): Systematic coverage >90%\n- **Status:** Ongoing (Queen Lotus 5-phase model implemented)\n\n**H4 (Specialized): Ternary Code Density > Binary**\n- Null hypothesis (H0): Ternary encoding larger than binary\n- Alternative hypothesis (H1): Ternary 1.58 bits/trit < 2 bits/bit\n- **Status:** Supported (1.58 bits/trit theoretical optimal)\n\n## Key Results\n\n**B001 (HSLM):**\n- PPL: 125.3 ± 2.1 (TinyStories)\n- Throughput: 51.2K tok/s @ 100MHz\n- Model size: 385 KB (19.7× smaller than FP32)\n- Statistical significance: t(14) = 8.73, p < 0.001 ***\n\n**B002 (FPGA):**\n- DSP utilization: 0% (zero-DSP design)\n- Power: 2.8W (10× reduction vs FP32)\n- Resource utilization: 14,256 LUTs (29.7%)\n- Timing closure: WNS = +2.1ns\n\n**B003 (TRI-27):**\n- Test coverage: 98.7% (129/129 tests)\n- Formal verification: 15 properties (Z3 4.12.6)\n- Throughput: 33 MIPS @ 100MHz\n- Code density: 0.89 bytes/instruction\n\n**B007 (VSA):**\n- SIMD speedup: 11.5× mean (NEON-256)\n- Noise resilience: 94.8% @ 20% noise\n- Accuracy: 91.3% unbinding @ 30% noise\n- Correlation with noise: r = -0.997 (p < 0.001 ***)\n\n## Reproducibility\n\nAll experiments conducted with:\n- Fixed random seeds (42, 133, 267, 313, 647, 751, 941, 997)\n- Deterministic synthesis (Vivado 2023.3, YosysHQ 2023.12)\n- Bootstrap validation (10,000 resamples)\n- 95%/99% confidence intervals\n- Open-source code (MIT license)\n\n## Publications Plan\n\n**Paper 1 (NeurIPS 2026):** \"Zero-DSP Ternary Neural Networks: Sacred Geometry for Efficient Edge AI\"\n- Focus: B001 (HSLM) + B002 (FPGA) + B006 (GF16)\n- Submission: May 2026\n- Expected contribution: 19.7× model size reduction, 0% DSP utilization\n\n**Paper 2 (ICLR 2027):** \"TRI-27: A 27-Register Ternary Processor with Formal Verification\"\n- Focus: B003 (TRI-27) + B005 (TriLang)\n- Submission: September 2026\n- Expected contribution: 98.7% test coverage, Z3 verification\n\n**Paper 3 (MLSys 2026):** \"Vector Symbolic Architecture for Autonomous Agent Systems\"\n- Focus: B004 (Lotus) + B007 (VSA)\n- Submission: November 2026\n- Expected contribution: 11.5× SIMD speedup, noise resilience\n\n## Future Work\n\n- Complete B005 (TriLang) compiler implementation\n- Integrate B004 (Lotus) consciousness model into HSLM training\n- Port to ARM Cortex-X for embedded deployment\n- Investigate adaptive τ (sparse attention threshold)\n- Evaluate on domain-specific benchmarks (code, scientific reasoning)\n- Multi-modal extension (text + symbolic representations)\n\n## References\n\n- Vasilev, D. (2026). Trinity B001: HSLM-1.95M Ternary Neural Networks. Zenodo. https://doi.org/10.5281/zenodo.19227865\n- Vasilev, D. (2026). Trinity B002: Zero-DSP FPGA Accelerator. Zenodo. https://doi.org/10.5281/zenodo.19227867\n- Vasilev, D. (2026). Trinity B003: TRI-27 ISA — 27-Register Ternary Processor. Zenodo. https://doi.org/10.5281/zenodo.19227869\n- Vasilev, D. (2026). Trinity B004: Queen Lotus Consciousness Cycle. Zenodo. https://doi.org/10.5281/zenodo.19227871\n- Vasilev, D. (2026). Trinity B005: Tri Language Specification. Zenodo. https://doi.org/10.5281/zenodo.19227873\n- Vasilev, D. (2026). Trinity B006: GF16 Ternary Format. Zenodo. https://doi.org/10.5281/zenodo.19227875\n- Vasilev, D. (2026). Trinity B007: VSA Operations — Vector Symbolic Architecture Primitives. Zenodo. https://doi.org/10.5281/zenodo.19227877", "keywords": [ "Trinity Framework", "autonomous agents", @@ -81,7 +81,7 @@ "access_right": "open", "resource_type": { "type": "software", - "title": "Trinity S\u00b3AI Framework \u2014 Complete Research Platform" + "title": "Trinity S³AI Framework — Complete Research Platform" }, "communities": [ { @@ -130,7 +130,7 @@ "doi": "10.13039/501100000000", "award": [ { - "title": "Trinity S\u00b3AI Research Framework", + "title": "Trinity S³AI Research Framework", "number": "TRI-2024-001", "url": "https://github.com/gHashTag/trinity" } diff --git a/src/tri/tri_zenodo.zig b/src/tri/tri_zenodo.zig index 3d06c65558..f92011d577 100644 --- a/src/tri/tri_zenodo.zig +++ b/src/tri/tri_zenodo.zig @@ -1544,7 +1544,7 @@ fn updateSingleRecord(allocator: std.mem.Allocator, rec: UpdateRecord) !void { kw_pos += 1; const related_ids = - \\[{"identifier":"10.5281/zenodo.18939352","relation":"isPartOf","resource_type":"software"},{"identifier":"10.5281/zenodo.19020211","relation":"isRelatedTo","resource_type":"software"},{"identifier":"10.5281/zenodo.19020213","relation":"isRelatedTo","resource_type":"software"},{"identifier":"10.5281/zenodo.19020215","relation":"isRelatedTo","resource_type":"software"},{"identifier":"10.5281/zenodo.19020217","relation":"isRelatedTo","resource_type":"software"}] + \\[{"identifier":"10.5281/zenodo.18939352","relation":"isPartOf","resource_type":"software"},{"identifier":"10.5281/zenodo.19020270","relation":"isRelatedTo","resource_type":"software"},{"identifier":"10.5281/zenodo.19020275","relation":"isRelatedTo","resource_type":"software"},{"identifier":"10.5281/zenodo.19020280","relation":"isRelatedTo","resource_type":"software"},{"identifier":"10.5281/zenodo.19020282","relation":"isRelatedTo","resource_type":"software"}] ; const meta_body = try std.fmt.allocPrint(allocator,