# Ternary Intelligence Stack (TIS) | RFI-IRFOS — Master Development Chronicle
### Official Evaluation Artifact (SPRIND / Next Frontier AI)
**Stack Version:** v1.3.7 (Ecosystem) / v3.0 (albert. model)
**Last Updated:** 2026-05-19
**Status:** ACTIVE — PHASE 20: AUTO-EVOLUTIONARY TERNARY TRAINING
**Repository:** https://github.com/eriirfos-eng/ternary-intelligence-stack
**Patent Pending:** A50296/2026 (@sparseskip sparse matmul primitive)

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## 1.0 ACTIVE & UPCOMING PHASES (Reverse Chronological)

### Phase 25: tern-forge — Hierarchical Code Synthesis Framework
*Target: First non-zero score on ProgramBench (May 2026 benchmark; every major model scored 0)*
*ETA: 2026 Q4 (dependent on Albert reaching 10L+ with code corpus)*

ProgramBench exposes the gap between "code assistance" and "systems engineering": models fail to coordinate thousands of functions into a compilable system from a blank slate, with no internet and no docs. The TIS stack has native primitives to attack this:

- [ ] **25.1 Orchestration Scaffold** — `crates/tern-forge/` Rust crate using albert-cli infrastructure. Phases: Blueprint (module tree only) → Stub (empty functions, trait signatures) → Recursive Refinement (one MoE expert per module domain) → Verify.
- [ ] **25.2 Compiler Veto Gate** — Integrate `rustc` / `ternlang` compiler output as a ternary signal: `{-1=reject, 0=hold, +1=accept}`. Failed compilation issues a Reject verdict and forces targeted re-roll on the failing module only — not the entire codebase.
- [ ] **25.3 Structural Diffing Corpus** — Train Albert on repository evolution sequences (git diff chains: `main.rs` → 50-file library). Teaches that "building from scratch" is a sequence of refactors, not a monolithic generation.
- [ ] **25.4 Expert-to-Module Routing** — Map MoE expert domains to code subsystem types (I/O, core logic, data structures, tests). Each module routes to its specialist expert rather than averaging across all.
- [ ] **25.5 BET VM Sandbox Execution** — Use the BET VM to simulate generated sub-module execution in isolation before system integration, catching semantic errors before the compiler sees them.

> **Honest note:** Albert at 6L with word-level vocabulary cannot generate coherent Rust today. tern-forge will initially use Claude/GPT as interim code intelligence, with Albert replacing it as he matures. The TIS-native contribution is the orchestration framework and the ternary veto gate — those are buildable now.

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### Phase 24: General Purpose Transition — Instruction Following & Chain-of-Thought
*Target: Albert answers questions, not just continues text. ETA: 2026 Q3 (9L+)*

- [ ] **24.1 Instruction Fine-Tuning** — `qa_instruction.txt` (2196 User:/Albert: pairs, generated 2026-05-07) unlocks automatically at stage_9 in the staged corpus loader. Albert sees the `User:/Albert:` format statistically and learns query-response continuation.
- [ ] **24.2 Chain-of-Thought Emergence** — At 9-10L depth, intermediate layer representations form stable "thought" attractors before the final token. No architectural change needed — depth is the mechanism.
- [ ] **24.3 Reasoning Corpus** — Add structured Q&A with explicit reasoning steps (math, logic, code explanation) to stage_11 corpus.
- [ ] **24.4 Evaluation Harness** — Held-out test set perplexity vs. unigram baseline. `scripts/eval_perplexity.py` harness ready; requires albert-test `--eval` mode.

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### Phase 23: GPU Backend — CUDA Ternary Sparse Matmul
*Target: 10-50× inference speedup on NVIDIA Pascal+ hardware. ETA: 2026 Q3*

- [ ] **23.1 INT2-Packed Weight Encoding** — Pack ternary weights as 2-bit values (4 weights per byte). 16× memory reduction vs F32. Architecture documented in `cuda_matmul.rs` (TRL 3).
- [ ] **23.2 dp4a GEMV Kernel** — CUDA `ternary_gemv_dp4a`: uses `__dp4a` integer dot product instruction (Pascal+, compute 6.1+). Projected 10-50× vs CPU baseline at 56% sparsity.
- [ ] **23.3 candle CustomOp1 Integration** — Wire CUDA kernel into the candle tensor graph via `CustomOp1` trait so training and inference use the same code path.
- [ ] **23.4 Benchmark Publication** — Docker image reproducing the sparsity speedup table. `docker run rfi-irfos/tis-bench` → reproduces all numbers from `BENCHMARKS.md`.

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### Phase 22: SPRIND Credibility & Benchmark Reproducibility
*Target: Satisfy SPRIND evaluator audit requirements. ETA: 2026 May (deadline May 15)*

- [x] **22.1 Whitepaper** — `docs/tis-sprind-submission-2026.tex` submitted (IEEE two-column LaTeX, all 5 authors, Patent Pending A50296/2026).
- [x] **22.2 EU AI Act Mapping** — `docs/compliance/eu_ai_act_mapping.md` — article-by-article for Art. 5, 9–15, 53.
- [x] **22.3 Evolution Evidence** — `albert-moe-13/docs/EVOLUTION_EVIDENCE.md` — verified 3L→6L surgery timeline with Net2Net code and scaling table.
- [x] **22.4 Reproducibility Spec** — `docs/REPRODUCIBILITY.md` + `repro_check.rs` + `verify_reproducibility.sh`.
- [x] **22.5 Security Audit** — SQL injection fixed (2026-05-07), `.env` removed from repo, auth middleware verified globally applied.
- [ ] **22.6 Held-out Perplexity** — Run `eval_perplexity.py` and publish `eval_results.json` as benchmark artifact. Float32 baseline comparison required.
- [ ] **22.7 Benchmark Docker** — Reproduce sparsity speedup table in a pinned container with statistical uncertainty intervals.

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### Phase 21: Staged Knowledge Scaling (ACTIVE)
*Focus: Match corpus complexity to model depth — autonomous knowledge unlocking per surgery.*

- [x] **21.1 Staged Corpus Loader** — `load_corpus(num_layers)` reads `data/corpus/stage_N/` dirs where N ≤ num_layers. Surgery increments depth; next restart auto-unlocks richer data. (2026-05-07)
- [x] **21.2 Stage 3: Foundational** — Bible + Alice. Grammar, vocabulary, basic syntax. Active from 3L.
- [x] **21.3 Stage 6: Narrative** — 12 Gutenberg classics (Moby Dick, War and Peace, Crime & Punishment, Frankenstein, etc.). Complex sentence structure, wider vocabulary. Active from 6L.
- [ ] **21.4 Stage 7: Factual** — Simple Wikipedia (120k lines, 500k+ tokens). Diverse topics, factual prose, topic-sentence structure. Unlocks on next surgery → 7L. 🎯 *Imminent.*
- [ ] **21.5 Stage 9: Instruction** — `qa_instruction.txt` (2196 User:/Albert: pairs from Wikipedia + Bible + Gutenberg). Instruction format unlock at 9L.
- [ ] **21.6 Stage 11: Technical** — Linux kernel docs, EU AI Act text. Specialized language, legal/technical register. Unlocks at 11L.
- [ ] **21.7 Stage 13: Command/Response** — TLDR pages (Unix command → description pairs). Terse instruction-following format. Unlocks at 13L.

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### Phase 20: Auto-Evolutionary Ternary Training (ACTIVE — CURRENT)
*Focus: Albert MoE-13 trains and expands its own architecture autonomously.*

- [x] **20.1 Net2Net Surgery** — Safe-copy layer expansion: layer N weights copied to N+1 for warm-start. EvolutionManager triggers autonomously on plateau/collapse. Witnessed live: 3L→4L→5L→6L.
- [x] **20.2 @sparseskip Element-Level** — `SparseCache` with `pos_indices[i]` / `neg_indices[i]` per output neuron. `forward_sparse()` skips ~56% of multiplications. Patent Pending A50296/2026.
- [x] **20.3 KV-Cache** — `Attention.kv_cache`: prefill stores full K/V; decode concatenates single new token. O(1) per step vs O(seq²). Result: **83-125 tok/s** from ~5 tok/s baseline.
- [x] **20.4 Pre-Ternarized Weight Cache** — `prepare_inference()` computes ternary weights once at load. Eliminates 8 candle ops per `TernaryLinear` per forward pass.
- [x] **20.5 F32 Gate Fix** — MoE routing gate changed from TernaryLinear to `candle_nn::Linear`. Ternary at 256→12 scale caused routing collapse (0.333/0.333/0.333). F32 gate has full resolution to differentiate 12 experts.
- [x] **20.6 Collapse→Surgery Escalation** — When best checkpoint doesn't exist or is also above collapse threshold, rollback skipped; surgery fires directly. Breaks infinite rollback loop.
- [x] **20.7 Whitepaper Annotations** — All core algorithm files annotated with §N.N cross-references (ste.rs §5.1, ternary_linear.rs §5.1+5.2, moe.rs §11.1+10.4, evolution.rs §11.2, train_bible.rs §11.4+11.6).
- [x] **20.8 GPU Backend Sketch** — `cuda_matmul.rs`: INT2-packed weight encoding, dp4a kernel outline, candle CustomOp1 skeleton, 3-phase roadmap. TRL 3.
- [x] **20.9 Dashboard Live Telemetry** — TELE every 30 batches (~60s), GRAD every batch, ROUTE every 10 batches. 6dp precision for sub-millinorm grad norm visibility.
- [ ] **20.10 albert-test `--eval` Mode** — Enable `eval_perplexity.py` by adding held-out evaluation forward pass to albert-test binary.

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### Phase 19: Universal Knowledge Layer ✅
- [x] Joint Alice + Bible 8000-token WordLevel tokenizer (min_freq=2). All major characters tokenized.
- [x] Multi-corpus pipeline: `load_corpus()` auto-discovers all `.txt` in corpus dir.
- [x] GitHub engineering reasoning corpus harvester (`scripts/ingest_github.py`).
- [x] Reddit scraper (`scripts/reddit_scraper.py`) — staged pending API keys.

### Phase 18: TernAudit Infrastructure ✅
- [x] CLI/REST audit framework with EU AI Act regulatory mapping.
- [x] VS Code extension inline audit markers.

### Phase 17: WASM Runtime ✅
- [x] BET VM compiled to wasm32-unknown-unknown. Semantic consistency validated.

### Phase 16: TernStudio IDE ✅
- [x] Monaco-based IDE with Liquid Time, TAP (Ternary Actuator Protocol), Pyodide WASM sandbox.

### Phase 15: Academic & Ecosystem Convergence ✅
- [x] USN ternary research group alignment. IEEE-style whitepaper. `ternpkg` registry.

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## 2.0 LEGACY PHASES (1–14)

| Phase | Description | Key Artifact | Status |
| :--- | :--- | :--- | :--- |
| **14** | TernTranslator | `ternlang translate` CLI | COMPLETE |
| **13** | Repo Professionalization | `CODEOWNERS`, SEO READMEs | COMPLETE |
| **12** | Model Coherence (QAT/STE) | STE ternary training from scratch | COMPLETE |
| **11.6** | ExaTern Foundation | SIMD/Trit-Packing AVX2 kernels | COMPLETE |
| **11.5** | Ternary Compression | Weight transmutation pipeline | COMPLETE |
| **11** | MCP Intelligence Upgrade | 34 MCP tools (all free) | COMPLETE |
| **10.5** | Ecosystem Collaboration | TERN-ASM Emitter | COMPLETE |
| **10** | Extension Maturity | VS Code ghost hints/decorations | COMPLETE |
| **9** | MoE-13 Orchestrator | 12 experts, Top-3 sparse routing | COMPLETE |
| **8** | AI Reasoning Toolkit | DeliberationEngine (EMA) | COMPLETE |
| **7** | Ecosystem Bridges | crates.io — 38 crates at v1.3.7 | COMPLETE |
| **6** | Hardware HDL/ISA | `BetRtlProcessor` simulation | COMPLETE |
| **5** | Actor Model/Distributed | `TernNode` TCP/Actor protocol | COMPLETE |
| **4** | Language Completeness | `for`, `loop`, `struct`, `match` | COMPLETE |
| **3** | TritTensors & Sparse Inference | `TSPARSE_MATMUL` — @sparseskip origin | COMPLETE |
| **2** | Stdlib & CLI | `ternlang` compiler CLI | COMPLETE |
| **1** | Foundation | BET VM byte-machine architecture | COMPLETE |

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## 3.0 CURRENT METRICS (2026-05-19)

| Metric | Value | Context |
| :--- | :--- | :--- |
| albert. architecture | **17L** · 256H · 12E · Top-3 · **256CTX** · 32,000V | v3.0 · 5 autonomous Net2Net surgeries since ep511 |
| albert. parameters | ~58M total stored / ~13M active per token | Top-3 of 12 experts; ternary {−γ, 0, +γ} throughout training via STE |
| Training loss — epoch ATL | **9.7884** (ep2116, 2026-05-19) | Alternating descent phase; 5 new ATLs in 7 epochs |
| Training loss — batch ATL | **9.6235** (ep2114, 2026-05-19) | Intra-epoch minimum; random baseline ln(32000) = 10.373 |
| Inference speed | **9.9–21.3 tok/s** CPU (ep2104 bench, v3.0) | KV-cache + @sparseskip; 32k vocab embedding dominates at current training depth; sparsity benefit scales with weight convergence |
| Routing sparsity | 9/12 experts skipped per decode step | Expert-level @sparseskip — 75% routing skip; entropy H=2.467 (uniform) |
| Weight sparsity | 3–6% zero-weight skip at ep2116 (TELE) | Grows toward v2.0.0 levels (~56%) as weights converge; zero positions never touched in matmul |
| Corpus (active) | **1.6 GB** total · stages [3,6,7,8,9,10] active | EN/DE/FR/HU/ZH/AR/KO/SV/FI + 15-language Wikipedia · 10% chaos layer invariant |
| Patent | A50296/2026 | @sparseskip sparse matmul primitive — Patent Pending (Austria) |
| Crates published | 38 crates at v1.3.7 | crates.io, all open-core LGPL |
| MCP tools | 34 tools | All free tier via Smithery + HTTP |
| Training epochs | ep2116+ (2026-05-19) | Modal T4 GPU · ~$0.021/epoch · uninterrupted run since 2026-05-10 |

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## 4.0 ROADMAP MAINTENANCE PROTOCOL
1. **Honesty first** — no hyperbolic parameter counts or capability claims without verifiable artifacts.
2. **Reverse chronological** — newest phases at top.
3. **Artifact requirement** — phases remain active until a corresponding verified artifact is registered.
4. **Metric grounding** — all numbers in §3.0 must match current `config.json` and training logs.
