Roadmap¶

Canonical machine-facing roadmap lives in .specs/project/ROADMAP.md. This page is the human-facing view with exit criteria and risk notes. Task-level detail: docs/tasks.md.

Current phase: M6 Proof & Release ✅ complete and released (v0.3.0), including the post-M6 B-1 (multilingual quality + per-language calibration), B-2 (CUDA-graph fast path), B-3 (confidence thresholding / System-2 handoff) and B-4 (input-noise robustness) work. Delivery model: one shippable increment per milestone; no fixed dates.

graph LR
    M0[M0 Bootstrap] --> M1[M1 Wire Contract]
    M1 --> M2[M2 LLM Backend + Serve]
    M2 --> M3[M3 Local Encoder]
    M3 --> M4[M4 Training + Calibration]
    M4 --> M5[M5 Ecosystem + Accel]
    M5 --> M6[M6 Proof + Release]

M0 — Bootstrap¶

Goal: Reproducible Python 3.12 + uv project with quality gates and license. Depends on: nothing. Exit criteria: - uv sync installs from uv.lock on Python 3.12. - uv run ruff check ., uv run pyright, uv run pytest all pass. - CI runs the same gates on push and PR. - Apache-2.0 LICENSE, README.md, CONTRIBUTING.md, AGENTS.md present.

Risks: Python version drift (see B-001); uv lockfile churn.

M1 — Wire Contract¶

Goal: Freeze the Jev-exact contract and primitives before backends. Depends on: M0. Exit criteria: - Primitives validated with limits (choice ≤255, score 2–10 levels). - wire.py matches Jev field names/types; golden fixtures pass. - All documented error shapes (401/422/429/529) reproduce. - A FakeBackend answers the full cycle offline.

Risks: misreading Jev edge behavior; mitigated by golden fixtures and docs/protocol.md.

M2 — LLM Backend + Serving¶

Goal: End-to-end product using structured outputs of existing LLMs. Depends on: M1. Exit criteria: - POST /v1/systemone served by FastAPI; SDK + CLI work. - A real Jev client repointed at tachyone-serve returns correct answers. - Backend is swappable behind backends/base.py; no wire change. - Retry/backoff verified for 429/529.

Risks: structured-output reliability across providers (OD-1); provider drift.

M3 — Local Encoder Backend¶

Goal: Offline single-pass encoder with routing and calibrated confidence. Depends on: M2 (server reused), M4 (for a trained checkpoint — can start with base weights). Exit criteria: - All three primitives answered offline, no key, no network. - Router selects English vs multilingual checkpoint with documented overhead. - Confidence derived from distribution; return_details exposes probabilities. - Batching (predict_batch, sort_by_length) and lifecycle (preload, max_loaded, eviction) work.

Risks: base-model quality before tuning; VRAM pressure with two checkpoints.

M4 — Training & Calibration¶

Goal: Reproducible training and calibration within 12GB VRAM. Depends on: M3 (architecture), M1 (data contract). Exit criteria: - generate_data.py → deterministic JSONL. - LoRA/QLoRA fine-tuning completes on RTX 3060 12GB. - RLCD proper-scoring training produces calibrated probabilities. - Temperature fitting reduces ECE on a held-out split. - Accuracy/ECE/latency report reproducible from committed scripts.

Risks: OOM (B-002); small calibration sets; synthetic-data bias.

M5 — Ecosystem & Acceleration¶

Goal: Integration surface and speed paths, contract unchanged. Depends on: M3, M4. Exit criteria: - ONNX backend + optional fast path (TileLang/CUDA graphs) behind extras. - MCP stdio server, LangChain adapter, Docker/compose all work. - Extras isolate optional dependencies; graceful fallback when unavailable.

Risks: ONNX op coverage; CUDA-graph constraints (OD-4).

M6 — Proof & Release¶

Goal: Public credibility. Depends on: M5. Exit criteria: - Benchmark report comparable to MASSIVE / XNLI / typed-decisions — partially met (2026-09-26): an external, public nine-family probe is now measured and published with the method and its limitations in compare.md §3 (it reproduces the peer model's own card to three decimals); the three named probes are still open — benchmarks.md → Known limitations, backlog B-7. - Docs site published. - Hugging Face weights + model card and a GitHub release.

Risks: benchmark comparability; weights distribution model (OD-3).

Milestone → feature mapping¶

Milestone	Feature spec
M0, M1	`.specs/features/wire-contract/`
M2	`.specs/features/llm-backend/`
M3	`.specs/features/encoder-backend/`
M4	`.specs/features/training-calibration/`
M5, M6	`.specs/features/ecosystem/`

Post-M6 — Backlog (delivered)¶

The active backlog items shipped across v0.2.0 and v0.3.0:

B-1 Multilingual choice/score quality ✅ (v0.2.0) — localized per-language data, per-record RNG, per-(primitive, language) temperature fitting, runtime language detection, and per-language ECE reporting. Follow-up raised the multilingual LoRA rank to 64: overall accuracy 0.702 → 0.853 and es ECE 0.170 → 0.038 (2/6 languages ≤ 0.05 — es, pt; four open).
B-2 Fast-path (TileLang/CUDA graphs) ✅ (v0.2.0) — TACHYONE_FAST=1 wires per-shape CUDA graphs with bf16 weights and graceful fallback; measured 2.68× p50 (NFR-P01/P07 met).
B-3 Confidence thresholding / System-2 handoff ✅ (v0.3.0) — normalized_entropy/margin and assess/assess_response; CLI --threshold appends a sibling handoff object (CAL-06).
B-4 Input-noise robustness ✅ (v0.3.0) — seeded noise_rate augmentation and a clean/noisy evaluation split; the released adapters are already robust to the injected noise (TRAIN-08).

Carried-over ideas (canonical list: .specs/project/BACKLOG.md):

Provider registry for LLM backends (OpenAI-compatible, local llama.cpp).
Additional checkpoints (typed-decisions variant, larger multilingual).
Streaming multi-question responses.
Web console for interactive triage.