CMF Decision

Decisions in milliseconds. One portable CMF file.

Turn a customer message into a clear action: route a request, classify an intent, or choose a tool using your own trained skill. Cortiq selects a label or abstains when uncertain — without generating tokens locally. Connect an optional oracle for unfamiliar cases, then use its answers to grow your local skills.

Local decisions · Custom skills · Optional oracle

Try the live Space → — enter a request and inspect the decision, confidence and reconstruction errors. No installation or API key needed.

Quick start · Your own skill · Oracle · API · Benchmarks · На русском

Quick start

Use the hosted service

Prefer an API without local setup? allaigate Routing API is a ready-to-use routing service built on the CMF format. Get an API key on the site and follow the integration guide — no model download or server deployment required.

Run locally

Install the latest published CLI from crates.io (requires Rust 1.88 or newer):

cargo install cortiq-cli --locked

Run the same command again to update. Download the model and make your first decision:

curl -fL -o cortiq-decision.cmf \
  https://huggingface.co/infosave/cmf-decision/resolve/main/cortiq-decision.cmf
cortiq decide cortiq-decision.cmf --skill banking77 -p "I still have not received my new card"

Result: card_arrival, answered locally. No cloud key required. Included skills: banking77, clinc150, massive.

Choose your hardware

CPU works without configuration. The same installed CLI also includes GPU support (from Cortiq 0.8.0):

# Apple Silicon
CORTIQ_DECISION_DEVICE=metal cortiq decide cortiq-decision.cmf \
  --skill banking77 -p "I still have not received my new card"

# Vulkan GPU (a hardware Vulkan driver is required)
CORTIQ_DECISION_DEVICE=vulkan cortiq decide cortiq-decision.cmf \
  --skill banking77 -p "I still have not received my new card"

On multi-GPU hosts, also set CORTIQ_DECISION_VULKAN_ADAPTER to a unique part of the GPU name. The same variables work with cortiq serve. GPU guide →

Measured results

Quality: the model alone vs Jev 1.13

With abstention enabled, accepted answers are 97.24–98.70% correct; the model answers 54.30–92.11% of requests locally, depending on the task. Accuracy and coverage together →

Laya: 77 choices on the same Mac

Trained CMF skill: 93.34%, 3.10 ms p50. Laya's base English checkpoint, all 77 label names at once: 35.65%, 917.59 ms p50. All 3,080 rows, both local, no oracle. A 77-option stress test, not Laya's best achievable result: shortlisting and multilingual were not tested, and training conditions differ. Full protocol, failed long-rubric run and raw results →

Speed: now on Metal and Vulkan

1.10–1.24 ms on RTX PRO 4000; 2.28–2.58 ms on Apple M4. The full path is accelerated, not just the reconstruction kernel. All 10,554 test examples keep their CPU decisions and abstentions. No retraining.

M4 uses alternating CPU/GPU requests; RTX uses a warmed continuous stream. Sparse RTX traffic is slower; Metal can have worse tails under desktop load. Protocol, tail latency and memory → · Earlier API comparison with Jev →

Cost: cloud only when needed

Local-only decisions incur $0 in API fees. The optional hybrid run used DeepSeek V4.1 Flash for hard cases, reaching 93.93% / 97.47% / 88.00% overall accuracy. Fees are extrapolated from recorded runs, not a hosting quote. Oracle setup →

Why CMF?

One deployable file. Encoder, skills and decision rules travel together.
Resonance, not text generation. Each label tries to reconstruct the input signal; the smallest error wins. A calibrated gate decides whether to answer.
Your tasks, your control. Add skills from examples; keep existing skill parameters unchanged. Version and roll back learned updates.
Local by default. Enable an external oracle only when you need one.

Add your own skill

Prepare labeled examples in train.jsonl and describe the labels in question.json:

cortiq decision add-skill cortiq-decision.cmf --skill support \
  --train train.jsonl --question question.json -o support.cmf
cortiq decide support.cmf --skill support -p "Please cancel my subscription"

Example data, calibration and training options →

To serve over HTTP: cortiq serve cortiq-decision.cmf. Requests, keys and router compatibility →

Benchmark scope: reused public datasets; CMF trained on train + dev, Jev given two examples per label. The model combines a Cortiq NVG-modified text encoder with trained resonance topologies. Method, data and licenses · Chart data

Resonance Routing — US 19/452,440.

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