O1-Flash — 端侧 System One 决策模型

Typed decisions, not text. O1-Flash is the O-series (attention-free ARR) line of AwareLiquid, re-cast as a System One model in the spirit of TypeSafe's Jev (Sept 2026): state in, calibrated parallel probabilities out. No text generation exists anywhere in the output path.

state text/JSON ──> liquid recurrent core (O(1) carried state)
                        │  h_final (B, D)  ── encoded once
                        ├─ Choice      ──> distribution over options  + confidence
                        ├─ Score       ──> distribution over levels   + confidence
                        └─ Probability ──> p ∈ [0,1]                  + confidence
                    (all read in parallel, no cross-question interaction)

Why it exists

The M1 repo's O-series measured three edge properties no transformer can match: O(1) inference state (0.381 MB flat, 8063× smaller than a KV cache at 1M tokens), 5 MB ONNX wake-word export, and CPU-only inference on irregular sensor streams. Jev (TypeSafe, 2026-09-15) showed the product shape for such an engine: give up text generation, return typed calibrated decisions in one parallel pass — 40–400× cheaper than routing every small decision through an LLM.

O1-Flash combines the two: the Jev parallel-readout paradigm on the O(1) liquid state, sized for the edge instead of the cloud.

What is proven / what is not (honest)

Structural, proven by tests in this repo:

• O(1) state: carried state size depends only on architecture, never on input length (tested to 4096 tokens; the M1 repo sweeps to 1M).
• Question independence: answer A is bit-identical whether B is in the request — no cross-question attention exists (test_decision_heads.py).
• Schema-bounded outputs: every answer is a probability vector over caller-defined options/levels; an out-of-schema value cannot be produced.
• Calibrated-confidence training reference: Brier + decision-CE objective with ECE reporting (mt_flash/calibration.py).

Not proven (no trained checkpoint yet):

• No decision-quality numbers. The encoder is the O-series liquid core ported from M1 (mt_lnn/mt_lnn_v2.py MTLNNLayerV2 math), but no weights ship in this repo. This is the architecture + training reference, same discipline as M2's DPO/GRPO reference.
• No comparison against Jev's published workflow accuracy (67.8%).
• Calibration claims are reference-level: the recipe exists and is tested on synthetic data, but RLCD itself is TypeSafe's unpublished method.

Quick start

pip install -r requirements.txt
python -m pytest tests/ -q          # 22 tests (joint-training test ~3 min)

# smoke: one state, three typed questions, one parallel pass
python -m mt_flash.smoke

# decision bench: joint calibration training on the synthetic set
python -m benchmarks.decision_bench

from mt_flash.model import O1Flash
from mt_flash.schema import ChoiceQuestion, ProbabilityQuestion, ScoreQuestion

m = O1Flash()
resp = m.decide(
    "Server down since 03:12; 400 tickets in the infra queue.",
    [
        ChoiceQuestion(id="route", options=("billing", "infra", "other")),
        ScoreQuestion(id="sev", levels=("low", "mid", "high")),
        ProbabilityQuestion(id="escalate", prompt="needs escalation now"),
    ],
)
print(resp.to_dict())

Design

The Jev-parallel mapping and every architectural decision are documented in DESIGN.md. Lineage: the liquid core is the standalone port of M1's mt_lnn/mt_lnn_v2.py (MTLNNLayerV2) and mt_lnn/arr.py (MTRecurrentMixer, attention-free). Sequential scan here (O(T) compute, O(1) state); the Blelloch parallel-scan path is a documented optimisation.

Repository boundaries

• everest-an/M1 — the M1 model core (private). The O-series ARR and liquid math live there.
• AwareLiquid/M2 — experimental architecture (DPO/GRPO references).
• AwareLiquid/O1-Flash (this repo) — the standalone edge decision model.
• AwareLiquid/AwareLiquid-World — JEPA world-model line.

License

MIT — see LICENSE.