---
license: other
license_name: proprietary
license_link: LICENSE
tags:
  - coreml
  - regression
  - a-list
library_name: coreml
---

# A-LIST exercise extractor (Core ML)

On-device replacement for the remote LLM that extracts structured numbers from
free-text exercise entries in the A-LIST app (locomo mode). Single fp16 Core ML
graph: MiniLM-L6 encoder (frozen, mean-pool + L2-norm inside the graph) + MLP
heads.

- **Inputs**: `input_ids` int32 (1,128) WordPiece/bert-base-uncased; `attention_mask`
  int32 (1,128); `parsed` float32 (1,10) regex features (see the app's
  `ExerciseFeatureParser` for the exact order and transforms).
- **Output**: float32 (1,8) — `[log1p(duration_min), log1p(calories), heart_rate_bpm,
  intensity_1_10, steps_gate_logit, log1p(steps), distance_gate_logit, log1p(distance_km)]`.

## Versions

- **`v2/` (current, 2026-08-23)**: identical graph retrained on the sol-relabeled
  dataset (exercise-text-unified:v0 — one uniform frontier teacher, calorie CV 1.6%
  vs the old labels' ~11%). On the frozen held-out-user sol eval (1,127 entries) it
  beats the v1 artifact on every field: duration 68.5% vs 62.7%, calories 58.0% vs
  51.5%, heart rate ±10bpm 76.0% vs 67.6%, intensity ±1 81.7% vs 76.0%, steps gate
  94.9% vs 88.6%, distance gate 94.6% vs 90.8%. Same I/O contract and footprint.
- **Root files (v1, 2026-08-16)**: distilled from ~14.5k historical production
  extractions (mixed teacher generations). Kept resolvable for rollback; the app's
  `ExerciseExtractorDescriptor.v1()` still pins these bytes.
- `golden_cases.json` holds 10 input→output cases generated from the v1 artifact —
  the app uses them to pin the *decode rules* (weights-independent), so they remain
  valid across versions.

Internal model for the A-LIST iOS app. Not intended for third-party use.