Publish 0.6 main 3-seed point-stroke model stack and technical model card

MANIFEST.json

@@ -1,8 +1,9 @@
 {
-    "schema":  "aiflow-hf-research-snapshot-v1",
-    "generated_at":  "2026-07-23T18:48:05.4863509Z",
+    "schema":  "aiflow-hf-research-snapshot-v2",
+    "generated_at":  "2026-07-23T18:58:21.6521577Z",
     "track":  "R_noncommercial_only",
     "product_validation":  false,
+    "public_release":  true,
     "contains_raw_dataset":  false,
     "files":  [
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                       "path":  "reports/boundary_behavior_guard_report.json",
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                       "sha256":  "27b31da71b9f39f7ded38aa1ce3694911af140d22854543d4ae2b5f9e709b479"
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+                  },
                   {
                       "path":  "reports/RESEARCH_REPORT.md",
                       "bytes":  22993,

MODEL_INDEX.json

@@ -0,0 +1,46 @@
+{
+  "schema": "aiflow-math-ink-06-public-checkpoint-index-v1",
+  "model_version": "AIFlow Math Ink 0.6 research",
+  "input_contract": {
+    "ink_space": [128, 128],
+    "maximum_events": 128,
+    "canonical_sample_hz": 6.0,
+    "sequence_channels": 19,
+    "source_modalities": ["online", "raster"]
+  },
+  "output_contract": {
+    "exact_labels": 378,
+    "visual_families": 324,
+    "virtual_stroke_hypotheses": 4,
+    "behavior_roles": [
+      "identifier_lower",
+      "identifier_upper",
+      "multiply_operator"
+    ]
+  },
+  "seeds": {
+    "17": {
+      "base": "models/seed17/base_378.pt",
+      "online_adapter": "models/seed17/online_adapter.pt",
+      "behavior_role_head": "models/seed17/behavior_role_head.pt"
+    },
+    "31": {
+      "base": "models/seed31/base_378.pt",
+      "online_adapter": "models/seed31/online_adapter.pt",
+      "behavior_role_head": "models/seed31/behavior_role_head.pt"
+    },
+    "47": {
+      "base": "models/seed47/base_378.pt",
+      "online_adapter": "models/seed47/online_adapter.pt",
+      "behavior_role_head": "models/seed47/behavior_role_head.pt"
+    }
+  },
+  "grouping_head": "artifacts/boundary_behavior_guard.joblib",
+  "release_state": {
+    "track": "R_noncommercial_only",
+    "product_validation": false,
+    "commercial_release": false,
+    "litert_exported": false,
+    "distillation_allowed_for_r_track_heads": false
+  }
+}

NOTICE.md

@@ -0,0 +1,11 @@
+# Rights and attribution notice
+
+This repository publishes AIFlow research checkpoints and reports, not the original handwriting databases.
+
+- HWRT database material used during research is treated as ODbL-1.0. Database attribution and share-alike implications must be reviewed for any derivative database distribution.
+- CROHME-derived grouping and behavior heads are restricted to non-commercial research use in this release.
+- UJI Pen and Pendigits source terms remain applicable to their respective source material.
+- No permission is granted here to redistribute the original datasets.
+- The checkpoints have not passed AIFlow commercial distribution, on-device release, or product-validation gates.
+
+The repository is therefore marked `license: other` and the included weights must not be used commercially. A commercial AIFlow checkpoint must be trained from a separately approved P-track federation.

README.md

@@ -2,63 +2,214 @@
 language:
   - ko
   - en
-library_name: scikit-learn
+library_name: pytorch
 license: other
+pipeline_tag: feature-extraction
 tags:
-  - handwriting
-  - mathematical-expression-recognition
   - online-handwriting
-  - segmentation
-  - research
+  - mathematical-expression-recognition
+  - trajectory
+  - temporal-convolution
+  - on-device
+  - pytorch
 ---
 
-# AIFlow Math Ink 0.6 — Intermediate Research Snapshot
+# AIFlow Math Ink 0.6
+
+AIFlow Math Ink 0.6은 수학 필기를 **이미지보다 point/stroke sequence로 먼저 처리하는** 온디바이스 연구 모델이다. 원본 touch event를 보존하면서 모델 입력만 6Hz canonical tap으로 재표본화하고, 각 고립 기호의 378-class top-k와 visual-family 확률을 반환한다.
+
+현재 공개본은 seed 17·31·47의 연구 checkpoint와 grouping/behavior head를 포함한다. 전체 수식 LaTeX decoder나 완성된 Android LiteRT 배포본은 아니다.
+
+![AIFlow Math Ink 0.6 architecture](assets/architecture.svg)
 
-이 저장소는 AIFlow Math Ink 0.6의 **중간 R-track 연구 스냅샷**이다. 전체 LaTeX OCR 또는 상용 ODA 모델이 아니며, 수식 stroke grouping과 행동 문맥 실험의 재현 자료를 보존한다.
+## 핵심 입력 계약
 
-## 이번 스냅샷의 결론
+![Stroke encoding](assets/stroke_encoding.svg)
 
-- protected multi-stroke family selector 기준 CROHME official test exact partition: **60.04%**
-- boundary behavior guard 적용 exact partition: **60.25%**
-- pair-F1: **91.07% → 91.26%**
-- overmerge formula rate: **21.72% → 20.49%**
-- validation에서 고정한 boundary 설정: probability threshold `0.5`, logit penalty weight `6.0`
-- 전체 로컬 Python 회귀: **271 passed**
+실제 펜 입력은 다음 순서로 처리한다.
 
-Boundary head는 grouping 전에 다획 후보가 둘 이상의 정답 기호 경계를 침범하는지 예측한다. 규칙형 component competition과 이미 grouping된 partition 이후의 local-baseline relation guard는 채택되지 않았다.
+1. 기기의 모든 touch event를 원본 timestamp와 함께 메모리에 보존한다.
+2. stroke별 시작점·끝점·pen-up을 필수 anchor로 남긴다.
+3. 모델용 temporal view만 6Hz로 재표본화한다.
+4. 종횡비를 보존해 128×128 ink space에 중앙 정규화한다.
+5. 최대 128 event, 19개 feature를 `128×19` tensor로 만든다.
 
-## 디렉터리
+19개 channel:
 
-- `artifacts/`: scikit-learn boundary 연구 head
-- `reports/`: validation/test 지표와 실패 감사
-- `scripts/`: 학습·sweep·감사 entry point
-- `src/`: 이번 실험에 직접 관련된 selector/관계 코드 snapshot
+```text
+shape_x, shape_y,
+canvas_x, canvas_y,
+direction_x, direction_y,
+curvature, pen_up, stroke_progress,
+aspect_ratio,
+bbox_top, bbox_bottom, bbox_height, center_y,
+baseline_available,
+time_delta, speed, missing_mask, source_modality
+```
+
+- timestamp가 실제로 있으면 `timestamp_mode=observed`
+- 정적 이미지처럼 시간이 없으면 `timestamp_mode=canonical`, `missing_mask=1`
+- 추정 시간을 실제 관측 시간처럼 저장하지 않는다.
+
+## 모델 구조
+
+```text
+Online stroke
+  → 6 Hz canonical taps (128×19)
+  → online dual-TCN adapter
+  ────────────────────────────────────────────┐
+                                               │
+Raster 128×128                                 │
+  → spatial encoder                            │
+  → causal virtual-trajectory decoder          │
+  → top-4 stroke hypotheses                    │
+  ────────────────────────────────────────────┤
+                                               ▼
+                                shared residual TCN (hidden=128)
+                                     ├─ exact head: 378 labels
+                                     ├─ family head: 324 families
+                                     └─ top-k probabilities
+
+Local formula context + stroke tensor
+  → 49-d context MLP + stroke TCN
+  → identifier_lower / identifier_upper / multiply_operator
+
+Segmentation lattice geometry
+  → boundary behavior head
+  → candidate crosses a symbol boundary?
+```
 
-## 사용 제한
+Raster 경로도 최종 label shortcut을 사용하지 않는다. 이미지에서 top-4 virtual stroke를 만든 후 동일 trajectory encoder로 다시 인식한다.
 
-이 스냅샷은 **연구 전용**이다.
+## 공개 checkpoint
 
-- `boundary_behavior_guard.joblib`은 CROHME 정답 grouping으로 학습된 R-track 산출물이다.
-- 상용 제품, 배포 checkpoint, teacher 또는 distillation 입력으로 사용하지 않는다.
-- CROHME/HWRT 원본 데이터, 필기 표본, OCR cache는 이 저장소에 포함하지 않는다.
-- 상용 전환은 동일 boundary target을 상용 허용 P-track 연속식 데이터로 다시 학습해야 한다.
-- 개별 외부 데이터셋��� 원 라이선스와 사용 조건은 사용자가 별도로 확인해야 한다.
+각 seed는 세 파일로 구성된다.
+
+```text
+models/
+  seed17/
+    base_378.pt
+    online_adapter.pt
+    behavior_role_head.pt
+  seed31/
+    ...
+  seed47/
+    ...
+artifacts/
+  boundary_behavior_guard.joblib
+```
 
-## 보안 주의
+| 파일 | 역할 | 주요 계약 |
+|---|---|---|
+| `base_378.pt` | 공통 trajectory/raster base | hidden 128, top-4 hypothesis, 378 exact, 324 family |
+| `online_adapter.pt` | 실제 온라인 stroke 보정 | `dual_tcn_v3`, `top4-skeleton-128x19` |
+| `behavior_role_head.pt` | `x/X/×` 역할 문맥 | 128×19 stroke + 49 context feature |
+| `boundary_behavior_guard.joblib` | 잘못된 다기호 병합 억제 | geometry 17 feature, threshold 0.5, weight 6 |
 
-`joblib`/pickle 계열 파일은 임의 코드를 실행할 수 있다. 이 저장소와 파일 checksum을 신뢰하는 환경에서만 로드한다.
+세 seed teacher를 그대로 모바일에 넣는 것이 최종 목표는 아니다. release 경로는 seed ensemble을 하나의 student로 distillation한 뒤 LiteRT INT8/FP16을 비교하는 것이다.
+
+## Checkpoint metadata 확인
+
+아래 코드는 network를 실행하지 않고 checkpoint 계약을 확인한다.
 
 ```python
-import joblib
+from pathlib import Path
+import torch
+
+root = Path("models/seed17")
+
+base = torch.load(root / "base_378.pt", map_location="cpu", weights_only=False)
+adapter = torch.load(root / "online_adapter.pt", map_location="cpu", weights_only=False)
+role = torch.load(root / "behavior_role_head.pt", map_location="cpu", weights_only=False)
+
+print(base["model_version"])
+print(base["max_events"], base["sample_hz"])
+print(len(base["feature_names"]), len(base["exact_labels"]), len(base["family_labels"]))
+print(adapter["adapter_architecture"], adapter["feature_contract"])
+print(role["role_labels"], role["context_features"])
+```
+
+`state_dict`를 실제 module에 적재하려면 이 저장소의 snapshot 코드뿐 아니라 전체 AIFlow 연구 코드가 필요하다. checkpoint 내부 경로는 lineage 기록이며 로컬 절대경로에 의존해 추론하지 않는다.
+
+## 현재 성능
+
+### 378-label trajectory classifier
 
-payload = joblib.load("artifacts/boundary_behavior_guard.joblib")
-print(payload["schema"])
-print(payload["feature_names"])
-print(payload["threshold"], payload["weight"])
+최신 online case-context seed 17 기준:
+
+| split | top-1 | top-5 | family top-1 |
+|---|---:|---:|---:|
+| writer validation, 4,261 samples | 86.13% | 99.48% | 92.94% |
+| paired writer-disjoint test, 3,782 samples | 82.87% | 97.73% | 91.22% |
+
+Paired source는 HWRT 내부 writer hash split과 UJI Pen v1/v2 writer-disjoint 계약을 사용했다. HWRT official test는 거대 writer 중복 때문에 제외했다.
+
+### 수식 행동 head
+
+정답 symbol grouping 이후의 CROHME 조건부 역할 평가:
+
+| 지표 | 3-seed 평균 |
+|---|---:|
+| role accuracy | 93.33% |
+| macro-F1 | 76.41% |
+| lowercase identifier recall | 95.66% |
+| uppercase identifier recall | 55.91% |
+| multiplication recall | 88.89% |
+| ECE | 4.88% |
+
+### Grouping boundary head
+
+| 지표 | 이전 | boundary 적용 |
+|---|---:|---:|
+| CROHME exact partition | 60.04% | 60.25% |
+| pair-F1 | 91.07% | 91.26% |
+| overmerge formula rate | 21.72% | 20.49% |
+
+## 출력 범위
+
+의도한 모바일 API:
+
+```text
+recognizeOnline(strokes, canvas) → SymbolResult
+recognizeRaster(bitmap)          → SymbolResult
+
+SymbolResult:
+  candidates[{token, probability}]
+  confidence
+  modelVersion
+  latencyMs
 ```
 
-## 재현 경계
+이미지, raw stroke, virtual stroke는 서버 payload로 전송하지 않는다. virtual hypotheses는 로컬 debug에서만 노출한다.
+
+## 알려진 한계
+
+- 378-label paired writer/device-disjoint top-1 목표 92%에 아직 미달한다.
+- 전체 수식 LaTeX, Tray decoder, gridding은 0.7 범위다.
+- uppercase 역할 recall과 `O/0`, styled-letter hard family가 남은 병목이다.
+- raster virtual-stroke 경로는 digit/Greek slice에서는 개선됐지만 378-label release gate를 통과하지 못했다.
+- boundary/behavior head는 CROHME R-track 학습물이므로 제품 weight로 distill할 수 없다.
+- Android LiteRT 변환, PyTorch/LiteRT logit parity, 저가·중급·고급 기기 benchmark가 남아 있다.
+
+## 데이터와 권리
+
+이 저장소에는 원본 필기 데이터, 이미지, OCR cache를 포함하지 않는다.
+
+- HWRT database: ODbL-1.0 취급, attribution 및 파생 database 검토 필요
+- UJI/Pendigits 계열: 각 원 출처 조건을 별도로 따라야 함
+- CROHME/MathWriting: 비상업 R-track 평가 또는 연구 head에만 사용
+- checkpoint와 report는 현재 **research-only / non-commercial** 공개물이다.
+- 상용 checkpoint는 권리 검토를 통과한 P-track 데이터로 처음부터 재학습해야 한다.
+
+이 공개는 제품 정확도·상용 배포 가능성·LiteRT 호환성을 보증하지 않는다.
+
+## 보안
+
+PyTorch checkpoint와 joblib/pickle은 신뢰할 수 없는 출처에서 로드하면 임의 코드를 실행할 수 있다. `MANIFEST.json`의 SHA-256을 확인하고 신뢰된 환경에서만 사용한다.
 
-포함된 script는 전체 AIFlow 프로젝트와 사용자가 별도로 확보한 데이터셋이 있어야 완전 실행된다. 공식 test는 validation 선택에 사용하지 않았지만, 반복 연구 과정에서 test 실패를 관찰했으므로 여기에 기록된 결과는 독립 제품 인증 수치가 아니다.
+## 연구 자료
 
-자세한 설계·실패 분석·후속 P-track 계획은 `reports/RESEARCH_REPORT.md`를 참고한다.
+- 전체 기술 보고서: [`reports/RESEARCH_REPORT.md`](reports/RESEARCH_REPORT.md)
+- boundary 학습 결과: [`reports/boundary_behavior_guard_report.json`](reports/boundary_behavior_guard_report.json)
+- 3-seed 행동 head: [`reports/behavior_role_3seed_summary.json`](reports/behavior_role_3seed_summary.json)
+- 파일 checksum: [`MANIFEST.json`](MANIFEST.json)

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+}

reports/behavior_role_seed31.json

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+}

reports/behavior_role_seed47.json

@@ -0,0 +1,1342 @@
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+}

reports/online_casecontext_seed17.json

@@ -0,0 +1,1086 @@
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