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+# 131장 자료의 새 Figure 1–6 / Supplementary 분류
+
+> **페이지 번호 안내:** 이 문서의 쪽수는 재배치 전 131장 덱 기준이다. 실제 PPTX/PDF는 사용자 정의에 맞게 재배치됐으며, 현재 쪽수는 [페이지 대응표](../figure_enrichment_20261013/page_map.csv)와 [수정 보고서](../figure_enrichment_20261013/README.md)를 따른다.
+
+사용자가 2026-10-13에 다시 정의한 여섯 Figure를 기준으로 분류했다. **아래 쪽수는 131장 확장 덱의 쪽수**다. 덱 안의 기존 Figure 번호와 계획표는 이전 기획이다. 원본 PPTX/PDF는 수정하거나 삭제하지 않았다.
+
+- [131장 전체 분류표](slide_assignment.csv): 페이지별 주배정, 데이터 종류, 사용 조건, 필요한 수정, 원본 76장 번호.
+- [패널 구성안](panel_plan.json): Figure별 패널과 재사용 범위.
+- [사용자 정의](AUTHOR_SCOPE.md): 앞으로 적용할 Figure 1–6의 범위.
+
+## 본문 Figure 구성안
+
+| Figure | 본문에 넣을 자료 | 현재 페이지 | 상태 / 경계 |
+|---|---|---|---|
+| **1** | 전체 실험 흐름, corpus 통계, dataset별 규모와 K 분포 | **9, 11, 16 하단** | 기존 자료 재사용. 전체 corpus와 training subset의 분모를 구분 |
+| **2** | architecture, gene context/panel ablation, 공통 유전자 축의 비교 | **13, 14, 15** | **SLE pseudobulk**만 사용. 12의 corpus validation loss는 보충 |
+| **3** | healthy mismatch → full FT 개선 → LoRA rank 비교 → paired 수 ablation → exact-five 성능 | **21, 23, 116의 ImMicro pooled, 51, 49, 54**; **55** 선택 | 모두 healthy paired. 49의 추가 validation과 54의 exact-five를 구분 |
+| **4** | 질환 cohort, LoRA-five/5-fold의 logFC·DEG·CAT, deconvolution 비교 | **62 질환 행, 63, 67, 68, 71** | 핵심 정량 결과. 비교 단위와 cohort별 예외를 유지 |
+| **5** | paired sample의 real/LoRA/scSemi rare population, marker, donor별 사례 | **직접 충족하는 완성 슬라이드는 현재 없음** | 기존 생성 결과로 정성 패널 조립. 89, 91–93, 95–96은 정량 보조 자료 |
+| **6** | paired adaptation → bulk-only inference → 기존 논문의 특정 결과 재현 | **TODO**; 설계 참고 **104, 106, 107** | 재현 결과 패널은 아직 없음. 105는 과거 탐색 기록 |
+
+## Figure 1 — 전체 실험 개요와 corpus
+
+- **1a / 9쪽:** pretraining → paired adaptation → single-cell generation의 전체 실험 흐름.
+- **1b / 11쪽:** corpus 규모 및 assay, condition, cell-type 분포. cell·donor·dataset의 분모를 명시한다.
+- **1c / 16쪽 하단:** dataset별 corpus 규모와 recipe-size 분포. 상단 workflow는 9쪽과 중복되므로 Methods/S01로 이동한다.
+
+8, 10, 17, 38–41쪽의 세부 방법은 Methods/보충으로 배치한다. 필요하면 62쪽의 cohort 요약을 작은 evaluation inset으로 재사용한다. Corpus의 자연적 구성과 학습 sampler의 노출 비율은 별도 통계다.
+
+## Figure 2 — SLE pseudobulk에서 architecture와 gene context
+
+- **2a / 13쪽:** architecture와 conditioning width에 따른 SLE pseudobulk 성능.
+- **2b / 14쪽:** 2k/3k/6k/15k gene context 비교. 6k가 모든 지표에서 최고라는 표현 대신 성능과 biological gene coverage를 함께 보여준다.
+- **2c / 15쪽:** 공통 1,006-gene 축의 precision/coverage 및 composition 비교. Architecture의 1% floor와 panel의 0.1% floor 실험군은 나누어 해석한다.
+
+**12쪽**은 corpus pretraining validation loss이므로 SLE benchmark 성능으로 부르지 않는다. **18, 72–75쪽**의 conditioning intervention, per-cell-type SLE, broadcast 및 split-half controls는 S02로 분류한다. 특히 72–75쪽은 이전 덱의 위치와 관계없이 새 Figure 4에 들어가지 않는다.
+
+## Figure 3 — healthy paired adaptation의 필요성과 효율
+
+패널 흐름은 **21 → 23 → 116의 healthy 부분 → 51 → 49 → 54**로 잡는다. 55쪽의 donor별 composition 오차는 선택 패널이다.
+
+**Full FT의 개선은 raw zero-shot과 비교하면 지지된다.** ImMicro pooled 결과의 precision/coverage/L1은 raw에서 **0.6804/0.1918/0.4549**, full FT에서 **0.8895/0.6954/0.1572**다. Calibrated zero-shot의 precision은 0.9050으로 full FT보다 높으므로, 모든 baseline의 모든 지표가 개선됐다고 표현하지 않는다.
+
+| ImMicro pooled CV | Precision ↑ | Coverage ↑ | Composition L1 ↓ | Trainable parameters |
+|---|---:|---:|---:|---:|
+| Full FT | 0.8895 | 0.6954 | **0.1572** | 54.185M |
+| LoRA r4 | **0.9333** | 0.7123 | 0.1858 | **0.324M** |
+| LoRA r16 | 0.9241 | 0.7843 | 0.1617 | 1.296M |
+| LoRA r64 | 0.9044 | **0.8159** | 0.1610 | 5.185M |
+
+**현재 결과로 r16을 모든 지표에서 가장 좋은 rank라고 단정할 수는 없다.** r4는 precision, r64는 coverage와 LoRA 중 L1에서 앞선다. r16은 r64보다 학습 파라미터가 4배 적고 precision이 높으며 관측 L1 차이가 작은 절충안이다. 이는 통계적 동등성을 입증한 결과가 아니다. 5 training + 7 validation 조건에서도 r4는 L1, r64는 coverage, r16은 precision에서 가장 좋아 순위가 나뉜다.
+
+권장 설명: **“성능과 학습 파라미터 수의 균형을 고려해 rank 16을 선택했다.”** 이 선택 이유는 제안이며, 사전에 정한 종합 선택 기준이 있었는지는 별도로 확인해야 한다.
+
+- **116쪽:** `dataset=immicro, call=pooled`만 추출해 raw/calibrated/full FT/LoRA를 비교한다. Sepsis나 22명 fold0의 condition-encoder pilot을 107명 pooled 결과와 합치지 않는다.
+- **51쪽:** healthy full FT/rank/parameter 비교의 주패널. 50쪽의 rank별 곡선은 S06에서 보완한다.
+- **49쪽:** n training + 7 validation 및 early stopping 곡선이다. n=5를 “총 5 paired만 사용”으로 읽을 수 없다.
+- **54쪽:** 5 pairs, 추가 validation 없음, fixed 3,000 steps의 exact-five 비교다. **“5 paired로도 성능을 확보한다”의 핵심 근거는 이 패널**이다. 5 support draws와 각 draw의 나머지 102 donors를 구분한다.
+- **56쪽:** historical scSemi budget curve에는 CV-HVG 재사용이 있어 완전히 동일한 정보 접근량의 비교로 본문에 쓰지 않는다.
+- **23쪽:** mismatch 진단의 native-depth scorer가 FT benchmark와 다르다. 그림 사이의 precision을 동일 기준의 before/after 수치로 직접 연결하지 않는다.
+
+## Figure 4 — 질환 신호 보존의 정량 비교
+
+**62 → 63 → 67 → 68 → 71쪽**을 중심으로 배치한다. 62에서는 질환 행을 사용한다. 63/67의 logFC correlation, bulk-controlled partial correlation, DEG를 나누고, five-donor와 five-fold 설계를 같은 축으로 표시하되 합쳐 평균하지 않는다. 다중 방문 cohort에서 five donors는 five samples와 다르다.
+
+**현재 수치는 “모든 질환·지표에서 최고”를 지지하지 않는다.** 예를 들어 five-donor Sepsis의 logFC r은 scSemi 0.4025 > LoRA 0.3501, five-fold Giroux도 scSemi 0.5373 > LoRA 0.4610이다. Sepsis의 deconvolution DEG overlap은 BayesPrism 27.0/CIBERSORTx 25.4 > LoRA 16.4다. 이 예외를 본문 그림에서 제거하지 않는다.
+
+기존 20개 five-donor draw에서는 LoRA가 scSemi보다 logFC r **13/20**, partial r **14/20**에서 높고, DEG는 **12승 + 8동률**이다. 단, support draws는 독립적인 환자 반복이 아니다. 현재 지지되는 결론은 **여러 질환 cohort에서 cell-type별 질병 신호를 보존하며 많은 비교에서 우수하지만, cohort와 endpoint별 예외가 있다**는 범위다.
+
+**68쪽에 빠진 scSemi five-fold CAT는 새 실험이 필요 없다.** 저장된 `scsemi_cv_20261002/pooled/cat.csv`의 `predictor=generated` 행에서 가져올 수 있다. CAT100의 LoRA/scSemi 값은 Sepsis 0.1625/0.0450, Imperial 0.2700/0.2250, Giroux 0.0675/0.0675, Ji 0.1525/0.0525다. Bulk-broadcast 행을 섞지 않는다.
+
+- **69–70쪽:** patient-aware corrected DEG의 보조 근거. 현재 덱은 Sepsis 5 draws/Imperial 1 draw의 고정 snapshot이며 모든 cohort의 완료판은 아니다.
+- **64–66, 110–115쪽:** cell realism/composition 등 생성 품질. Disease integrity의 직접 증거와 구분해 S07에 둔다.
+- **82–85, 120–122쪽:** rank, pair count, K=4,000, deconvolution coverage의 감도 분석은 S08.
+- **71쪽:** deconvolution이 지원하는 cell-type expression endpoint에서 비교한다. 공통 gene axis, 가용 draw 수와 NA를 표시한다.
+
+## Figure 5 — paired rare population의 정성적 재현
+
+현재 131장에는 **같은 held-out paired sample의 real/LoRA/scSemi rare-population identity를 직접 대조하는 완성 패널이 없다.** 89, 91–93, 95–96쪽은 빈도·miss·상관·방향의 정량 자료다. 97/99는 pseudobulk 조건, 98/100/101은 주로 unmatched Behcet reference라서 이 목적을 대신하지 못한다. 77–80은 paired COVID 자료지만 zero-shot/calibration의 parent-population 진단이다.
+
+기존 출력으로 다음 패널을 조립할 수 있다. 아래 사례는 후보이며, 성공 여부를 보고 사례를 고르는 대신 관측 자료 기준의 선택 규칙을 먼저 고정한다.
+
+1. **5a:** 같은 held-out sample의 observed/LoRA/scSemi를 공통 observed-reference 공간에 나란히 표시하고, 사전에 정의한 rare state를 강조한다.
+2. **5b:** 동일 marker의 feature plot과 co-detection으로 population identity를 보여준다.
+3. **5c:** case/control donor별 small multiples로 반복성을 확인한다. Donor/visit 단위를 유지한다.
+4. **5d:** Sepsis MS1-like와 cycling monocyte 등 서로 다른 재현 양상을 함께 제시하고, Imperial cycling T/NK 등 독립 cohort로 확장한다.
+
+**이 공백이 곧 새 학습/생성이 필요하다는 뜻은 아니다.** 저장된 marker cache와 관측/생성 세포를 먼저 이용한다. Marker-space PCA/UMAP는 whole-transcriptome embedding과 다르며, 정의에 쓴 marker의 분리만으로 독립적인 identity 검증이 되지는 않는다. Bulk–sc pairing도 real/generated 개별 cell의 일대일 pairing을 의미하지 않는다. 여기서 “발견”은 우선 관측된 rare state의 회복/식별로 표현한다.
+
+기존 재료 위치(모두 Model 8 아래):
+
+- `plans/rare_states_paired_20261008/work/<cohort>/drawN/{lora5,scsemi5,zs_raw,zs_cal}.npz`
+- `plans/paired_finetune_intradataset_20260927/data/<cohort>/{cells_panel6k.npz,cells_obs.parquet,samples.csv,folds.csv}`
+- `plans/paired_finetune_intradataset_20260927/scoring_cache/<cohort>/observed_labels/labels/labels.parquet`
+- `plans/rare_states_screen_20261004/tools/states.json`
+- `plans/paper_figures_strictpairs_20261012/tools/{rare_cache_builder.py,export_rare_visuals.py}`
+
+확인 시점에 strict visual cache, coordinate CSV, 최종 qualitative PDF는 없었다. 전체 campaign exporter의 20-unit 완료 조건을 우회하지 않는다. 필요하면 검증된 subset의 범위를 명시한 별도 illustration으로 만든다.
+
+## Figure 6 — bulk-only 자료에서 기존 논문 결과 재현
+
+**TODO를 유지한다.** 재현할 논문의 특정 결과, adaptation pairs, bulk-only targets, 재현 endpoint와 독립적인 보조 근거를 먼저 고정한다. 104/106/107쪽은 설계 참고 자료이고 105쪽은 과거 탐색 기록이다. 기존 탐색의 “0 confirmed”는 특정 논문 결과 재현의 성공/실패를 뜻하지 않는다.
+
+Bulk-only sample에 sc partner가 없더라도 같은 donor의 다른 paired visit가 학습에 쓰였을 수 있으므로 자동으로 donor-held-out이라고 표기하지 않는다.
+
+## Supplementary 후보군
+
+다음S번호는**주제별분류용임시번호**다. 각그룹이그대로한장의SupplementaryFigure가된다는뜻은아니다.
+
+- **S01 — 모델·conditioning·학습·평가 설계 상세:** 8, 10, 17, 38, 39, 41쪽. Figure1/2/3를 보완; 경험적 SLE/paired benchmark와 구분
+- **S02 — SLE pseudobulk 모델 검증과 대조군:** 12, 18, 72, 73, 74, 75쪽. 12쪽은 corpus validation loss, 나머지는 SLE development. paired disease Figure4에 넣지 않음
+- **S03 — paired 입력 mismatch와 calibration 상세:** 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37쪽. sc-derived depth/masks,10-pair calibration,AIFI/pretraining overlap을 명시. strict-five 결과와 합치지 않음
+- **S04 — 평가 단위·annotation·depth/readout 검증:** 40, 42, 43, 46, 47쪽. technical repeat와 follow-up, annotation taxonomy, sequencing depth를 각각 구분
+- **S05 — healthy paired 재구성·FT mode·대표 샘플 대조:** 44, 45, 48, 57쪽. 48의 condition-encoder는 fold0 pilot. 57은 literal cell copying이 아닌 composition dependence
+- **S06 — rank·pair budget·K·시간 상세:** 50, 56, 58, 59, 117, 118, 119쪽. Fig3 재사용 시 healthy만 추출. historical n+validation/CV-HVG와 exact-five를 구분; r16 universal-best 금지
+- **S07 — 질환/다중 cohort 생성 품질과 비교 안정성:** 64, 65, 66, 110, 111, 112, 113, 114, 115쪽. 질환 신호 자체의 증거와 구분. strict6-unit snapshot과 historical designs를 혼합평균하지 않음
+- **S08 — 환자 단위 DEG·질환 sensitivity·deconvolution/K4000:** 69, 70, 82, 83, 84, 85, 120, 121, 122쪽. 69는 Sepsis5draw,70은 Imperial1draw의 고정 snapshot. 전체 cohort 완성 결과처럼 제시하지 않음
+- **S09 — 외부 질환 real-bulk diagnostic:** 76, 77, 78, 79, 80, 81쪽. LoRA-five/scSemi paired rare-state 증거가 아님. Figure4 핵심 adaptation 비교와 분리
+- **S10 — paired rare-state 정량 검증과 case 선택 근거:** 89, 91, 92, 93, 95, 96쪽. Fig5 정성 사례를 뒷받침하되 빈도/상관 heatmap을 population-identity 그림으로 대체하지 않음
+- **S11 — pseudobulk rare screen·unmatched Behcet 한계:** 97, 98, 99, 100, 101쪽. Fig5의 독립 paired qualitative validation으로 사용하지 않음
+- **S12 — pretraining·cross-cohort·scratch 대조:** 123, 124, 125, 126, 127, 131쪽. 131의 다른 tissue 전이는 전망이며 실제 검증 결과가 아님;127은 rare 정량 대조
+- **S13 — scSemi output-threshold sensitivity:** 129쪽. 본문은 official output. 수정 readout은 별도 sensitivity로 표기
+- **S14 — bulk-only target cohort 차이:** 106쪽. Fig6 설계용 shift 점검; published finding의 재현 결과가 아님
+
+## 전체 페이지 처리
+
+| 분류 | source pages |
+|---|---:|
+| 본문 후보: 부분 추출·조건부 사용 포함 | 18 |
+| Supplementary 후보 | 86 |
+| Figure 6 설계/TODO: 104, 107쪽 | 2 |
+| 논문 증거로 사용 보류: 과거 rare scorer의 88, 90, 94쪽 | 3 |
+| 과거 workflow 기록: 105쪽 | 1 |
+| 표지·구분·이전 계획·읽기 안내 | 21 |
+| **합계** | **131** |
+
+분류는 131장 모두에 하나의 주배정을 부여하며 부분 재사용은 별도 열에 적었다. 원본 덱의 축소·삭제·재정렬은 하지 않았다. 새 학습, inference, scoring 또는 통계 검정도 실행하지 않았다.
+
+## 주요 근거 파일
+
+`data/`는 이 덱 폴더 아래, `plans/`는 Model 8 아래 경로다.
+
+- `data/expanded_master_20261013/spec.json`: 현재 131장 내용과 protocol.
+- `data/sec07_rank_sweep.csv`, `data/sec07_rank_sweep_runs.csv`, `data/sec07_ft_modes_accuracy.csv`: healthy paired rank/FT.
+- `plans/paper_figures_strictpairs_20261012/source_data/fig2_historical_rank_full.csv`: full FT/rank summary.
+- `data/fig4_disease.csv`, `data/sec09_cat_curves.csv`, `data/secS_deconvolution.csv`: 질환 비교.
+- `plans/paired_finetune_intradataset_20260927/scores_contrast/<cohort>/scsemi_cv_20261002/pooled/cat.csv`: 빠진 CV-CAT를 채울 기존 결과.
diff --git a/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_assignment_20261013/STRENGTHENING_PROPOSAL.md b/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_assignment_20261013/STRENGTHENING_PROPOSAL.md
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+# Figure 1–6 / Supplementary 근거 보강 제안
+
+> **페이지 번호 안내:** 이 문서의 쪽수는 재배치 전 131장 덱 기준이다. 실제 PPTX/PDF는 사용자 정의에 맞게 재배치됐으며, 현재 쪽수는 [페이지 대응표](../figure_enrichment_20261013/page_map.csv)와 [수정 보고서](../figure_enrichment_20261013/README.md)를 따른다.
+
+2026-10-13. 사용자의 새 Figure 정의를 기준으로 한 **논의용 제안**이다. 새 실행 승인이나 기존 campaign의 변경 지시가 아니다. 이번 검토에서는 학습·추론·scoring·그림 생성을 실행하지 않았다.
+
+## 우선순위와 중복 방지
+
+가장 큰 보강 효과는 Figure 4의 환자 단위 비교, Figure 5의 독립적인 rare-state identity 확인, Figure 6의 구체적인 논문 결과 재현에서 나온다. Figure 1–2는 기존 자료의 사용 범위와 비교 조건을 명료하게 보여주는 일을 먼저 한다.
+
+- **기존 자료 재구성:** corpus/exposure 지도, SLE 공통 축 ablation, healthy full FT/rank 비교, 누락된 scSemi CV-CAT, robustness summary.
+- **이미 진행 중인 일의 완료:** strict total-support curve 및 질환 patient-aware 비교. 별도의 중복 campaign을 만들지 않는다.
+- **새 CPU 분석·그림:** paired rare-state 정성 패널과 독립 marker 검증, 기존 bulk-only 결과의 논문 endpoint 재현. 분석의 새로움과 model inference의 새로움을 구분한다.
+- **조건부 새 GPU 작업:** 기존 근거로 해소되지 않는 명시적 질문이 있을 때만 제한된 sampling/adapter-seed 보완 또는 adapted-model input intervention. 아래 기본 계획에는 새 대규모 학습을 넣지 않는다.
+
+검토 중 상태(13:58 KST 전후): server_1은 Imperial draw1의 scSemi target, server_2는 healthy fold4/n10의 scSemi target을 처리 중이었다. 질환 완료 score receipts는 Sepsis 5 draws + Imperial draw0이며, 최종 cross-draw 통계는 아직 없었다. Healthy는 20개 support unit이 이미 계획되어 있고, 로컬에서 11개 generation triples 및 n5 두 unit의 scoring summary를 확인했다. 이는 시간에 따라 변하는 관측이며 새 실행 전에 live receipts를 다시 확인한다.
+
+## Figure 1 — 규모뿐 아니라 데이터 사용 경계를 보여준다
+
+**추가할 그림:** corpus 구성 통계 옆에 `pretraining / calibration reference / paired adaptation / held-out paired evaluation / bulk-only application`의 데이터 사용 지도를 둔다. 상세 donor·sample·study 교차표는 Supplementary Table로 보낸다.
+
+**이유:** corpus 표는 이미 있지만, 어떤 데이터가 언제 모델과 평가에 노출됐는지를 한눈에 볼 수 있어야 한다. 기존 corpus 문서에는 27명이 내부 corpus split을 가로지르는 기록이 있다(26 GSE274680, 1 GSE132802; 38 validation/test samples). 이는 **외부 SLE/paired test 환자의 누출을 입증하는 자료가 아니다**. AIFI reference의 85/108 donor가 사전학습에 포함된 것도 calibration reference 노출과 target-patient 중복을 구분해야 한다.
+
+**작업:** metadata와 최종 checkpoint/corpus 계약을 연결하고, 표를 `분리 확인 / 중복 확인 / identity 미확인`으로 구분한다. Sample 불일치만으로 donor 독립을 주장하지 않는다. 실제 validation recipe에 해당 세포가 사용됐는지는 별도 linkage 문제다.
+
+**완료 기준:** 각 성능 패널에서 train/support/reference/test의 데이터 접근 범위와 고유 donor 수를 추적할 수 있다. 알려진 내부 corpus 중복을 근거 없이 외부 benchmark 누출로 확대하지 않는다. 실제 target 중복이 발견되면 기존 출력에서 평가 대상을 재정의할 수 있는지 먼저 확인한다.
+
+**종류:** metadata audit + 도표. 새 학습 없음. 현재 9·11·16쪽 보강.
+
+## Figure 2 — 동일 조건의 SLE 비교와 불확실성
+
+**추가/개선할 그림:** architecture와 gene context를 두 panel family로 분리한 점·오차막대 plot. 오차막대는 기존 donor L1 분산과 sampling-seed 변동 등 근거가 있는 경우에만 표시한다. 단일 pooled P/C 값에는 기존 CI가 없으며, 필요하면 donor-aware CPU 분석을 별도 작업으로 둔다. 공통 SLE 42 donors / 1,006 genes로 이미 계산된 P/C/composition을 사용하고, 기존 logFC/DEG benchmark는 별도 gene-axis 정보를 함께 표시한다. Real-versus-real sampling control을 기준선으로 둔다.
+
+**이유:** architecture의 1% detection floor와 gene-panel의 0.1% floor를 합쳐 순위를 매기면 비교 조건이 흐려진다. 6k의 5 seeds는 **sampling seeds이며 독립 pretraining 5회가 아니다**.
+
+**완료 기준:** architecture 내부, gene-context 내부 비교가 각각 동일 평가 조건을 쓰고, donor·sampling seed·training seed의 수를 구분한다. Development benchmark에서의 선택을 독립적인 외부 검증으로 부르지 않는다.
+
+**종류:** 기존 결과 재시각화. 13–15쪽과 `ablation_pretrain_*` source tables 재사용. 모든 architecture를 재학습할 필요는 없다.
+
+**조건부 추가:** 특정 architecture/gene panel의 안정적 우위를 논문의 주요 주장으로 삼아야 할 때만 가장 가까운 경쟁 checkpoint에 sampling seeds를 보완한다. 학습 안정성 자체의 주장은 별도의 training-seed 반복이 필요하며 sampling 반복으로 대신할 수 없다.
+
+## Figure 3 — real-bulk 문제와 적은 paired 수로 해결되는 범위
+
+**3A: 같은 평가 기준의 before/after.** Healthy paired raw zero-shot, calibrated zero-shot, full FT, LoRA를 한 축에 나란히 표시한다. 지표는 precision, coverage, composition L1을 각각 유지한다. 23쪽 native-depth 진단과 116쪽 pooled FT benchmark 수치를 직접 연결하지 않는다.
+
+**3B: rank 선택의 근거.** r4/r16/r64/full FT에 대해 parameter–quality plot을 만든다. Precision과 coverage를 분리하고 composition error를 함께 보여준다. r16은 성능·파라미터 절충안으로 평가하며, r16을 이기게 하려고 사후 가중 종합점수를 만들지 않는다.
+
+**3C: strict total-support curve.** 5/10/20/40 pairs × 5 folds, 추가 validation 없음, fixed 3,000 steps의 기존 campaign을 완료한다. 각 method에 동일 support/test와 허용된 feature-selection 정보를 적용한다. 49쪽의 n+7 validation 곡선은 historical sensitivity, 54쪽은 기존 exact-five 근거로 유지한다.
+
+**완료 기준:** 총 접근 paired 수가 x축과 일치하며, 동일 donor의 방법 간 차이를 보여준다. 불확실성은 support/seed variation과 held-out donor variation을 구분한다. 최종 결론은 해당 fixed-step 학습 절차에서의 데이터 효율이지 모든 최적화 절차의 최소 paired 수가 아니다.
+
+**종류:** 3A/3B는 기존 결과 재구성. 3C는 **기존 진행 작업의 완료**다. 이미 생성된 n5 출력은 scoring/조립으로 이어가며 재생성하지 않는다.
+
+## Figure 4 — 핵심 disease-integrity 주장을 가장 먼저 보강
+
+**4A: 환자 단위 효과 비교 그림.** Cohort × cell type별로 `PBISC − comparator`의 logFC correlation 차이와 CI를 보여주는 forest plot을 만든다. scSemi를 주 비교로 두고 deconvolution은 지원하는 expression endpoint에서 별도로 비교한다. 5-fold와 exact-five를 분리하며, PBISC가 뒤지는 결과도 표시한다.
+
+**4B: 서로 다른 관점의 질병 신호.** LogFC r를 주 지표로 두는 안을 제안하며, DEG 방향 일치·CAT curve·partial correlation은 서로 다른 보조 질문으로 둔다. 최종 주/보조 지표 선택은 재분석 전에 고정한다. 저장된 scSemi five-fold CAT를 추가한다. 생성 세포 수를 생물학적 n으로 사용하지 않는다.
+
+**4C: 통계 단위와 정보 접근량.** 기존 patient-aware contrast/DEG 및 cross-draw donor CI pipeline을 완료한다. 같은 donor가 여러 visit/draw에 나타나는 것을 보존한다. 고정된 생성 결과를 재분석한 CI는 모델을 재학습했을 때의 모든 불확실성을 포함하지 않음을 명시한다. 각 방법의 support 수, unique donors, extra validation, gene axis, HVG 선택, test access, missing outputs를 표로 제시한다.
+
+**4D: 단순 bulk 신호 복사를 넘어서는가.** 이미 있는 bulk-broadcast, partial-r, representative-dependence 대조를 먼저 묶는다. 필요할 때만 support-only subtype reference profile과 예측 subtype proportions으로 만든 composition-only baseline을 추가한다. 이 추가 CPU 분석은 충분한 subtype annotation/support가 있을 때만 의미가 있다. Test scRNA로 reference를 적합하지 않고, broad cell-type normalization만으로 subtype 구성 효과를 제거했다고 주장하지 않는다.
+
+**완료 기준:** 모든 주장에 동일 환자·contrast·gene-axis 비교가 대응하고, cohort별 양/음의 효과 및 NA가 유지된다. 독립 생물학적 표본 간 변이를 무시하면 DEG 오류가 커질 수 있으므로 환자 단위를 우선한다([Squair et al., 2021](https://www.nature.com/articles/s41467-021-25960-2)). Giroux의 sex/severity confounding은 sex-linked gene 제거만으로 해결되지 않는다. 관측된 cohort contrast의 재현과 질환의 인과 효과를 구분한다.
+
+**종류:** 대부분 기존 campaign의 CPU 통계 완료와 재시각화. Sepsis deconvolution은 5개의 기존 adoption receipts를 사용하고 재실행하지 않는다. 다른 cohort의 실제 support 차이로 필요한 계산은 기존 campaign 범위에서만 남은 작업으로 센다.
+
+## Figure 5 — rare population이 있다는 것을 직접 확인
+
+**5A: matched population views.** 같은 held-out sample의 observed / PBISC / scSemi를 나란히 놓는다. Observed에서만 fit한 공통 projection을 사용하고, 전체 parent population을 회색, rare state를 강조한다. Observed-only 규칙으로 사례를 정하고 성공·과소생성·관측 음성 사례를 포함한다. Sepsis MS1-like/cycling monocyte, Imperial cycling T/NK 등은 사전 정의와 marker availability를 확인할 후보다.
+
+**5B: 독립 marker identity.** Rare-state 정의, 사례 선택, projection 및 annotator 입력에 사용하지 않은 유전자들로 별도의 발현/co-detection 패널을 만든다. 기존 73-marker cache는 marker-space illustration에는 충분하지만 독립적인 전사체 검증 자체는 아니다. Full 6k 출력에서 소수 유전자만 추출한다. 세 방법과 observed에서 모두 측정 가능한 gene만 사용하며, marker 역할은 생물학적 근거로 정한다.
+
+**5C: rare detection의 신뢰도.** Parent population 안에서 marker co-detection, abundance와 false-positive-like calls를 함께 본다. Observed zero는 유한한 sc sampling에서의 non-detection이므로 진정한 생물학적 부재로 단정하지 않는다. 동일 depth/cell budget의 nested subsampling과 draw별 일관성을 보충자료로 둔다.
+
+**중요한 K 구분:** 기존 paired K1000/K4000 자료의 generated-cell count는 둘 다 1,500/sample이다. 이는 recipe K 감도 분석이며 1,500 대 4,000 생성 세포 수 비교가 아니다. Actual N_gen, K_cond, depth를 따로 표기한다.
+
+**완료 기준:** 보기 좋은 UMAP만으로 결론 내리지 않고, 독립 marker·donor 반복·관측 음성/불일치 사례가 함께 있다. Real/generated cell의 일대일 대응을 암시하지 않는다. Bulk 조건에서 관측 rare state를 회복했다는 결론과 새 생물학적 population을 발견했다는 결론을 구분한다.
+
+**종류:** 기존 세포·cache를 사용하는 새 CPU 분석/그림. Strict 전체 exporter의 20-unit gate를 우회하지 않고, 먼저 완료된 검증 subset의 범위를 명시한 illustration을 만든다.
+
+## Figure 6 — 구체적인 기존 논문 결과 하나를 재현
+
+**추천 후보:** Stephenson et al. (2025), *Temporal multi-omics analysis of COVID-19 in end-stage kidney disease*, Figure 3C의 monocyte HLA-II 감소. Severe/critical과 mild/moderate COVID의 비교에서 HLA-DPB1/DPA1/DRB1/DRA/DQA1 감소가 보고돼 있다([원 논문](https://pmc.ncbi.nlm.nih.gov/articles/PMC12366655/)).
+
+**기존 재료:** Imperial bulk-only 생성 결과, 5개 유전자, bulk 및 clinical metadata가 모두 있다. 로컬 metadata상 164 bulk-only samples/76 donors 중 paired registry의 모든 donor를 보수적으로 제외하면 97 samples/45 donors가 남으며 모두 wave1이다. 이는 analytic cohort를 확정한 수치가 아니다. 로컬 sample-time severity 집계에서는 COVID+ 81 samples 중 70 mild/moderate, 11 severe/critical이며, 두 범주에 모두 등장하는 donor가 4명이다.
+
+**설계 순서:**
+
+1. 원 논문의 severity 정의(patient maximum인지 sample-time인지), time window, 포함/제외 조건을 원문·metadata와 대조해 고정한다. 서로 다른 contrast를 같은 재현으로 부르지 않는다.
+2. 모든 paired donor와 bulk-only target donor를 분리하고, 회복기 포함 여부·repeated visits·wave를 명시한다.
+3. Paired held-out observed cells에서 해당 marker와 monocyte localization을 먼저 점검한다.
+4. 기존 bulk-only 생성 결과에서 donor-aware HLA-II contrast를 계산하고, raw bulk의 같은 유전자 신호와 함께 보여준다. Generated cells를 독립 환자로 세지 않는다.
+5. 기존 adaptation draws/readout에 대한 민감도를 확인한다. 원 논문의 방향·세포군·시점 조건을 얼마나 재현했는지 각각 보고한다.
+
+**권장 패널:** cohort/adaptation 그림 → 원 논문의 재현 대상 → paired validation → bulk-only donor-level effect와 CI → raw-bulk 비교.
+
+**한계:** 이 HLA-II 방향은 과거 workflow에서도 살펴봤다. 기존 데이터의 분석은 **후향적 재현**이며 새 blind validation이 아니다. 독립 확인은 사용자 준비 데이터에서 별도로 정의한다. Wave1-only 집합으로 wave2 steroid-associated population을 재현하려고 하지 않는다. 기존 Ye bulk-only 집합에는 pediatric sepsis cases가 없어 소아 cycling-monocyte 재현을 우선 대상으로 삼지 않는다.
+
+**종류:** endpoint 정의 + 기존 출력의 CPU 재분석이 우선. 새로운 모델 학습이나 bulk-only 재생성을 기본 요구로 두지 않는다.
+
+## Supplementary의 역할
+
+1. **데이터 사용·공정성 표:** corpus/target donor overlap, support/validation/HVG/gene-axis/readout 접근 범위.
+2. **전체 성능·예외:** 모든 cohort/cell type/draw, full FT/rank trade-off, complete CAT/RBO, donor counts 및 NA. 주 그림에서 평균으로 숨겨지는 예외를 보존한다.
+3. **Readout·평가법 감도:** Pearson/Spearman, patient-aware DEG, official scSemi versus modified readout, gene-mask sensitivity. 기존 자료를 같은 비교 단위로 묶는다.
+4. **사전학습·일반화 대조:** 기존 scratch와 cross-cohort 결과를 정확한 fold/seed 범위로 표시한다. 모든 cohort에서의 독립 재현처럼 확대하지 않는다.
+5. **Rare robustness:** marker 정의·독립 검증 marker·모든 사례·depth/N_gen subsampling·zero handling. Figures 4/5의 정량/정성 역할은 유지한다.
+
+## 실제 새 GPU 실험이 필요할 수 있는 두 가지 조건
+
+- **승자/안정성 주장:** 기존 결과로 architecture 또는 total-five rank의 안정성을 주장할 수 없는데 그 주장이 필수인 경우에만, 경쟁 후보를 좁혀 matched sampling seeds 또는 adaptation-training seeds를 추가한다. 이기게 될 때까지 반복하지 않는다.
+- **환자별 conditioning 기전 주장:** 기존 SLE opposite-group/permuted-donor/cohort-mean controls는 이미 있다. Real-bulk에 adapted한 모델의 기전까지 주장해야 할 때만, 기존 adapter 하나에서 correct bulk와 사전에 정한 wrong-bulk intervention을 같은 sampling noise로 비교한다. 이는 새 추론이며 새 학습은 아니다.
+
+둘 다 현 시점의 기본 필수 과제가 아니다. 새 protocol이 필요할 때에는 checkpoint/support/test/validation/mask/conditioning/seed/readout 계약을 reuse registry와 대조한 뒤 바뀐 연산만 수행한다.
+
+## 권장 진행 순서와 종료 조건
+
+**1단계:** Figure1 데이터 사용 경계와 Figure4 비교 계약 확정. Figure6 논문 endpoint 선택은 병행한다.
+
+**2단계:** 기존 strict Figure3/4 campaign의 완료된 출력을 회수·검증·scoring한다. 기존 Figure2/rank/CAT/readout 자료는 재시각화한다. 새 campaign을 추가하지 않는다.
+
+**3단계:** Figure5의 두 rare-state 사례에 공통 projection + 독립 marker + 음성 사례를 붙여 작은 완성 패널을 먼저 만든다. 기존 결과로 분류가 안 되면 그 한계를 그대로 남긴다.
+
+**4단계:** Figure6의 한 endpoint를 기존 결과로 후향 재현한다. 새로운 독립 확인은 데이터와 질문이 고정된 뒤 별도 계획으로 둔다.
+
+**5단계:** 위 결과가 나온 뒤에만 조건부 GPU 항목의 필요성을 판단한다. 유의성이나 순위가 원하는 방향이라는 것을 종료 조건으로 삼지 않는다. 비교의 완결성, 불확실성, 재현 가능성이 종료 기준이다.
+
+## 재사용 근거 경로
+
+`M`은 Model8, `D`는 이 덱 폴더, `P=M/plans/paper_figures_strictpairs_20261012`, `F=M/plans/paired_finetune_intradataset_20260927`이다.
+
+- Corpus: `paper-release-20260928/manuscript/source_data/corpus/{README.md,corpus_summary.json}`; `M/runs/f01_6k_1x_seed20260627/ALLCELL_DATA_CONTRACT.json`.
+- SLE: `P/source_data/ablation_pretrain_{pc,composition_summary}.csv` 및 해당 receipts; `D/data/sec03_panel_seeds.csv`.
+- Healthy: `D/data/sec07_ft_modes_accuracy.csv`, `P/source_data/fig2_historical_rank_full.csv`, `P/protocol/splits/immicro/healthy/`, `P/generated/immicro/healthy/`, `P/scores/immicro/healthy/`.
+- Disease: `P/tools/{donor_statistics.py,disease_signal.py,finalize_outputs.py,deconv_strict.py}`, completed-unit `statistics/`, `F/scores_contrast/`, `P/deconvolution/ye2026_sepsis/disease/drawN/ADOPTION.json`.
+- Readout: `F/scsemi/analysis/output_variants_20261003/per_draw.csv`; `F/scores_nosex/giroux2022/`.
+- Rare: `M/plans/rare_states_paired_20261008/work/`; `F/data/<cohort>/`, `F/scoring_cache/<cohort>/observed_labels/`; `P/tools/{rare_cache_builder.py,export_rare_visuals.py}`.
+- Bulk-only: `M/plans/bulkonly_lora5_20261006/analysis/features/imperial_eskd/meta.csv`; `.../work/imperial_eskd/{bulkonly,paired_gen}/drawN/{pb_l1.npz,pb_l2.npz,cells.parquet,RECEIPT.json}`; corresponding `bulkonly_lora5_k4000_20261006` outputs.
+- Reuse registry: `M/EXPERIMENT_INDEX.md` and `M/EXPERIMENT_REUSE_INDEX.jsonl`.
diff --git a/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/README.md b/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6e87b260a9ad9f985ef50b1e39940ef2695b86c6
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/README.md
@@ -0,0 +1,45 @@
+# Figure 1–3 확장본: 140장
+
+현재 파일: [PPTX](../../PBISC-Diffusion_expanded_master_20261013_enriched.pptx) · [PDF](../../PBISC-Diffusion_expanded_master_20261013_enriched.pdf)
+
+사용자가 지정한 Figure 1–6 구분을 유지하고, 재배치된 131장 덱을 140장으로 확장했다. 이전 131개 페이지의 ID와 원본 76개 ID는 각각 한 번씩 남아 있다. 기존 파일과 원래 scientific figure assets는 그대로 보존했다.
+
+| 현재 페이지 | 변경 내용 |
+|---|---|
+| 10 | Figure 1: 실제 저장된 0/17/33/50 단계의 PCA1/2를 네 패널로 표시 |
+| 13 | Figure 2: context와 trunk가 각각 어디이며 무엇이 달라지는지 보여주는 도식 |
+| 14 | Pred/oracle hint의 출처, condition encoder 앞의 주입 위치, 같은 denoiser를 쓰는 비교 |
+| 15 | Architecture validation loss 전체 및 후반 진행. 이전 supplementary loss 페이지를 갱신해 이동 |
+| 16–17 | SLE architecture benchmark와 정확한 width/parameter/completion 표 |
+| 18–20 | Gene-context benchmark, validation 진행, 실제 gene 수와 parameter 표 |
+| 21 | 모든 10개 학습 arm의 parameter, 기록된 update당 시간 및 누적 학습시간 |
+| 22–23 | Real-cell repeat의 의미와 새 이름을 반영한 공통 축 ablation |
+| 26–27 | Figure 3: bulk와 pseudobulk expression 값의 직접 비교 및 donor별 오차 |
+
+Figure 범위: F1 5–10, F2 11–23, F3 24–33, F4 34–39, F5 40–41, F6 42–44. Supplementary는 45쪽부터다.
+
+## 도식과 수치의 의미
+
+- **Context:** bulk/depth와 measurement features를 context width C로 인코딩하고 time encoding과 혼합한 뒤, 일곱 trunk block에 각 projection을 통해 더한다. Context ×4는 C=512→2,048이다.
+- **Trunk:** denoiser의 hidden block widths다. Trunk ×4는 down widths [1,024,1,024,512,256]을 [4,096,4,096,2,048,1,024]로 늘리고 context C는 512를 유지한다. Width 배수와 parameter 배수는 다르다.
+- **Pred/oracle hint:** 같은 hint-trained denoiser에서 9개 cell-type fractions만 교체한다. `[base G+6 | hint 9 | measurements 2G]`가 condition encoder에 들어간다. Pred는 별도 MLP의 예측, oracle은 target donor의 관측 scANVI fractions를 사용한다. Predictor는 3.206M parameters를 추가하며 별도 시간 기록은 없다.
+- **Real-cell repeat:** 같은 42 donors에서 donor당 100개씩 겹치지 않는 두 실제 세포 묶음을 비교한다. 한 묶음은 reference, 다른 묶음은 평가 대상이다. 새로운 donor 집합이나 이론적 최고 성능을 뜻하지 않는다. 본문과 보충 그림의 Real R1/R2 약어도 고쳤다.
+
+## 검증 범위와 표시한 한계
+
+1. **Trajectory:** 웹사이트용 저장 좌표와 point ID를 사용한다. 기존 ctx2048 E12 EMA의 한 donor pseudobulk illustration이다. 최종 모델의 paired 검증 결과로 부르지 않는다. 4,096개 점과 4,362개 실제 reference, 고정 PCA axes를 유지했다. 최종 cell-type 색을 단계 진행률에 따라 드러내며, timestep confidence로 해석하지 않는다. [참고 웹사이트](https://www.virtualhumanlab.com/research/pbisc-diffusion/)
+2. **Validation:** 내부 corpus validation recipe의 loss이며 SLE biological benchmark와 별도다. 전체 저장된 146개 epoch 기록을 기존 CSV와 대조했다. 16× 모델은 E2 이후 중단돼 최종 loss와 saturation을 NA로 표시한다. 1% endpoint rule은 경험적 종료값 기준이며 수학적 수렴 증명이 아니다.
+3. **Time:** history의 `train_seconds`와 optimizer updates로 계산했다. Loader와 host 조건 변화가 포함된 실제 관측 시간이며 통제된 hardware benchmark가 아니다. Resume 뒤 reset될 수 있는 RUN_STATUS wall_seconds를 총시간으로 사용하지 않았다.
+4. **Mismatch:** 건강인 107 donors × 최종 모델의 공통 측정 5,508 genes. 같은 gene axis에서 `ln(1 + 10^6 × count / gene-axis total)`로 비교했다. 모든 측정 zero를 유지했다. 기존 10-pair other-pool calibration을 불러왔고 새로 fit하지 않았다. Paired calibration은 diagnostic이며 strict-five adaptation 결과와 다르다.
+5. **수치:** 새 mismatch 도표는 기존 assays와 calibrated conditions의 binning 및 donor별 기술통계다. 나머지는 기존 로그·score·좌표의 재시각화다. 새 training/inference/projection fit은 실행하지 않았다.
+
+## 재현과 페이지 연결
+
+- [현재 페이지 ↔ 이전 재배치 페이지 ↔ 원래 131장/76장 번호](page_map.csv)
+- [Source manifest](manifest.json)
+- [검수 결과](qa/VERIFICATION.json)
+- Architecture: `arch_result.json`, `arch_training_arms.csv`, `arch_loss_and_timing_by_part.csv`, `arch_inference_variant_register.csv`.
+- Trajectory: `trajectory_provenance.json`, owned source snapshot와 좌표 CSV.
+- Mismatch: `mismatch_result.json`, donor별 CSV와 density-bin CSV.
+- Labels: `labels_result.json`, source-bound plot proofs.
+- `render.py`는 저장된 enrichment assets를 읽어 덱을 조립하고 PDF로 변환한 다음 검증한다. Host guard/CPU memory limits를 적용한 user-systemd unit에서 실행한다. 과거 builder를 실행해 현재 순서를 덮어쓰지 않는다.
diff --git a/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/arch_additions.json b/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/arch_additions.json
new file mode 100644
index 0000000000000000000000000000000000000000..376a05f212020ea3f73bf45cafbee8ba07a00104
--- /dev/null
+++ b/paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/arch_additions.json
@@ -0,0 +1,3413 @@
+{
+  "schema": "pbisc.figure-enrichment.architecture.v1",
+  "slides": [
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+    },
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+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_server2_snapshots/f01_p15k_1x_seed20260627/history.json",
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+    },
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+      "sha256": "7c8805749d4484d1eea3503a8410c09572d799b219401f48b17c9bf56533e444",
+      "role": "existing figure source table; no rescoring",
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+      "original_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_width_loss.csv"
+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_panel_loss.csv",
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+    },
+    {
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+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_panel_bench.csv",
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+      "original_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_panel_bench.csv"
+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_panel_seeds.csv",
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+      "role": "existing figure source table; no rescoring",
+      "host": "server_1",
+      "original_host": "server_1",
+      "original_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec03_panel_seeds.csv"
+    },
+    {
+      "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/enrich_architecture_20261013.py",
+      "sha256": "770df0d5e3d6dbdba0ccedb9317a807167a66b02a82005255b9c5351ca1e5f4c",
+      "role": "reproducible CPU-only enrichment renderer",
+      "host": "server_1",
+      "original_host": "server_1",
+      "original_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/tools/enrich_architecture_20261013.py"
+    }
+  ],
+  "training_runs": 10,
+  "inference_variants": 11,
+  "scope": "Figure 2: all architecture/hint and final gene-panel arms in sec03; excludes retired2x and unrelated pilot/loss-policy studies.",
+  "style_reference": "https://www.virtualhumanlab.com/research/pbisc-diffusion/; code-native redraw; no website data copied",
+  "timing_definition": "History train_seconds times train_one_epoch including data loading and heartbeat overhead. Divide its sum by recorded optimizer_steps for weighted seconds/update. Sum only completed parts; validation, checkpoint writes, resume gaps and failed partial parts excluded. RUN_STATUS.wall_seconds resets on resumed invocations and is not a total. Hosts, loader settings and competing load changed; do not interpret timing ratios as a controlled benchmark. Server_2 histories are existing local snapshots, not a live-status claim.",
+  "limitations": [
+    "No training seed replicates; five6k seeds are generation repeats.",
+    "16x onlyE1\u2013E2; saturationNA; failedpartial elapsed excluded.",
+    "Separate hint predictor runtimeNA.",
+    "SLEscores and corpusloss are distinct evidence.",
+    "Historical loader/host changes preclude controlledspeedclaims."
+  ],
+  "source_validation": {
+    "width_csv_losses_match_history": true,
+    "all_recorded_parameter_counts_match_arithmetic": true
+  }
+}