# PAPERS.md — PhysioJEPA Reference Index
*Oz Labs — April 2026*
*Covers every paper referenced across the full conversation and all project documents.*

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## How to use this file

Three things per entry:
1. **What to use it for** — the specific task or decision the agent needs this paper for
2. **Key numbers** — exact figures the agent must not get wrong in code or prose
3. **Location** — where to fetch the PDF

Read the tier before writing any code in that tier's domain.
Do not cite a number that isn't in this file without fetching the source first.

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## Tier 1 — Implement from these
*Read before writing any training code. Contains exact equations, hyperparameters, architecture details.*

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### [T1-1] Weimann & Conrad — ECG-JEPA
**arXiv**: 2410.13867 · `arxiv.org/pdf/2410.13867`
**Code**: `github.com/kweimann/ECG-JEPA` ← fork this

**Use for**: This is the codebase we fork. Before writing any encoder code, read Section 2 (architecture), Section 3 (data), Appendix A (hyperparameters).
- Patch tokenisation: 2D over (12 leads × time), patch size = 25 time steps at 500 Hz
- Masking: multi-block contiguous, 50% ratio, 4 target blocks
- EMA: τ starts 0.996, cosine-annealed to 0.9999 over training
- Loss: L1 in latent space — no pixel decoder
- ViT-S: 12 layers, d=256, 8 heads, MLP ratio=4

**Key numbers**: PTB-XL all-statements AUC **0.945** — this is Baseline A in the experiment matrix. Training time ~26h on RTX 3090.

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### [T1-2] Assran et al. — I-JEPA
**arXiv**: 2301.08243 · `arxiv.org/pdf/2301.08243`
**Code**: `github.com/facebookresearch/ijepa`

**Use for**: The masking strategy foundation. Why multi-block contiguous > random masking (forces semantic prediction, not texture interpolation). The stop-gradient / EMA target encoder design justification. The predictor should be *narrower* than the encoder — this prevents shortcutting through the predictor.

**Key numbers**: ViT-H/14 ImageNet — scale reference only, not a target for us.

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### [T1-3] Bardes et al. — V-JEPA (Revisiting Feature Prediction)
**arXiv**: 2404.08471 · `arxiv.org/pdf/2404.08471`

**Use for**: Spatiotemporal tube masking — how to mask contiguous blocks across both spatial and temporal axes simultaneously. Template for PPG 1D+time representation. Two-encoder EMA recipe at scale. Why predicting in latent space beats pixel reconstruction for noisy signals — core justification for JEPA over MAE.

**Key numbers**: SSv2 top-1 77.3%.

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### [T1-4] Balestriero & LeCun — LeJEPA
**arXiv**: 2511.08544 · `arxiv.org/pdf/2511.08544`

**Use for**: Ablation A3 only (SIGReg). Do not implement SIGReg without reading this first.
- Theorem 1: isotropic Gaussian is the optimal JEPA embedding distribution
- SIGReg: K=128 random 1D projections w~N(0,I), KL(z·w || N(0,1)) per projection, sum. O(Kd).
- λ range: [0.01, 0.1]; start at 0.05
- Apply to *pooled global representation only* — not per-patch tokens
- ~50 lines of PyTorch

**Key numbers**: 79% ImageNet ViT-H/14 with only 2 loss terms.

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### [T1-5] Kim — CroPA-ECG-JEPA
**arXiv**: 2410.08559 · `arxiv.org/pdf/2410.08559`
**Code**: `github.com/sehunfromdaegu/ECG_JEPA`

**Use for**: Second ECG-JEPA implementation for debugging. Cross-Pattern Attention (CroPA) = inter-lead masked attention = inspiration for cardiac phase encoding in ablation A2. Also: 1D PE for predictor vs 2D for encoders — different from Weimann, compare before finalising.

**Key numbers**: Recovers HR and QRS duration from frozen representations without supervised training — target behaviour for PTT.

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### [T1-6] Botman et al. — Laya (LeJEPA for EEG)
**arXiv**: 2603.16281 · `arxiv.org/pdf/2603.16281`

**Use for**: Most direct prior to PhysioJEPA. Read before implementing ablation A3.
- SIGReg with aggressive λ destabilises training on impulsive signals (QRS-like spikes in EEG)
- Mitigation: lower λ (0.001–0.01), aggressive gradient clipping, apply to pooled global rep only
- Latent prediction outperforms reconstruction on EEG clinical tasks

**Key numbers**: Outperforms reconstruction baselines on EEG-Bench with 10% of pretraining data.

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## Tier 2 — Baseline numbers and comparisons
*Read to correctly report comparison numbers. Getting baselines wrong is a rejection risk.*

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### [T2-1] Nie et al. — AnyPPG
**arXiv**: 2511.01747 · `arxiv.org/pdf/2511.01747`

**Use for**: Primary contrastive baseline (Baseline C in experiment matrix).
- Exact loss: **symmetric InfoNCE** with learnable temperature τ
- **CRITICAL: ECGFounder encoder is FROZEN during AnyPPG training.** ECG is a fixed supervisory signal. AnyPPG is not a jointly trained dual-encoder model.
- Architecture: Net1D (PPG branch), ECGFounder frozen (ECG branch)
- Trained on >100,000 hours

**Key numbers**: PPG→ECG retrieval **R@1=0.736**, R@5=0.906, R@10=0.935. AF detection AUC ~0.90. Mean **9.1% AUC improvement** over non-ECG-guided baselines.

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### [T2-2] Wagner et al. — PTB-XL
**arXiv**: 2004.13701 · `arxiv.org/pdf/2004.13701`

**Use for**: ECG evaluation benchmark. Task definitions, train/test/val splits, and label hierarchy. Must replicate Weimann's exact split for comparison.

**Key numbers**: Weimann ECG-JEPA AUC **0.945** all-statements = Baseline A target.

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### [T2-3] Charlton et al. — Towards Ubiquitous BP Monitoring via PTT (review)
**URL**: `pmc.ncbi.nlm.nih.gov/articles/PMC4515215/`

**Use for**: Before writing E4 rollout coherence physiological consistency checks. PTT definition, normal range, PTT–BP and HR–PTT relationships. Per-patient calibration required for absolute BP — do not claim uncalibrated absolute BP from PTT.

**Key numbers**: Normal PTT **100–400ms** (ICU adults). Within-patient tracking ~10 mmHg MAE with calibration.

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### [T2-4] Assran et al. — V-JEPA 2 (including V-JEPA 2-AC)
**arXiv**: 2506.09985 · `arxiv.org/pdf/2506.09985`

**Use for**: Architecture D future work template. Two-stage recipe: action-free pretraining → action-conditioned fine-tuning with frozen encoder.

**Key numbers**: **<62 hours** of robot interaction data for Stage 2. SSv2 top-1 77.3%.

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## Tier 3 — Related work framing
*Read to correctly describe prior work and differentiate PhysioJEPA.*

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### [T3-1] Sarkar & Etemad — CardioGAN
**arXiv**: 2010.00104 · `arxiv.org/pdf/2010.00104`
**Code**: `github.com/pritamqu/ppg2ecg-cardiogan`

**Use for**: First major cross-modal ECG-PPG paper (AAAI 2021).
- Uses **CycleGAN backbone** with attention-based generators and dual time/frequency discriminators
- **NOT reconstruction/L1, NOT InfoNCE** — adversarial + cycle consistency loss
- t=0 alignment — discards lag. Do NOT call this "pixel reconstruction."

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### [T3-2] Liu, Wang & Wang — TSTA-Net
**PMLR**: proceedings.mlr.press/v278/liu25d.html

**Use for**: Hierarchical contrastive ECG-PPG baseline (PMLR 2025).
- **Hierarchical contrastive learning** — NOT raw InfoNCE
- 9.3% higher AF F1 vs prior SSL methods
- Still t=0 aligned

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### [T3-3] Fang et al. — PPGFlowECG
**arXiv**: 2509.19774 · `arxiv.org/pdf/2509.19774`

**Use for**: Two-stage generative translation baseline.
- Stage 1: **InfoNCE instance alignment** (CardioAlign encoder, shared weights)
- Stage 2: **rectified flow** generation from aligned latents
- Figure 1 explicitly shows ECG precedes PPG temporally but the architecture does not exploit this
- Do NOT describe as "rectified flow only" — InfoNCE is in Stage 1

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### [T3-4] Dong et al. — Brain-JEPA (NeurIPS 2024 Spotlight)
**arXiv**: 2409.19407 · `arxiv.org/pdf/2409.19407`
**Code**: `github.com/hzlab/2024_Dong_Li_NeurIPS_Brain-JEPA`

**Use for**: Cardiac phase encoding inspiration (ablation A2). Brain Gradient Positioning → our cardiac phase PE. Hard phase boundaries fail during AF — use soft Gaussian encoding over cardiac landmarks.

**Key numbers**: NeurIPS 2024 Spotlight. UK Biobank 40k patients.

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### [T3-5] Hojjati et al. — EEG-VJEPA
**arXiv**: 2507.03633 · `arxiv.org/pdf/2507.03633`
**Code**: `github.com/amir-hojjati/eeg-vjepa`

**Use for**: V-JEPA adapted to 1D physiological signal — most direct predecessor. How to reshape multi-channel 1D signal into 3D tensor treated as "video." UMAP showing pathological clustering without labels.

**Key numbers**: TUH fine-tuned accuracy **85.8%**, AUROC **88.5%**. Frozen probe 83.3%.

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### [T3-6] Munim et al. — EchoJEPA
**arXiv**: 2602.02603 · `arxiv.org/pdf/2602.02603`

**Use for**: Strongest empirical evidence that JEPA > MAE for noisy medical signals. Use in intro to justify JEPA over MAE.

**Key numbers**: JEPA degrades **2%** under perturbation vs **17%** for VideoMAE. **79%** accuracy at 1% labels. 20% LVEF improvement.

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### [T3-7] Wu, Lei et al. — SurgMotion
**arXiv**: 2602.05638 · `arxiv.org/pdf/2602.05638`

**Use for**: One-sentence citation alongside EchoJEPA: "JEPA's noise rejection under clinical signal artifacts has been validated in echocardiography [EchoJEPA] and surgical video [SurgMotion]."

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### [T3-8] LeCun — A Path Towards Autonomous Machine Intelligence (JEPA position paper)
**URL**: `openreview.net/pdf?id=BZ5a1r-kVsf`

**Use for**: One intro citation: "A world model should predict consequences of actions in abstract representation space [LeCun 2022]."

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### [T3-9] Abbaspourazad et al. — Apple Heart Study Foundation Model
**arXiv**: 2312.05409 · `arxiv.org/pdf/2312.05409`
**Published**: ICLR 2024

**Use for**: Prior art on wearable-scale PPG+ECG foundation models. InfoNCE + KoLeo, participant-level positives, Apple Watch data. Shows ECG more discriminative than PPG — context for why cross-modal training helps PPG.

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## Tier 4 — Evaluation methodology and datasets
*Read when writing the evaluation harness code.*

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### [T4-1] Pimentel et al. — BIDMC PPG and Respiration Dataset
**PhysioNet**: `physionet.org/content/bidmc/1.0.0/`

**Use for**: Fallback dataset if E0 fails.
- WFDB format, **53 recordings × 8 min**, **125 Hz**
- Signals: **Lead II ECG + fingertip PPG** + impedance respiration
- Labels: HR, RR, SpO2 — **no AF labels** (use for HR probe only)

**Key numbers**: **53 patients**, ~7 hours total, **125 Hz**.

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### [T4-2] Moody et al. — MIMIC-IV Waveform Database
**PhysioNet**: `physionet.org/content/mimic4wdb/0.1.0/`

**Use for**: Understanding HuggingFace mirror provenance.
- v0.1.0: **200 records from 198 patients**; upcoming release ~10,000 records
- MIMIC-IV-ECG module: **~800k ECGs across ~160k patients**, 500 Hz, 10s, 12-lead — AF label source candidate

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### [T4-3] Kachuee et al. — Cuffless BP Estimation Dataset (UCI)
**UCI**: `archive.ics.uci.edu/dataset/340`

**Use for**: E5a PTT probe evaluation.
- 12,000 records, 942 patients — **patient ID removed** — population-level evaluation only
- PPG + ABP at 125 Hz, derived from MIMIC-II

**Key numbers**: AAMI standard ≤5 mmHg mean ± 8 mmHg SD.

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### [T4-4] Goldberger et al. — PhysioBank, PhysioToolkit, PhysioNet
**DOI**: 10.1161/01.CIR.101.23.e215

**Use for**: Required citation whenever using BIDMC, MIMIC waveforms, or any PhysioNet dataset. One line in methods: "Data obtained from PhysioNet [Goldberger et al., 2000]."

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## Tier 5 — Context and intellectual lineage
*Do not read these to implement anything. One citation each.*

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### [T5-1] Ha & Schmidhuber — World Models
**arXiv**: 1803.10122

**Use for**: Intro citation only. "World models learn a compressed latent representation and a transition function [Ha & Schmidhuber, 2018]."

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### [T5-2] Bardes et al. — VICReg
**arXiv**: 2105.04906

**Use for**: Related work only. "VICReg requires hand-crafted augmentations that JEPA avoids."

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### [T5-3] Ronan et al. — VICReg for Brugada ECG Detection
**DOI**: 10.1038/s41598-025-94130-x

**Use for**: One sentence. "VICReg-based SSL has been applied to ECG classification [Ronan et al., 2025] but requires augmentation engineering."

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### [T5-4] Johnson et al. — MIMIC-IV (clinical database paper)
**DOI**: 10.1038/s41597-022-01899-x

**Use for**: Required data citation whenever using MIMIC-IV derived data. "MIMIC-IV [Johnson et al., 2023], a freely accessible EHR database."

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### [T5-5] CLIMB multimodal clinical benchmark
**arXiv**: 2503.07667

**Use for**: ECG-JEPA performance in multimodal settings. "ECG-JEPA outperforms general time-series models like UniTS by 36.8% on ECG tasks [CLIMB, 2025]." One citation in intro.

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## Quick reference: numbers the agent must not get wrong

| Claim | Correct value | Source |
|-------|--------------|--------|
| ECG-JEPA PTB-XL AUC | **0.945** all-statements | T1-1 Weimann |
| AnyPPG PPG→ECG R@1 | **0.736** | T2-1 Nie |
| AnyPPG AUC improvement | **9.1%** over non-ECG baselines | T2-1 Nie |
| AnyPPG ECGFounder | **FROZEN** during training | T2-1 Nie |
| EchoJEPA JEPA perturbation | **2%** degradation | T3-6 Munim |
| EchoJEPA MAE perturbation | **17%** degradation | T3-6 Munim |
| EchoJEPA 1% label accuracy | **79%** | T3-6 Munim |
| Normal PTT range (ICU) | **100–400ms** | T2-3 Charlton |
| BIDMC size | **53 recordings × 8 min @ 125 Hz** | T4-1 Pimentel |
| V-JEPA 2-AC interaction data | **<62 hours** | T2-4 Assran |
| EEG-VJEPA TUH AUROC | **88.5%** fine-tuned | T3-5 Hojjati |
| CardioGAN objective | **CycleGAN adversarial** — not reconstruction | T3-1 Sarkar |
| TSTA-Net objective | **Hierarchical contrastive** — not raw InfoNCE | T3-2 Liu |
| PPGFlowECG Stage 1 | **InfoNCE alignment**, then rectified flow | T3-3 Fang |
| BP calibration requirement | **Per-patient calibration required** for absolute values | T2-3 Charlton |

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## File locations in repo

```
docs/papers/*.pdf
```

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*This is the complete reference index. Fetch from arXiv if a PDF is missing. Never cite a number not in this file without verifying the source first.*