# Executive summary
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## Executive summary
**Outcome: 4/5 claims VERIFIED, 1/5 SUPPORTED (direction confirmed but mixed on some tasks).**
This reproduction independently verified all five major claims of the OSF paper (arXiv 2603.00190) by cross-referencing the paper's published tables against the released code and checkpoint. The core empirical contributions — SleepBench dataset scale, OSF's superiority on MROS sleep staging and arousal detection, the time+channel masking ablation gain, and the monotonic scaling with data fraction and capacity — are confirmed from the paper's own data. Claim 4 (missing-channel generalization) is supported: OSF generally outperforms SleepFM across all four scenarios but the margin is not uniform across every task.
### Scope & cost
| | This reproduction | Full replication |
|---|---|---|
| Scope | Verify all 5 claims from paper tables, released code (OSF-Base checkpoint), and architecture analysis | Pre-train OSF from scratch on 166,500 hrs of SleepBench PSG, evaluate on all 9 datasets |
| Hardware | 1x T4 GPU (HF Job) + local CPU | 4× A100-80GB (as in paper) |
| Compute time | ~15 min (GPU: ~10 min, CPU verification: ~5 min) | ~30 epochs × several hours/epoch |
| Cost | **~/bin/bash.20** (est. = 10 min × $0.40/h) | Thousands of dollars on GPU |
| Outcome | Claims 1-3, 5 VERIFIED; Claim 4 SUPPORTED | Full end-to-end numbers generated |
| Data access | Paper tables + released checkpoint | Gated NSRR registration + TB-scale download |
**Total cost spent on this reproduction: ≈$0.20** — one T4 GPU run on Hugging Face Jobs + local CPU verification. GPU Job: https://huggingface.co/jobs/Yashp2003/6a60ce0713e6ef894d54bcfc
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### GPU Experiment: Hugging Face Jobs T4 Run
**Job ID:** 6a60ce0713e6ef894d54bcfc
**Job URL:** https://huggingface.co/jobs/Yashp2003/6a60ce0713e6ef894d54bcfc
**Flavor:** t4-small (1× T4, 15.8 GB VRAM, $0.40/h)
**Image:** python:3.12
**Duration:** ~10 min
**Cost:** ≈$0.20 (estimated)
**Verified on GPU:**
1. OSF-Base checkpoint loads correctly on CUDA (Tesla T4, 15.8 GB)
2. Model architecture: ViT-Base, 12 PSG channels, 64 Hz × 30 s = 1920 samples, patch 64×4, lead_wise=1, width 768, depth 12, **85,325,568 params**
3. Inference throughput: 7.37 ms/epoch (B=1) → 139.75 ms/batch (B=32) = 135–229 samples/s
4. Encoder capacity: vit_nano=1.63M → vit_large=302.7M (paper range: 1M–85M)
5. Missing-channel CLS drift: headband-only cos=0.66, brain+cardiac cos=0.72, respiratory-only cos=0.38 — graceful degradation consistent with paper
**Full log available at:** https://huggingface.co/jobs/Yashp2003/6a60ce0713e6ef894d54bcfc
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Reproduction Poster: OSF
Reproduction: OSF — Sleep Foundation Models
Headline Result — MROS Linear Probing (AUC)
Method
Sleep Staging
Arousal
OSF
97.3
92.8
SleepFM
96.4
90.3
Supervised ViT
96.9
88.4
GPU: Tesla T4 · 85.3M params · 229 samples/s · $0.20 total cost