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Updates benchmark results and adds raw output artifact
Refreshes the benchmark section with results from the latest master verification run, providing improved transparency and reproducibility. Links raw benchmark output for deeper analysis and future reference.
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CHANGELOG.md

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- ResNet benchmark defaults now enable adaptive sleep budget scaling by default while keeping reward-modulated learning disabled by default.
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- Updated circadian unit tests (NumPy + Torch) with coverage for reward scaling and adaptive budget behavior.
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- Updated README, model card, and core module docs to document new circadian controls.
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- Refreshed README benchmark section with a latest master verification run on 2026-02-28 and added raw output artifact under `docs/benchmarks/`.
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- Repositioned repository messaging to Circadian Predictive Coding as the primary focus.
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- Updated `README.md` with:
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- circadian-first project framing

README.md

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| PredictiveCodingResNet50 | 0.723 | 2093.4 | 4839.0 | fixed head |
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| CircadianPredictiveCodingResNet50 | 0.734 | 2059.9 | 4831.4 | hidden 384->394, splits=12, prunes=2, rollbacks=7 |
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### Latest master verification run (single-seed subset, 2026-02-28)
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Command:
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```powershell
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python resnet50_benchmark.py --dataset-name cifar100 --classes 100 --dataset-train-subset-size 20000 --dataset-test-subset-size 5000 --epochs 12 --device cuda --target-accuracy -1 --backprop-freeze-backbone --backbone-weights imagenet
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```
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| Model | Accuracy | Cross-Entropy | Train SPS | Inference P95 (ms) | Notes |
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|---|---:|---:|---:|---:|---|
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| BackpropResNet50 | 0.678 | 1.7144 | 981.3 | 23.03 | fixed head |
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| PredictiveCodingResNet50 | 0.692 | 1.1175 | 965.2 | 20.77 | fixed head |
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| CircadianPredictiveCodingResNet50 | 0.685 | 1.1082 | 874.2 | 23.27 | hidden 384->384, splits=0, prunes=0, rollbacks=0 |
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Raw benchmark output: [`docs/benchmarks/benchmark_master_cifar100_subset_2026-02-28.txt`](docs/benchmarks/benchmark_master_cifar100_subset_2026-02-28.txt)
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## Repository Layout
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