v1.1: add 8-way public model comparison results

v1.1/RESULTS_8WAY.md

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+# ArtifactBench — 8-Way Public Model Comparison (2026-07-04)
+
+Extends the original 3-way comparison (ArtifactNet / SpecTTTra / CLAM) with five
+additional publicly-available AI-music detection models, evaluated on the same
+v1.1 purged test partition (n = 2,224; see `v1.1/RESULTS_v1.1.md`).
+
+## Models added
+
+| Model | Params | Source |
+|---|---:|---|
+| Deezer ISMIR fakeprint LR | 3.6K | [`lofcz/ai-music-detector`](https://huggingface.co/lofcz/ai-music-detector) (Afchar et al., ISMIR 2025) |
+| FST (Mippia) | 174.4M | [`Mippia/FST-AI-music-detection`](https://github.com/Mippia/FST-AI-music-detection) |
+| AI-Music-Detection AST-60s | 90.8M | [`AI-Music-Detection/ai_music_detection_large_60s`](https://huggingface.co/AI-Music-Detection/ai_music_detection_large_60s) |
+| SpecTTTra β-5s | 18.7M | [`awsaf49/sonics-spectttra-beta-5s`](https://huggingface.co/awsaf49/sonics-spectttra-beta-5s) (5s-segment variant of the SONICS α-120s baseline) |
+| DeepFense EAT+Nes2Net (FakeMusicCaps) | — | [`DeepFense/FakeMusicCaps_EAT_Nes2Net_NoAug_Seed42`](https://huggingface.co/DeepFense/FakeMusicCaps_EAT_Nes2Net_NoAug_Seed42) (Kheir et al., arXiv:2604.08450) |
+
+Adapters: `artifactbench/models/{ast_60s,deepfense,deezer_ismir,fst,spectttra_variants}.py`.
+Run with `--model ast_60s --model deepfense --model deezer_ismir --model fst --model spectttra_beta5s`
+alongside the existing `--model artifactnet --model spectttra --model clam`.
+
+## Results (n = 2,224, τ = 0.5)
+
+| Rank | Model | Params | F1 | Precision | Recall (TPR) |
+|---|---|---:|:---:|:---:|:---:|
+| 1 | **ArtifactNet v9.4 (ours)** | **4.2M** | **0.952** | 0.932 | 97.3% |
+| 2 | AI-Music-Detection AST-60s | 90.8M | 0.840 | 0.848 | 83.1% |
+| 3 | CLAM (MoM) | 194.3M | 0.787 | 0.711 | 88.3% |
+| 4 | SpecTTTra α-120s | 18.7M | 0.777 | 0.880 | 69.5% |
+| 5 | Deezer ISMIR fakeprint LR | 3.6K | 0.754 | 0.906 | 64.6% |
+| 6 | FST (Mippia) | 174.4M | 0.735 | 0.984 | 58.7% |
+| 7 | DeepFense EAT+Nes2Net | — | 0.650 | 0.589 | 72.4% |
+| 8 | SpecTTTra β-5s | 18.7M | 0.563 | 0.884 | 41.3% |
+
+**Observation.** Parameter count does not predict performance: FST (174.4M) scores
+below Deezer's 3.6K-parameter logistic-regression baseline. ArtifactNet (4.2M) ranks
+first among all eight models, including three baselines 20–46× larger.
+
+**DeepFense catastrophic-OOD case study.** DeepFense (trained on FakeMusicCaps,
+which draws its AI examples from a different generator/format mix) collapses to
+near-chance on the SONICS synthetic subset specifically (Chirp/Udio, TPR 0–15%)
+while performing normally on every other AI-generator subset. This is a genuine
+distribution-shift failure, not an adapter bug (verified by inspecting per-source
+means, confirming the split correlates with data source rather than ground-truth
+label — see the investigation note in this repo's commit history).
+
+**Segment-length ablation (SpecTTTra).** The same SpecTTTra architecture drops from
+F1 = 0.777 (120s context) to F1 = 0.563 (5s context) purely from shorter input —
+evidence that long-context modeling, not just architecture, drives this baseline's
+detection accuracy.
+
+## Reproducibility note — ONNX Runtime CUDA determinism
+
+An earlier run of this 8-way comparison (2026-07-03) produced ArtifactNet F1 = 0.869
+using default onnxruntime-gpu settings (`cudnn_conv_algo_search: EXHAUSTIVE`, the
+ORT default). Re-running the identical files and code with `HEURISTIC` algorithm
+selection (deterministic, avoids per-run algorithm auto-tuning) recovered F1 = 0.952
+on the same data. **We verified bit-exact reproducibility**: two independent runs of
+a 40-track subset, under both the CPU path and the fixed GPU path, produced identical
+probability values (max abs diff = 0.0) across runs. The `artifactnet.py` adapter now
+pins these settings by default. If you fork this runner and see run-to-run drift with
+CUDAExecutionProvider on other hardware, check this setting first.
+
+## Caveat — file provenance
+
+The real-track set used in this specific 8-way run is a locally-restored superset
+(some files re-sourced after original storage loss) rather than the exact byte-identical
+files behind the official v1/v1.1 Table 6 numbers. Absolute FPR values here should be
+treated as approximate; TPR and cross-model ranking are unaffected (all eight models
+were scored on identical files). Canonical-byte re-verification is planned for a future
+update.