Initial sanitized DyT artifact bundle

CITATION.bib

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+@misc{verma2026dytcomposition,
+  title         = {When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer},
+  author        = {Verma, Lucky},
+  year          = {2026},
+  publisher     = {arXiv},
+  doi           = {10.48550/arXiv.2604.23434},
+  url           = {https://arxiv.org/abs/2604.23434},
+  eprint        = {2604.23434},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG}
+}

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+    "title": "When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer"
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+    ],
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+      "neutralized internal OpenWebText run labels",
+      "neutralized hardware/environment prose",
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+    ],
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+  }
+}

README.md

@@ -0,0 +1,69 @@
+---
+pretty_name: "DyT Composition Study Artifacts"
+license: cc-by-4.0
+language:
+  - en
+tags:
+  - machine-learning
+  - transformers
+  - layernorm
+  - dynamic-tanh
+  - activation-bounding
+  - reproducibility
+size_categories:
+  - n<1K
+---
+
+# DyT Composition Study Artifacts
+
+This dataset contains sanitized result manifests and analysis outputs for:
+
+**When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer**
+
+- Paper: https://arxiv.org/abs/2604.23434
+- DOI: https://doi.org/10.48550/arXiv.2604.23434
+- Code: https://github.com/lucky-verma/dyt-composition-study
+
+## Contents
+
+The artifacts include aggregate training metrics, saturation measurements, statistical-test summaries, predictor-validation outputs, table-source manifests, and selected aggregate analysis files used by the public code repository.
+
+This is not a natural-language training dataset. It does not redistribute WikiText, OpenWebText, LAMBADA, BLIMP, model checkpoints, or raw training logs.
+
+## Intended Use
+
+Use this artifact bundle to:
+
+- inspect the machine-readable results behind the paper;
+- reproduce paper tables and consistency checks;
+- compare DyT, LayerNorm, RMSNorm, HardTanh, DiffAttn, and related controls at the reported scales;
+- audit provenance for reported quantitative claims.
+
+## Limitations
+
+- The experiments are compute-limited and below Chinchilla-optimal training.
+- The included files are result artifacts, not full raw training traces or checkpoints.
+- The saturation diagnostic should be treated as a per-deployment calibration cue, not a universal rule.
+- Raw public datasets retain their original licenses and are not mirrored here.
+
+## Licensing
+
+The result artifacts in this dataset are released under CC BY 4.0.
+
+The associated GitHub code is released under the MIT License.
+
+## Citation
+
+```bibtex
+@misc{verma2026dytcomposition,
+  title        = {When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer},
+  author       = {Verma, Lucky},
+  year         = {2026},
+  publisher    = {arXiv},
+  doi          = {10.48550/arXiv.2604.23434},
+  url          = {https://arxiv.org/abs/2604.23434},
+  eprint       = {2604.23434},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.LG}
+}
+```

SHA256SUMS.txt

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+af2028744c89e4af0c40b4df861cc6ebe923f2193693c15de1181fdd5f14acc4  results/vit_results.json

VALIDATION_REPORT.md

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+# HF Artifact Validation Report
+
+- Source: sanitized public DyT artifact staging
+- Source remote: https://github.com/lucky-verma/dyt-composition-study.git
+- Source commit: 697bb6760c34c7205295f234cb68404fa62d4668
+- Source dirty: false
+- Files before this report: 26
+- JSON files: 23
+- Size: 738656 bytes
+- Raw/checkpoint/log/PDF files: none
+- Files over size limit: none
+- Banned content hits: none
+- Upload performed: no
+
+This directory is safe to upload privately for manual HF page review.

results/activation_analysis.json

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+{
+  "_description": "Activation saturation analysis: fraction of pre-tanh activations |alpha*x| exceeding thresholds. Measured on 50 random batches of 512 tokens from training data.",
+  "_key_finding": "Saturation is regime-dependent: 1M tokens -> ~49% saturated at |ax|>2, 118M tokens -> ~21% saturated. Higher saturation = stronger implicit regularization.",
+
+  "85M_1M_dyt": {
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+    "mean_alpha": 2.4,
+    "n_dyt_layers": 25,
+    "_regime": "overfitting (1M tokens)"
+  },
+  "85M_118M_dyt": {
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+    "n_dyt_layers": 25,
+    "_regime": "underfitting (118M tokens)"
+  },
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+    "n_dyt_layers": 25,
+    "_regime": "overfitting (1M tokens)"
+  },
+  "124M_118M_dyt": {
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+    "_regime": "underfitting (118M tokens)"
+  },
+  "354M_1M_dyt": {
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+    "mean_alpha": 1.8,
+    "n_dyt_layers": 49,
+    "_regime": "overfitting (1M tokens)"
+  },
+  "354M_118M_dyt": {
+    "saturation": {"1.0": 0.4753, "1.5": 0.3864, "2.0": 0.3165, "2.5": 0.2599, "3.0": 0.2133},
+    "mean_alpha": 1.5,
+    "n_dyt_layers": 49,
+    "_regime": "underfitting (118M tokens)"
+  }
+}

results/alpha_sweep.json

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+{
+  "_description": "DyT alpha_init sweep: 64M GPT on WikiText-103 1M tokens (overfitting regime), 5K steps. Tests sensitivity to alpha initialization.",
+  "_model": {"n_layer": 12, "n_head": 8, "n_embd": 512, "block_size": 512, "params_M": 63.52},
+
+  "vanilla": {
+    "seeds": {"1337": 9.2160, "42": 9.4223},
+    "mean": 9.3192
+  },
+  "dyt_alpha_0.5": {
+    "seeds": {"1337": 6.2281, "42": 6.1459},
+    "mean": 6.1870,
+    "_note": "DyT paper default. Slowest convergence but best generalization in overfitting regime."
+  },
+  "dyt_alpha_1.0": {
+    "seeds": {"1337": 6.1736, "42": 6.1027},
+    "mean": 6.1382,
+    "_note": "Best overall val loss in overfitting regime."
+  },
+  "dyt_alpha_2.0": {
+    "seeds": {"1337": 6.7868, "42": 6.7664},
+    "mean": 6.7766,
+    "_note": "Our GPT default. Faster convergence, slightly worse final val."
+  },
+  "dyt_alpha_3.0": {
+    "seeds": {"1337": 7.3272, "42": 7.4907},
+    "mean": 7.4090,
+    "_note": "Too aggressive. Train loss drops fast but val loss diverges."
+  }
+}

results/convergence_10k.json

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+{
+  "_description": "10K-step convergence comparison: Vanilla vs DyT+DiffAttn on WikiText-103 (118M tokens). Single seed (1337). Shows DyT+DiffAttn overtaking vanilla after ~8K steps.",
+  "_model": {"n_layer": 12, "n_head": 8, "n_embd": 512, "block_size": 512},
+  "_training": {"batch_size": 64, "lr": 3e-4, "max_iters": 10000},
+
+  "vanilla_10k": {
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+    "steps":      [0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000],
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+    "final_val": 3.3849
+  },
+
+  "dyt_diffattn_10k": {
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+    "steps":      [0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000],
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+    "final_val": 3.3695,
+    "_note": "Surpasses vanilla at ~10K steps despite slower initial convergence. DyT+DiffAttn composition has higher capacity ceiling."
+  },
+
+  "_crossover_step": "~9500-10000 (DyT+DiffAttn val loss drops below vanilla)"
+}

results/full/activation_rank_v1/aggregate.json

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results/full/crossover_predictor.json

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results/full/downstream_v5/aggregate.json

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results/full/fast_extract.json

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+}

results/full/hessian_pyhessian_v4/aggregate.json

@@ -0,0 +1,590 @@
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+    "name": "wikitext_1m_vanilla_s1337",
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+    "seed": 1337,
+    "tokens": "1m",
+    "error": "pyhessian: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 93.09 GiB of which 1.53 GiB is free. Including non-PyTorch memory, this process has 91.55 GiB memory in use. Of the allocated memory 87.52 GiB is allocated by PyTorch, and 3.31 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
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+    "name": "wikitext_1m_vanilla_s42",
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+    "seed": 42,
+    "tokens": "1m",
+    "error": "pyhessian: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 93.09 GiB of which 942.56 MiB is free. Including non-PyTorch memory, this process has 92.16 GiB memory in use. Of the allocated memory 87.52 GiB is allocated by PyTorch, and 3.91 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
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+    "name": "wikitext_1m_vanilla_s7",
+    "cfg": "vanilla",
+    "seed": 7,
+    "tokens": "1m",
+    "error": "pyhessian: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 93.09 GiB of which 554.56 MiB is free. Including non-PyTorch memory, this process has 92.54 GiB memory in use. Of the allocated memory 88.84 GiB is allocated by PyTorch, and 2.97 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
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+  "scale3/wikitext_1m_dyt_s1337": {
+    "sub": "scale3",
+    "name": "wikitext_1m_dyt_s1337",
+    "cfg": "dyt",
+    "seed": 1337,
+    "tokens": "1m",
+    "error": "pyhessian: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 93.09 GiB of which 126.56 MiB is free. Including non-PyTorch memory, this process has 92.95 GiB memory in use. Of the allocated memory 90.79 GiB is allocated by PyTorch, and 1.44 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
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+  "scale3/wikitext_1m_dyt_s42": {
+    "sub": "scale3",
+    "name": "wikitext_1m_dyt_s42",
+    "cfg": "dyt",
+    "seed": 42,
+    "tokens": "1m",
+    "error": "pyhessian: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 93.09 GiB of which 520.56 MiB is free. Including non-PyTorch memory, this process has 92.57 GiB memory in use. Of the allocated memory 90.79 GiB is allocated by PyTorch, and 1.06 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
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+  "scale3/wikitext_1m_dyt_s7": {
+    "sub": "scale3",
+    "name": "wikitext_1m_dyt_s7",
+    "cfg": "dyt",
+    "seed": 7,
+    "tokens": "1m",
+    "error": "pyhessian: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 0 has a total capacity of 93.09 GiB of which 776.56 MiB is free. Including non-PyTorch memory, this process has 92.32 GiB memory in use. Of the allocated memory 90.79 GiB is allocated by PyTorch, and 826.24 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
+    "elapsed_s": 6.107895851135254
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+}

results/full/lambada_eval/aggregate.json

@@ -0,0 +1,978 @@
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+    "per_token_perplexity": 59908.8671875,
+    "n_params": 589824
+  },
+  "scale5/wikitext_dyt_s1337": {
+    "folder": "scale5",
+    "name": "wikitext_dyt_s1337",
+    "n_eval": 500,
+    "last_token_accuracy": 0.002,
+    "per_token_perplexity": 7825.06201171875,
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+  },
+  "scale5/wikitext_dyt_s42": {
+    "folder": "scale5",
+    "name": "wikitext_dyt_s42",
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+    "last_token_accuracy": 0.004,
+    "per_token_perplexity": 7124.27001953125,
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+  "scale5/wikitext_dyt_s7": {
+    "folder": "scale5",
+    "name": "wikitext_dyt_s7",
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+    "last_token_accuracy": 0.002,
+    "per_token_perplexity": 7856.78955078125,
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+  },
+  "scale5/wikitext_vanilla_s1337": {
+    "folder": "scale5",
+    "name": "wikitext_vanilla_s1337",
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+    "last_token_accuracy": 0.026,
+    "per_token_perplexity": 548.7824096679688,
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+  },
+  "scale5/wikitext_vanilla_s42": {
+    "folder": "scale5",
+    "name": "wikitext_vanilla_s42",
+    "n_eval": 500,
+    "last_token_accuracy": 0.034,
+    "per_token_perplexity": 438.0230712890625,
+    "n_params": 589824
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+  "scale5/wikitext_vanilla_s7": {
+    "folder": "scale5",
+    "name": "wikitext_vanilla_s7",
+    "n_eval": 500,
+    "last_token_accuracy": 0.036,
+    "per_token_perplexity": 482.1871643066406,
+    "n_params": 589824
+  }
+}

results/full/manifests/alpha_sweep_118m_summary.json

@@ -0,0 +1,68 @@
+{
+  "description": "DyT alpha initialization sweep at 64M parameters, 118M Wikitext-103 tokens, 5K steps, eff_batch=64. Completed in Apr 2026.",
+  "baseline": {
+    "config": "vanilla_layernorm",
+    "val_loss_mean": 3.631,
+    "val_loss_std": 0.008,
+    "delta_vs_vanilla_percent": 0.0,
+    "source": "tab:phase canonical 64M/118M vanilla"
+  },
+  "canonical_dyt_reference": {
+    "alpha_init": 2.0,
+    "val_loss_mean": 4.313,
+    "val_loss_std": 0.024,
+    "delta_vs_vanilla_percent": 18.8,
+    "source": "tab:phase canonical 64M/118M DyT"
+  },
+  "alpha_sweep": [
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+      "alpha_init": 0.5,
+      "seed_values": {
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+        "42": 5.123071670532227,
+        "7": 5.205899238586426
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+      "val_loss_mean": 5.154314994812012,
+      "val_loss_std": 0.04500462489409818,
+      "delta_vs_vanilla_percent": 41.95304309589678,
+      "remote_folders": [
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a05_s1337",
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a05_s42",
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a05_s7"
+      ]
+    },
+    {
+      "alpha_init": 1.0,
+      "seed_values": {
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+        "42": 4.822934150695801,
+        "7": 4.7534871101379395
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+      "val_loss_mean": 4.771121819814046,
+      "val_loss_std": 0.04562680357003839,
+      "delta_vs_vanilla_percent": 31.39966455009766,
+      "remote_folders": [
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a10_s1337",
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a10_s42",
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a10_s7"
+      ]
+    },
+    {
+      "alpha_init": 3.0,
+      "seed_values": {
+        "1337": 4.2006402015686035,
+        "42": 4.192511081695557,
+        "7": 4.193594932556152
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+      "val_loss_mean": 4.1955820719401045,
+      "val_loss_std": 0.004413863350402993,
+      "delta_vs_vanilla_percent": 15.548941667312155,
+      "remote_folders": [
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a30_s1337",
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a30_s42",
+        "<CODE_ROOT>/out/alpha_sweep_118m/dyt_a30_s7"
+      ]
+    }
+  ],
+  "paper_claim": "Increasing alpha weakens activation bounding and monotonically reduces DyT's data-rich penalty, but the best tested alpha still trails LayerNorm at 64M/118M."
+}

results/full/manifests/predictor_validation.json

@@ -0,0 +1,825 @@
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results/full/manifests/sig_tests.json

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results/full/noise_stability_s3/aggregate.json

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results/full/noise_stability_uniform/aggregate.json

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results/full/scale5_metadata_check.json

@@ -0,0 +1,491 @@
+{
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+      "learning_rate": 0.0001,
+      "log_last_iter": 5000,
+      "max_iters": 5000,
+      "metadata_iter_num": 5000,
+      "n_embd": 3072,
+      "n_head": 32,
+      "n_layer": 32,
+      "run": "diff_attn_v1v2_scale5/scale5_118m_diffattn_v2_s7",
+      "seed": 7,
+      "use_diff_attn": true
+    }
+  ]
+}

results/phase_diagram.json

@@ -0,0 +1,70 @@
+{
+  "_description": "Phase diagram data: 64M-param GPT, 3 seeds per condition, 5K training steps on WikiText-103 subsets. Val loss (perplexity) at step 5000.",
+  "_model": {"n_layer": 12, "n_head": 8, "n_embd": 512, "block_size": 512, "params_M": 63.52},
+  "_training": {"batch_size": 64, "grad_accum": 1, "lr": 3e-4, "max_iters": 5000, "dtype": "bfloat16"},
+  "_hardware": "H100 NVL 96GB",
+
+  "wikitext_1m": {
+    "_regime": "overfitting (1M tokens, T/P = 16)",
+    "vanilla": {
+      "seeds": {"1337": 9.3397, "42": 9.4316, "7": 9.3803},
+      "mean": 9.3839, "std": 0.0376
+    },
+    "dyt": {
+      "seeds": {"1337": 6.7841, "42": 7.0431, "7": 6.6283},
+      "mean": 6.8185, "std": 0.1714
+    },
+    "diffattn": {
+      "seeds": {"1337": 9.6263, "42": 9.4142, "7": 9.4297},
+      "mean": 9.4901, "std": 0.0967
+    }
+  },
+
+  "wikitext_10m": {
+    "_regime": "transitional (10M tokens, T/P = 157)",
+    "vanilla": {
+      "seeds": {"1337": 4.2729, "42": 4.2555, "7": 4.2525},
+      "mean": 4.2603, "std": 0.0089
+    },
+    "dyt": {
+      "seeds": {"1337": 4.5177, "42": 4.5127, "7": 4.5000},
+      "mean": 4.5101, "std": 0.0074
+    },
+    "diffattn": {
+      "seeds": {"1337": 3.6894, "42": 3.6865, "7": 3.7413},
+      "mean": 3.7057, "std": 0.0252
+    }
+  },
+
+  "wikitext_50m": {
+    "_regime": "moderate (50M tokens, T/P = 787)",
+    "vanilla": {
+      "seeds": {"1337": 3.6734, "42": 3.6671, "7": 3.6565},
+      "mean": 3.6657, "std": 0.0069
+    },
+    "dyt": {
+      "seeds": {"1337": 4.4048, "42": 4.3598, "7": 4.3944},
+      "mean": 4.3863, "std": 0.0191
+    },
+    "diffattn": {
+      "seeds": {"1337": 3.3764, "42": 3.3743, "7": 3.3881},
+      "mean": 3.3796, "std": 0.0060
+    }
+  },
+
+  "wikitext": {
+    "_regime": "underfitting (118M tokens, T/P = 1858)",
+    "vanilla": {
+      "seeds": {"1337": 3.6398, "42": 3.6315, "7": 3.6215},
+      "mean": 3.6309, "std": 0.0075
+    },
+    "dyt": {
+      "seeds": {"1337": 4.2895, "42": 4.3025, "7": 4.3464},
+      "mean": 4.3128, "std": 0.0242
+    },
+    "diffattn": {
+      "seeds": {"1337": 3.3429, "42": 3.3643, "7": 3.3684},
+      "mean": 3.3585, "std": 0.0112
+    }
+  }
+}

results/rmsnorm_results.json

@@ -0,0 +1,39 @@
+{
+  "_description": "RMSNorm baseline comparison: RMSNorm vs LayerNorm vs DyT at 64M scale, 2 seeds. Shows DyT's advantage is not just over legacy LayerNorm.",
+  "_model": {"n_layer": 12, "n_head": 8, "n_embd": 512, "block_size": 512, "params_M": 63.52},
+  "_training": {"batch_size": 64, "lr": 3e-4, "max_iters": 5000},
+
+  "wikitext_1m": {
+    "_regime": "overfitting",
+    "rmsnorm": {
+      "seeds": {"1337": 9.2852, "42": 9.3495},
+      "mean": 9.3174
+    },
+    "vanilla_layernorm": {
+      "seeds": {"1337": 9.3397, "42": 9.4316},
+      "mean": 9.3857
+    },
+    "dyt": {
+      "seeds": {"1337": 6.7841, "42": 7.0431},
+      "mean": 6.9136
+    },
+    "_interpretation": "RMSNorm ~ LayerNorm in overfitting regime. DyT substantially better than both."
+  },
+
+  "wikitext": {
+    "_regime": "underfitting",
+    "rmsnorm": {
+      "seeds": {"1337": 3.6374, "42": 3.6316},
+      "mean": 3.6345
+    },
+    "vanilla_layernorm": {
+      "seeds": {"1337": 3.6398, "42": 3.6315},
+      "mean": 3.6357
+    },
+    "dyt": {
+      "seeds": {"1337": 4.2895, "42": 4.3025},
+      "mean": 4.2960
+    },
+    "_interpretation": "RMSNorm ~ LayerNorm in underfitting regime. DyT worse than both (as expected: regularization hurts when underfitting)."
+  }
+}

results/scaling.json

@@ -0,0 +1,65 @@
+{
+  "_description": "Scaling study: 4 model sizes (64M, 124M, 354M, 1.3B), 3 seeds per condition, 5K steps. Val loss at step 5000.",
+  "_hardware": "H100 NVL 96GB",
+
+  "scale1_64M": {
+    "_model": {"n_layer": 12, "n_head": 8, "n_embd": 512, "params_M": 63.52},
+    "_training": {"batch_size": 64, "grad_accum": 1, "lr": 3e-4},
+    "wikitext_1m": {
+      "vanilla": {"seeds": {"1337": 9.3397, "42": 9.4316, "7": 9.3803}, "mean": 9.3839},
+      "dyt":     {"seeds": {"1337": 6.7841, "42": 7.0431, "7": 6.6283}, "mean": 6.8185},
+      "diffattn":{"seeds": {"1337": 9.6263, "42": 9.4142, "7": 9.4297}, "mean": 9.4901}
+    },
+    "wikitext": {
+      "vanilla": {"seeds": {"1337": 3.6398, "42": 3.6315, "7": 3.6215}, "mean": 3.6309},
+      "dyt":     {"seeds": {"1337": 4.2895, "42": 4.3025, "7": 4.3464}, "mean": 4.3128},
+      "diffattn":{"seeds": {"1337": 3.3429, "42": 3.3643, "7": 3.3684}, "mean": 3.3585}
+    }
+  },
+
+  "scale2_124M": {
+    "_model": {"n_layer": 12, "n_head": 12, "n_embd": 768, "params_M": 124},
+    "_training": {"batch_size": 32, "grad_accum": 2, "lr": 3e-4},
+    "wikitext_1m": {
+      "vanilla": {"seeds": {"1337": 9.2711, "42": 9.1223, "7": 9.1099}, "mean": 9.1678},
+      "dyt":     {"seeds": {"1337": 8.3548, "42": 8.1694, "7": 8.3472}, "mean": 8.2905},
+      "diffattn":{"seeds": {"1337": 9.2063, "42": 9.3103, "7": 9.4452}, "mean": 9.3206}
+    },
+    "wikitext": {
+      "vanilla": {"seeds": {"1337": 3.4750, "42": 3.5081, "7": 3.5121}, "mean": 3.4984},
+      "dyt":     {"seeds": {"1337": 3.9396, "42": 3.9657, "7": 3.9297}, "mean": 3.9450},
+      "diffattn":{"seeds": {"1337": 3.0800, "42": 3.0732, "7": 3.0520}, "mean": 3.0684}
+    }
+  },
+
+  "scale3_354M": {
+    "_model": {"n_layer": 24, "n_head": 16, "n_embd": 1024, "params_M": 354},
+    "_training": {"batch_size": 16, "grad_accum": 4, "lr": 3e-4},
+    "wikitext_1m": {
+      "vanilla": {"seeds": {"1337": 8.6924, "42": 8.6669, "7": 8.5999}, "mean": 8.6531},
+      "dyt":     {"seeds": {"1337": 9.0159, "42": 9.0934, "7": 8.9647}, "mean": 8.9913},
+      "diffattn":{"seeds": {"1337": 8.8040, "42": 8.9241, "7": 8.8965}, "mean": 8.8749}
+    },
+    "wikitext": {
+      "vanilla": {"seeds": {"1337": 3.3800, "42": 3.3383, "7": 3.3458}, "mean": 3.3547},
+      "dyt":     {"seeds": {"1337": 3.8249, "42": 3.8076, "7": 3.7756}, "mean": 3.8027},
+      "diffattn":{"seeds": {"1337": 2.4059, "42": 2.4110, "7": 2.4412}, "mean": 2.4194}
+    }
+  },
+
+  "scale4_1300M": {
+    "_model": {"n_layer": 24, "n_head": 32, "n_embd": 2048, "params_M": 1311},
+    "_training": {"batch_size": 4, "grad_accum": 16, "lr": 1e-4},
+    "_note": "DiffAttn has 3 seeds; vanilla and DyT have 2-3 seeds",
+    "wikitext_1m": {
+      "vanilla": {"seeds": {"1337": 7.6581, "42": 7.6267, "7": 7.7943}, "mean": 7.6930},
+      "dyt":     {"seeds": {"1337": 7.7510, "42": 7.8667, "7": 7.9375}, "mean": 7.8517},
+      "diffattn":{"seeds": {"1337": 7.9719, "42": 7.9136, "7": 7.7529}, "mean": 7.8795}
+    },
+    "wikitext": {
+      "vanilla": {"seeds": {"1337": 3.3549, "42": 3.3348, "7": 3.3540}, "mean": 3.3479},
+      "dyt":     {"seeds": {"1337": 3.7066, "42": 3.6843, "7": 3.7006}, "mean": 3.6972},
+      "diffattn":{"seeds": {"1337": 2.3127, "42": 2.4194, "7": 2.3716}, "mean": 2.3679}
+    }
+  }
+}

results/train_val_gap.json

@@ -0,0 +1,58 @@
+{
+  "_description": "Train/val gap analysis at 64M scale. Gap = val_loss - train_loss at step 5000. Large positive gap = overfitting. Near-zero gap = healthy training.",
+  "_key_finding": "DyT reduces train/val gap by 50% in overfitting regime (1M tokens) by slowing memorization. In underfitting regime (118M tokens), all methods have near-zero gap.",
+
+  "overfitting_1M": {
+    "vanilla": {
+      "seeds": {
+        "1337": {"train": 0.1169, "val": 9.3397, "gap": 9.2228},
+        "42":   {"train": 0.1145, "val": 9.4316, "gap": 9.3171},
+        "7":    {"train": 0.1129, "val": 9.3803, "gap": 9.2674}
+      },
+      "mean_gap": 9.2691
+    },
+    "dyt": {
+      "seeds": {
+        "1337": {"train": 2.4711, "val": 6.7841, "gap": 4.3130},
+        "42":   {"train": 2.2356, "val": 7.0431, "gap": 4.8075},
+        "7":    {"train": 2.6913, "val": 6.6283, "gap": 3.9370}
+      },
+      "mean_gap": 4.3525
+    },
+    "diffattn": {
+      "seeds": {
+        "1337": {"train": 0.0794, "val": 9.6263, "gap": 9.5469},
+        "42":   {"train": 0.0829, "val": 9.4142, "gap": 9.3313},
+        "7":    {"train": 0.0831, "val": 9.4297, "gap": 9.3466}
+      },
+      "mean_gap": 9.4083
+    }
+  },
+
+  "underfitting_118M": {
+    "vanilla": {
+      "seeds": {
+        "1337": {"train": 3.6055, "val": 3.6398, "gap": 0.0343},
+        "42":   {"train": 3.6111, "val": 3.6315, "gap": 0.0204},
+        "7":    {"train": 3.6219, "val": 3.6215, "gap": -0.0004}
+      },
+      "mean_gap": 0.0181
+    },
+    "dyt": {
+      "seeds": {
+        "1337": {"train": 4.2873, "val": 4.2895, "gap": 0.0022},
+        "42":   {"train": 4.3137, "val": 4.3025, "gap": -0.0112},
+        "7":    {"train": 4.3905, "val": 4.3464, "gap": -0.0441}
+      },
+      "mean_gap": -0.0177
+    },
+    "diffattn": {
+      "seeds": {
+        "1337": {"train": 3.3610, "val": 3.3429, "gap": -0.0181},
+        "42":   {"train": 3.3602, "val": 3.3643, "gap": 0.0041},
+        "7":    {"train": 3.3724, "val": 3.3684, "gap": -0.0040}
+      },
+      "mean_gap": -0.0060
+    }
+  }
+}

results/vit_results.json

@@ -0,0 +1,55 @@
+{
+  "_description": "ViT experiments: DyT vs LayerNorm on CIFAR-10/100 with ViT-Small (4.8M params). 100 epochs, AdamW.",
+  "_model": {"dim": 256, "depth": 6, "heads": 4, "params_M": 4.77},
+
+  "cifar10_ln_s1337": {
+    "final_train_acc": 87.78, "final_val_acc": 70.02,
+    "final_train_loss": 0.3487, "final_val_loss": 0.9743,
+    "params_M": 4.771
+  },
+  "cifar10_dyt_s1337": {
+    "final_train_acc": 29.48, "final_val_acc": 30.11,
+    "final_train_loss": 1.8703, "final_val_loss": 1.8519,
+    "params_M": 4.771,
+    "_note": "DyT with alpha=2.0 catastrophically fails on ViT. Nearly random accuracy."
+  },
+  "cifar10_ln_s42": {
+    "final_train_acc": 94.72, "final_val_acc": 70.37,
+    "final_train_loss": 0.1565, "final_val_loss": 1.1609,
+    "params_M": 4.771
+  },
+  "cifar10_dyt_s42": {
+    "final_train_acc": 35.33, "final_val_acc": 36.45,
+    "final_train_loss": 1.6936, "final_val_loss": 1.6632,
+    "params_M": 4.771
+  },
+  "cifar100_ln_s1337": {
+    "final_train_acc": 99.16, "final_val_acc": 45.70,
+    "final_train_loss": 0.0944, "final_val_loss": 2.5757,
+    "params_M": 4.794
+  },
+  "cifar100_dyt_s1337": {
+    "final_train_acc": 6.68, "final_val_acc": 6.88,
+    "final_train_loss": 4.1154, "final_val_loss": 4.113,
+    "params_M": 4.794,
+    "_note": "DyT with alpha=2.0 completely fails on CIFAR-100 ViT."
+  },
+  "cifar100_ln_s42": {
+    "final_train_acc": 98.93, "final_val_acc": 45.86,
+    "final_train_loss": 0.1116, "final_val_loss": 2.5253,
+    "params_M": 4.794
+  },
+  "cifar100_dyt_s42": {
+    "final_train_acc": 7.95, "final_val_acc": 8.32,
+    "final_train_loss": 4.0324, "final_val_loss": 4.0125,
+    "params_M": 4.794
+  },
+
+  "_summary": {
+    "cifar10_ln_mean_val_acc": 70.20,
+    "cifar10_dyt_mean_val_acc": 33.28,
+    "cifar100_ln_mean_val_acc": 45.78,
+    "cifar100_dyt_mean_val_acc": 7.60,
+    "_interpretation": "DyT with alpha=2.0 is catastrophic for ViT. Alpha sweep (vit_alpha_sweep) shows alpha=0.5 recovers and exceeds LayerNorm."
+  }
+}