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README.md

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+---
+license: cc-by-4.0
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+  - forward-forward
+  - local-learning
+  - cifar-100
+  - layer-free-riding
+  - neurips-2026
+datasets:
+  - uoft-cs/cifar100
+metrics:
+  - accuracy
+---
+
+# Forward-Forward free-riding checkpoints (NeurIPS 2026)
+
+This repository holds four trained checkpoints from *Cumulative-Goodness Free-Riding in
+Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant* (Amirhossein
+Yousefiramandi, NeurIPS 2026). Each file is the validation-selected Stage-1 network of the
+seed-42 run of one of the four central CIFAR-100 arms in the paper's Table 3:
+
+- block-local training (γ=0);
+- hardness-gated collaboration (κ=0);
+- cumulative-goodness training (γ=0.7);
+- missing-gradient compensation (MGC, c_d=1).
+
+This is a minimal release. The paper's other 94 result-bearing checkpoints are listed with their
+SHA-256 hashes in `release_manifest.json` and are available from the author on request (see
+[Other checkpoints](#other-checkpoints-on-request)). They cover all seeds, the Stage-2 heads and
+the CIFAR-10 runs.
+
+The checkpoints are research artifacts. They let you re-evaluate and probe the four Table 3 arms
+without retraining Stage 1. They are not general-purpose image classifiers.
+
+- **Paper:** [arXiv:2605.06240](https://arxiv.org/abs/2605.06240)
+- **Code** (trainers, analysis, reproduction manifest; Apache-2.0):
+  <https://github.com/amirhossein-yousefi/ff-free-riding>
+- **Checkpoints** (this repository; CC BY 4.0): <https://huggingface.co/Amirhossein75/ff-free-riding>
+
+## Files
+
+| File | Arm | Selected Stage-1 epoch (val. top-1) | Bytes | SHA-256 |
+|---|---|---|---:|---|
+| `checkpoints/c100_trio/ff_cifar100_cifar100_gamma0_seed42_L4_D256_P2_seed42_stage1_best.pt` | γ=0 (block-local) | 362 (65.72 %) | 211467565 | `d2e2a0548597355d633c7c3acad1ef01c274323f279ca1055e77a1c807e2848c` |
+| `checkpoints/c100_trio/ff_cifar100_cifar100_gated_k0_seed42_L4_D256_P2_seed42_stage1_best.pt` | hardness-gated κ=0 | 340 (66.22 %) | 211484697 | `58e0c770f80a4f70d9d6c814884ecbb1caf1162a270caf3294282b67d69fd4ca` |
+| `checkpoints/c100_trio/ff_cifar100_cifar100_cumulative_seed42_L4_D256_P2_seed42_stage1_best.pt` | cumulative γ=0.7 | 340 (66.22 %) | 211487237 | `537cf9e2e27e73009596a9de9f52097ee98d33ad1055783a8f434e20ab44cc3b` |
+| `checkpoints/mgc_c100/ff_cifar100_mgc_c100_L4_D256_P2_seed42_stage1_best.pt` | MGC (c_d=1) | 362 (66.96 %) | 211449657 | `6776a87e9afa3dcf6abc993b0e17646724b3b9f185ada825eebd54977c2c1a66` |
+
+The four files total 845,889,156 bytes (846 MB). All four arms share one setup: CIFAR-100,
+L4/D256 dense (no MoE), no SAM, 362 Stage-1 epochs, seed 42, and the same seed-42 train/validation
+split. The repository also contains:
+
+```
+README.md                this model card
+SHA256SUMS               sha256sum-compatible list of the four checkpoint files
+release_manifest.json    all 98 result-bearing checkpoints of the paper: run, paper tables, trainer,
+                         source, lineage, size, SHA-256; `availability` is "public_hf" for the four
+                         files here and "on_request" for the others
+```
+
+Each file stores the following keys:
+
+- `net_ema`: the EMA weights, which give the S1 numbers;
+- `model`: the raw weights;
+- `cfg`: the run's full configuration, identical to the run's `config.json` except `device`;
+- `epoch_stage1`: the selected epoch;
+- `best_s1`: the validation top-1 at that epoch;
+- `stage`: 1;
+- `opt_states`, `sched_states` and `queues`: the optimizer states, the scheduler states and the
+  contrastive feature queues.
+
+## Numbers behind each file
+
+| Arm | S1 | S1-TTA | S2 (logged) | S2-TTA (logged) |
+|---|---:|---:|---:|---:|
+| γ=0 | 66.17 | 66.92 | 68.27 | 68.94 |
+| κ=0 (gated) | 66.07 | 66.85 | 68.29 | 68.64 |
+| Cumulative γ=0.7 | 66.70 | 67.18 | 68.09 | 68.57 |
+| MGC | 66.05 | 66.79 | 68.71 | 69.19 |
+
+The values are CIFAR-100 test top-1 (%) of the seed-42 runs. They enter the 3-seed means of
+Table 3 (`tab:dissociation_merged`). The seed-42 columns of the frozen-backbone readout table
+(`tab:readouts`, appendix) are computed on these four backbones.
+
+- **S1 and S1-TTA** are the Stage-1 readouts of these files. S1 is the goodness-sum readout of the
+  EMA weights on a single crop; S1-TTA adds the horizontally flipped image. S1 was re-predicted
+  from each file on the full test set and matched the run logs to within 0.01 pp: 66.18, 66.07,
+  66.70 and 66.06 against logged S1 values of 66.17, 66.07, 66.70 and 66.05. S1-TTA was not
+  re-predicted; it is tied to these files by lineage (each run's `events.jsonl` names the file in
+  its final test evaluation; see [Run history and disclosures](#run-history-and-disclosures)).
+- **S2 and S2-TTA** come from the Stage-2 attentive head. This head is a backprop-trained probe on
+  the frozen backbone, not part of the FF method, and its checkpoints (`stage2_best.pt`) are not in
+  this release. To get S2 numbers, retrain the probe as described in
+  [Retraining the Stage-2 probe](#retraining-the-stage-2-probe). The original heads are available
+  on request.
+- **Deepest-block separation** (the "sep" column of Table 3) is a training-log quantity: it was
+  read at the last Stage-1 epoch (362), not recomputed from the validation-selected checkpoint.
+  The code repository ships the per-seed values in `metric_summaries/camera_ready/`.
+
+## Loading
+
+The network is defined by the CIFAR-100 trainer in the code repository, which works for all four
+files. MGC changes only the training loss, not the network.
+
+```bash
+git clone https://github.com/amirhossein-yousefi/ff-free-riding && cd ff-free-riding
+pip install -r requirements.txt huggingface_hub
+```
+
+```python
+import importlib.util, sys, torch
+from huggingface_hub import hf_hub_download
+from torchvision import datasets, transforms
+
+path = hf_hub_download("Amirhossein75/ff-free-riding",
+    "checkpoints/c100_trio/ff_cifar100_cifar100_gamma0_seed42_L4_D256_P2_seed42_stage1_best.pt")
+
+spec = importlib.util.spec_from_file_location("ff_c100", "trainers/cp_fair_cifar100.py")
+T = importlib.util.module_from_spec(spec); sys.modules["ff_c100"] = T; spec.loader.exec_module(T)
+
+ckpt = torch.load(path, map_location="cpu", weights_only=True)
+cfg = T.FFConfig()
+for k, v in ckpt["cfg"].items():            # the run's configuration is stored in the checkpoint
+    if hasattr(cfg, k):
+        setattr(cfg, k, v)
+cfg.device = "cuda" if torch.cuda.is_available() else "cpu"
+net = T.FFHybridNet(cfg).to(cfg.device)
+net.load_state_dict(ckpt["net_ema"])        # EMA weights: the S1 / S1-TTA numbers
+net.eval()
+
+tf = transforms.Compose([transforms.ToTensor(),
+                         transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))])
+test = datasets.CIFAR100("./data_c100", train=False, download=True, transform=tf)
+loader = torch.utils.data.DataLoader(test, batch_size=256, shuffle=False)
+acc = T.evaluate_ff(net, loader, cfg.device, tta=False, topk=(1,))   # tta=True gives S1-TTA
+print(f"S1 top-1: {100 * acc[1]:.2f} %")    # logged: 66.17 for this file
+```
+
+- **`weights_only`.** The files are PyTorch pickles. With PyTorch 2.14.0, all four load with
+  `weights_only=True`, because they contain only tensors and plain Python containers. If your
+  PyTorch version refuses a file, `torch.load(..., weights_only=False)` will load it, but that
+  setting executes arbitrary pickle code. Use it only after `sha256sum -c SHA256SUMS` passes. The
+  trainer's own `torch.load` calls, used by the Stage-2 retraining below, do not pass the argument,
+  so they fall back to PyTorch's default.
+- **Side effects.** Importing the trainer creates an empty `./checkpoints/` directory, and
+  `download=True` fetches CIFAR-100 into `./data_c100/`.
+- **Speed.** The goodness-sum readout scores all 100 label hypotheses for every image. On CPU it
+  took about 1 s per test image with 4 threads, so use a GPU for the full test set.
+
+## Retraining the Stage-2 probe
+
+When a Stage-1 checkpoint is complete, the trainer skips Stage 1 and does the following:
+
+1. It trains only the Stage-2 head: 20 epochs, AdamW, on the frozen EMA backbone, with the same
+   seed-42 train/validation split, which the trainer regenerates from the seed.
+2. It runs the final test evaluation: S1, S1-TTA, S2 and S2-TTA, written to `events.jsonl` in a
+   new run directory.
+
+This needs a GPU.
+
+```bash
+# one arm per invocation; EP must equal the checkpoint's epoch_stage1
+ARM=gamma0;     EP=362; ENV="FF_GAMMA_SCALE=0"
+# ARM=gated_k0; EP=340; ENV="FF_GAMMA_GATING_MODE=adaptive FF_GAMMA_0=0.7 FF_GAMMA_KAPPA=0.0 FF_GAMMA_TAU=1.0"
+# ARM=cumulative; EP=340; ENV="FF_GAMMA_SCALE=0.7"
+F=ff_cifar100_cifar100_${ARM}_seed42_L4_D256_P2_seed42_stage1_best.pt
+huggingface-cli download Amirhossein75/ff-free-riding "checkpoints/c100_trio/$F" --local-dir ./hf
+mkdir -p ckpt_s2 && cp "hf/checkpoints/c100_trio/$F" ckpt_s2/ && cp "ckpt_s2/$F" "ckpt_s2/${F/_best/_last}"
+env $ENV FF_CKPT_DIR=./ckpt_s2 FF_VERSION_TAG=cifar100_${ARM}_seed42 FF_EPOCHS_STAGE1=$EP \
+  FF_NUM_BLOCKS=4 FF_D_MODEL=256 FF_NUM_HEADS=8 FF_USE_SAM=0 FF_N_EXPERTS=1 FF_MOE_TOP_K=1 \
+  FF_SEED=42 python trainers/cp_fair_cifar100.py
+
+# MGC (EP=362)
+F=ff_cifar100_mgc_c100_L4_D256_P2_seed42_stage1_best.pt
+huggingface-cli download Amirhossein75/ff-free-riding "checkpoints/mgc_c100/$F" --local-dir ./hf
+mkdir -p ckpt_s2 && cp "hf/checkpoints/mgc_c100/$F" ckpt_s2/ && cp "ckpt_s2/$F" "ckpt_s2/${F/_best/_last}"
+FF_CKPT_DIR=./ckpt_s2 FF_VERSION_TAG=mgc_c100 FF_USE_MGC=1 FF_MGC_C0=1.0 FF_N_EXPERTS=1 FF_MOE_TOP_K=1 \
+  FF_USE_SAM=0 FF_NUM_HEADS=8 FF_GAMMA_GATING_MODE=constant FF_GAMMA_SCALE=0.7 FF_SEED=42 \
+  python trainers/cp_fair_cifar100.py
+```
+
+The trainer loads two files from `FF_CKPT_DIR`:
+
+- It resumes Stage 1 from `<run>_stage1_last.pt`.
+- It starts Stage 2 and the final Stage-1 evaluation from `<run>_stage1_best.pt`.
+
+Both copies are therefore needed.
+
+If `FF_EPOCHS_STAGE1` is larger than the checkpoint's `epoch_stage1` (340 for κ=0 and cumulative),
+the trainer resumes Stage-1 training instead of skipping it. The per-arm environment matches the
+arm's row in the code repository's `repro_manifest.csv`, with `FF_EPOCHS_STAGE1` set to the selected
+epoch.
+
+A retrained head is not bitwise identical to the paper's. The trainer that produced these runs did
+not store RNG state, so the Stage-2 data order differs. Expect S2 values close to the logged ones,
+not equal to them.
+
+## Run history and disclosures
+
+- **Not resumed.** None of the four released runs was resumed. Each ran from Stage-1 epoch 1 to
+  epoch 362 in one run directory, and its layer log starts at epoch 1. The resumed runs reported in
+  the paper are all among the on-request files: γ=0 seeds 123/456, κ=0 seed 456, cumulative seeds
+  123/456 and MGC seed 456 on CIFAR-100, plus some CIFAR-10 runs. The trainers that produced them
+  did not save RNG state, so a resumed run is not a bitwise continuation of an uninterrupted one.
+  `RELEASE_CHECKPOINTS.md` in the code repository lists them.
+- **Lineage.**
+  - All four runs were trained on Colab A100 GPUs.
+  - The three trio files are the extracted copies of the runs' Google Drive archive members, with
+    equal CRC-32 and size.
+  - Each file is the `stage1_best` that the result-bearing run's `events.jsonl` names in its final
+    test evaluation.
+  - Each file's `best_s1` equals the run's logged best validation accuracy, and its stored `cfg`
+    equals the run's `config.json`.
+- **Trainers.**
+  - The trio was trained with the v1 CIFAR-100 trainer, and the MGC run with the rebuttal-era MGC
+    trainer.
+  - The camera-ready `trainers/cp_fair_cifar100.py`, with its switches off, or with `FF_USE_MGC=1`
+    for MGC, builds the same network. The code repository's CPU equivalence tests check this, and
+    all four `net_ema` state dicts load into it with every key matched.
+- **MGC.** The MGC arm replaces the baseline's depth-scaled current-block term: block 0 loses its
+  auxiliary and has no compensator. The implementation is faithful but not an exact recovery
+  (ε=10⁻⁶, positive-part clamp).
+
+## Other checkpoints (on request)
+
+`release_manifest.json` lists all 98 result-bearing checkpoint files of the paper:
+
+- the `stage1_best.pt` and `stage2_best.pt` of 49 runs on CIFAR-10 and CIFAR-100;
+- for each file: its run, the paper tables it supports, its trainer and configuration, its source
+  and lineage, its size and its SHA-256.
+
+The 94 files marked `"availability": "on_request"` are available from the author on request
+(amir.usefi75@gmail.com). A recipient can check them against the SHA-256 values in the manifest.
+The following are not available at all:
+
+- the published CIFAR-10 γ=0 L4/D256 seed-42 checkpoint, which is not recoverable;
+- the Tiny ImageNet, text-domain and BP-control checkpoints (the paper reports their run logs and
+  metrics only);
+- the seed-42 run of the CIFAR-100 32-expert γ=0.7 baseline, which was not retained.
+
+## Integrity
+
+`SHA256SUMS` pins the four checkpoint files by their SHA-256 over the full file bytes, and
+`release_manifest.json` records the same hashes. To verify a download:
+
+```bash
+sha256sum -c SHA256SUMS
+```
+
+The release was staged with `release/stage_release.py --subset minimal` from the code repository.
+
+## License
+
+- **Checkpoints:** Creative Commons Attribution 4.0 International (CC BY 4.0). If you use them,
+  please cite the paper (below).
+- **Code:** Apache-2.0 (the `LICENSE` file of the GitHub repository).
+- **Datasets:** the checkpoints were trained on CIFAR-100, which is not redistributed here. A
+  checkpoint stores network weights, optimizer and scheduler states and the contrastive feature
+  queues, which hold projected features of training images and their labels. It contains no
+  images. Use of the dataset itself follows its authors' terms.
+
+## Dataset
+
+The four files were trained on CIFAR-100 (Krizhevsky, 2009), loaded with torchvision:
+
+- The 50,000 training images are split into 45,000 for training and 5,000 for validation.
+- The split is seeded with the run seed (42 for all four files), so the four share it.
+- The 10,000-image test set is used only for the final evaluation.
+
+## Intended use and limitations
+
+These checkpoints are for studying FF training. Possible uses:
+
+- per-block separation and goodness;
+- readouts and probes of the frozen backbones;
+- prediction churn between the four arms.
+
+The paper reports the accuracies, protocols and seed-level variation: Table 3, Appendix B and the
+appendix readout tables. A single seed per arm cannot reproduce the paper's paired multi-seed
+statistics, because those need the on-request files or retraining. The models are small
+CIFAR-scale classifiers trained with a local learning rule. They were not evaluated for
+robustness, fairness or out-of-distribution use.
+
+## Citation
+
+```bibtex
+@inproceedings{yousefiramandi2026cumulative,
+  title     = {Cumulative-Goodness Free-Riding in Forward-Forward Networks: Real, Repairable, but Not Accuracy-Dominant},
+  author    = {Yousefiramandi, Amirhossein},
+  booktitle = {Advances in Neural Information Processing Systems (NeurIPS 2026)},
+  year      = {2026}
+}
+```

SHA256SUMS

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release_manifest.json

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+{
+ "_meta": {
+  "title": "Checkpoint release manifest: Cumulative-Goodness Free-Riding in Forward-Forward Networks (NeurIPS 2026)",
+  "generated": "2026-09-25",
+  "hf_repo": "https://huggingface.co/Amirhossein75/ff-free-riding",
+  "code_repo": "https://github.com/amirhossein-yousefi/ff-free-riding",
+  "release_policy": "Minimal public release: only the 4 files with availability 'public_hf' (subset 'minimal': the seed-42 stage1_best checkpoints of the four central CIFAR-100 arms of main-text Table 3, gamma=0, gated kappa=0, cumulative gamma=0.7 and MGC) are uploaded to the Hugging Face repository. Every other file listed here ('on_request') is available from the author on request (amir.usefi75@gmail.com); its SHA-256 below lets the recipient verify it. The Stage-2 heads (stage2_best.pt) are not public: retrain the Stage-2 probe from a released stage1_best.pt (RELEASE_CHECKPOINTS.md, 'Retraining the Stage-2 probe').",
+  "availability_values": {
+   "public_hf": "uploaded to the Hugging Face repository at hf_path (license CC BY 4.0)",
+   "on_request": "not on the Hub; available from the author on request. hf_path is the path that release/stage_release.py --subset full gives the file when it is staged for sharing"
+  },
+  "subsets": {
+   "minimal": {
+    "description": "public Hugging Face release: seed-42 stage1_best of the CIFAR-100 gamma=0, gated kappa=0, cumulative gamma=0.7 and MGC arms (main-text Table 3)",
+    "n_files": 4,
+    "total_bytes": 845889156,
+    "files": [
+     "checkpoints/c100_trio/ff_cifar100_cifar100_gamma0_seed42_L4_D256_P2_seed42_stage1_best.pt",
+     "checkpoints/c100_trio/ff_cifar100_cifar100_gated_k0_seed42_L4_D256_P2_seed42_stage1_best.pt",
+     "checkpoints/c100_trio/ff_cifar100_cifar100_cumulative_seed42_L4_D256_P2_seed42_stage1_best.pt",
+     "checkpoints/mgc_c100/ff_cifar100_mgc_c100_L4_D256_P2_seed42_stage1_best.pt"
+    ]
+   },
+   "full": {
+    "description": "every result-bearing checkpoint listed in this manifest (the 4 public files plus the 94 available on request)",
+    "n_files": 98,
+    "total_bytes": 54444410714
+   }
+  },
+  "hashing": "SHA-256 over the full file bytes (Python hashlib, 8 MiB chunks). Archive members were streamed from the Google Drive export zips without extraction; zipfile verified each member's CRC-32. Extracted copies were cross-checked against their archive member's CRC-32.",
+  "source_paths": "relative to FF_HOME (the root that contains amir-porjects/ and ff-next/); 'archive.zip::member' means a member of that zip file",
+  "files_per_run": "stage1_best.pt (validation-selected Stage-1 network incl. EMA weights; S1/S1-TTA numbers) and stage2_best.pt (Stage-1 network + Stage-2 head; S2/S2-TTA numbers). *_last.pt resume files are not part of the release.",
+  "n_files": 98,
+  "total_bytes": 54444410714,
+  "resumed_runs_note": "None of these trainers saved RNG state in checkpoints, so a resumed run is not a bitwise continuation of an uninterrupted run; the camera-ready trainers save and restore it.",
+  "lineage_notes": [
+   "MGC-D128 seed 42 comes from the local DGX Spark (GB10) run (mgc_rebuttal/checkpoints); seeds 123/456 from Colab.",
+   "Squared-hinge seed 42 (both arms) comes from the Colab run in mgc_rebuttal/checkpoints_from_drive. The mgc_rebuttal/checkpoints/ff_cifar10_hinge_*seed42* files are from aborted DGX runs (no final evaluation) and are NOT released.",
+   "CIFAR-100 trio: stage1_best from the extracted copies in ff-next/checkpoints/trio (CRC-32 equal to the archive members), stage2_best streamed from ff_cifar100_multiseed-20260425T214118Z-3-00{1..7}.zip."
+  ],
+  "not_released": [
+   "CIFAR-10 gamma=0 L4/D256 seed 42 (the published run): checkpoint not recoverable; the only seed-42 gamma=0 checkpoints left come from the component-ablation run, not the published run.",
+   "Tiny ImageNet, text-domain and BP-control runs: not part of this release (run logs and metrics only).",
+   "CIFAR-100 32-expert MoE gamma=0.7 baseline (tab:cifar100_multiseed): seed-42 run not retained."
+  ],
+  "groups": {
+   "c100_trio": {
+    "title": "CIFAR-100 dissociation trio (gamma=0, kappa=0 adaptive, cumulative gamma=0.7), L4/D256 dense, no SAM, 362+20 epochs, seeds 42/123/456",
+    "paper": [
+     "tab:cifar100_dissociation_multiseed",
+     "tab:dissociation_merged",
+     "tab:readouts (seed 42)",
+     "tab:probes",
+     "tab:readout_ladder"
+    ],
+    "trainer": "trainers/cp_fair_cifar100.py (v1 file; flags off)"
+   },
+   "mgc_c100": {
+    "title": "MGC (c0=1) CIFAR-100 L4/D256 dense, no SAM, 362+20 epochs, seeds 42/123/456",
+    "paper": [
+     "tab:mgc_full",
+     "tab:readouts"
+    ],
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+   },
+   "mgc_d128": {
+    "title": "MGC (c0=1) CIFAR-10 L4/D128, SAM on, 180+10 epochs, seeds 42/123/456",
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+     "tab:gradshare (seed-42 MGC arm, stage1_best)"
+    ],
+    "trainer": "trainers/cp_fair_hardness_gated.py with FF_USE_MGC=1 (rebuttal file mgc_trainer.py)"
+   },
+   "hinge": {
+    "title": "Squared-hinge barrier, CIFAR-10 L4/D128, SAM on, 180+10 epochs; gamma=0.7 (g07) and gamma=0 (g0) arms, seeds 42/123/456",
+    "paper": [
+     "tab:hinge"
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+     "tab:freeriding (seed 42)",
+     "tab:dissociation_merged (CIFAR-10 pair)"
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+    "title": "CIFAR-10 gamma=0, L4/D256, SAM on, 362+20 epochs, seeds 123/456 (the seed-42 checkpoint is not recoverable)",
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+     "tab:gamma0_multiseed",
+     "tab:dissociation_merged (CIFAR-10 pair)"
+    ],
+    "trainer": "trainers/cp_fair_cifar10.py with FF_GAMMA_SCALE=0 (v1 file)"
+   },
+   "gated_l4_d128_seed42": {
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+     "tab:dissociation_full",
+     "tab:gated",
+     "Fig. 1 / F_d (baseline_constant, no_all_collab, lcff_prefix)",
+     "tab:gradshare (gamma=0.7, gamma=0)"
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+    "trainer": "trainers/cp_fair_hardness_gated.py (produced by its pre-locality-fix predecessor)"
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+   "gated_k0_l4_d128": {
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+    "trainer": "trainers/cp_fair_hardness_gated.py (v1 file; flags off)"
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