hub expansion plan

Model-hub expansion — architecture and implementation plan

Audience: the engineer or agent implementing this, in a fresh session, with no memory of the conversation that produced it. Everything needed is either here or named by file and line.

Goal: move the hub from six static-image reference checkpoints to a catalog that demonstrates spiking networks on the workloads that justify them — event vision first, then audio — without weakening a single honesty guarantee the project already makes.

Read first: spikeforge_hub/CURATION.md is the policy this plan operates under and does not amend. Where this plan and that document disagree, that document wins and this plan is wrong.

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0. The strategic frame, so the work is not mis-scoped

The hub's differentiator is not size. It cannot out-scale Hugging Face and must not try. What nothing else in this field offers is a catalog where every entry is NIR-exportable, drift-validated, deployment-reported, energy-estimated, checksum-pinned, and reproducible by a printed command. Ten entries where every claim is checkable beat a thousand where none are.

So: breadth across modalities, not across architectures. The current six entries are all MNIST-family static images — breadth on the axis that matters least, and on the one workload where a reviewer knows a conventional network is the better answer. One DVS128 Gesture checkpoint changes what the hub demonstrates more than twenty more MNIST variants would.

Keep the reference-configuration philosophy exactly as CURATION.md states it: stock hyperparameters, modest epochs, one seed, honest numbers, a reproduce command, and explicitly not a state-of-the-art claim.

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1. 🔴 Blocking discovery: event "test accuracy" is currently train accuracy

This must be fixed before any event-dataset checkpoint or benchmark row is produced. Nothing else in this plan is safe to start first.

The event training path has no train/test split anywhere in it. The train flag exists at the top of the chain, is threaded nowhere, and is silently ignored at the bottom. Traced end to end:

Layer	File	What it does
Dataset spec	spikeforge/data/datasets.py	kwargs hardcodes {"train": True} for n_mnist and dvs128_gesture, {"split": "train"} for ssc. cifar10_dvs passes no split at all.
Tonic construction	spikeforge/data/event_loader.py _dataset()	cls(_root(spec, save_to), **spec.kwargs) — no split parameter exists
Sample source	spikeforge/events/event_source.py EventSampleSource.__init__	no split parameter
Batch assembly	spikeforge/training/event_batches.py event_batches()	signature is (source, spec, subset, batch_size) — no split parameter
Engine	spikeforge/training/event_engine.py _epoch_batches(self, train=True)	accepts train and never reads it — always returns the same batches

The consequence: EventTrainingEngine._load_test_batches(), whose docstring says "Cache a few bridged event batches for held-out scoring", returns the first batches of the training stream. Every accuracy the event path can currently report is train accuracy wearing a test label.

For a project whose entire positioning rests on numbers a reader can trust, publishing an event benchmark on top of this would be the worst possible unforced error. It is also a real bug independent of this plan — the dashboard reports the same misleading number today.

1.1 Fix shape

Thread a split through all five layers. Two design decisions worth making deliberately rather than discovering:

(a) Split selection differs per dataset. NMNIST and DVSGesture take train: bool; SSC takes split: "train"|"test"; CIFAR10DVS has no split at all (it is a single pool and needs a deterministic partition, or it must be declared unsupported for held-out scoring — say which, do not quietly use all of it). Do not paper this over with a boolean. Extend DatasetSpec to carry explicit per-split kwargs, e.g. a splits: Dict[str, Dict[str, Any]] mapping "train"/"test" to the kwargs that select it, and make a missing "test" entry a typed, named error rather than a silent fallback to train.

(b) Two sources, not a threaded flag. The image path already works by holding separate loaders. Mirror it: EventTrainingEngine holds a train EventSampleSource and a test EventSampleSource, and _epoch_batches(train) picks between them. This keeps event_batches() unchanged and puts the split where it belongs — in the source that knows which dataset it opened.

(c) The synthetic backend needs a disjoint test stream. EventSampleSource falls back to a deterministic synthetic generator when tonic is absent, and the test suite relies on it (synthetic_only=True). If both splits generate the same samples, the regression test below passes vacuously. Give the synthetic test stream a disjoint index range or a different seed, and state in the docstring that synthetic "held-out" data is generated, not recorded.

1.2 Required regression test

A test that would have caught this, in tests/ alongside the existing event tests. It must assert the split is real, not merely that a flag is accepted:

• _epoch_batches(train=True) and _epoch_batches(train=False) return different tensors for the same engine and seed.
• EventSampleSource(..., train=False) opens a dataset whose length or content differs from train=True (under tonic), and a disjoint sample range (under synthetic).
• A dataset with no declared test split raises the typed error by name rather than returning training data.

1.3 Blast radius — already established, do not re-investigate

Checked while writing this plan, so it can be scoped calmly rather than treated as an incident:

• The public dashboard is not affected. tonic is not installed in the deployed image (absent from both requirements.txt and the Dockerfile, and confirmed absent inside a built container), so EventTrainingEngine._source() raises EventsExtraMissingError rather than returning a number. The event path fails honestly there instead of reporting a wrong accuracy. The public demo is read-only on top of that.
• Who is actually affected: anyone who installs the events extra locally and trains on an event dataset. Real, but bounded — and nobody has published a number from it.

So this is a correctness bug to fix properly and in sequence, not a hotfix.

1.4 Honesty follow-through

_load_test_batches's docstring currently states something untrue. Fix it. Check whether any dashboard copy, documentation/ page, or panel label presents an event accuracy as held-out, and correct anything that does. Record the bug and its blast radius in CHANGELOG.md under Fixed — the project's own standard is that a wrong number gets named, not quietly corrected.

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2. Phase 0 — dataset provenance in the catalog schema

Do this before producing new entries. Retrofitting attribution onto already published entries is worse than shipping it with the first batch.

2.1 The gap

HubEntry records dataset (a bare name) and license — and that license describes the weights, not the data they encode. Nothing records what the training data permits.

That is already live: reference/kmnist-fc-legacy ships marked "license": "BSD-3-Clause", but KMNIST is CC BY-SA 4.0 with a requested attribution which appears nowhere in the artifact. N-MNIST — the obvious next event target — is CC BY-SA 4.0 as well.

Whether trained weights constitute "adapted material" under a ShareAlike licence is genuinely unsettled; the prevailing ML norm says they do not, and Creative Commons themselves state their licences are not designed to govern model weights. This plan does not take a position on that question. It removes the need to have one: record the provenance, carry the attribution, let a reader judge. That is the same move the project already makes everywhere else.

2.2 Verified dataset licences

Each of these was checked against the dataset's own publisher during the session that wrote this plan — not carried over from memory. Re-verify rather than trusting this table: the authority is the publisher's page, and a licence can change.

Dataset	Licence	Attribution
DVS128 Gesture	CC BY 4.0	required
SHD / SSC	CC BY 4.0	required
N-MNIST	CC BY-SA 4.0	required, ShareAlike
KMNIST	CC BY-SA 4.0	required; CODH requests a specific wording
Fashion-MNIST	MIT	required

Not yet verified — the implementer must confirm and must not guess: MNIST itself (no explicit licence on the original distribution; commonly cited as CC BY-SA 3.0) and CIFAR10-DVS. If a licence cannot be confirmed from a primary source, record it with the project's existing unverified-candidate discipline rather than inventing one — that marker exists precisely for this.

2.3 Schema change

In spikeforge_hub/entry.py:

• Add dataset_license and dataset_attribution to _FIELDS and the HubEntry dataclass.
• In _check_reference(), require both for source: "reference", exactly as weights / sha256 / dataset / test_accuracy are required today. The rationale is identical: a trained entry that cannot say what its data permits is not one the project can stand behind.
• Validate dataset_license with the same rule as license — reuse _check_license's concrete-SPDX-or-marker logic rather than writing a second, looser one. Free-text escapes must be rejected in both fields.

Then:

• Extend _catalog_entry() in scripts/train_reference_models.py to emit both fields, sourced from a single provenance table — put it next to the dataset registry so it cannot drift from the datasets it describes.
• Surface both on the public page in scripts/build_hub_page.py (a Data column, or a line under the notes). The page is HTML-escaped already; keep it that way.
• Backfill all six existing entries via --publish, not by hand.
• Add the dataset attributions to NOTICE.md.

2.4 Tests

Extend tests/test_hub_reference_weights.py: a reference entry missing either new field is rejected by name; a free-text dataset licence is rejected; the shipped catalog has both fields populated on every reference entry.

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3. Phase 1 — DVS128 Gesture reference checkpoint

The entry that changes what the hub demonstrates. Chosen over N-MNIST deliberately: CC BY 4.0 rather than ShareAlike, a genuinely event-native task rather than a re-recording of MNIST, and the canonical "why spikes" example.

3.1 What already works — do not rebuild it

• conv_net already accepts the needed geometry. See spikeforge/topology/presets.py: conv_net(in_channels=1, channels=8, num_classes=10, input_size=28, ...), and input_size accepts an int square side or an explicit (H, W) pair. No new preset is required.
• Reference in scripts/train_reference_models.py already carries a topology_params field that reaches the builder.
• EventTrainingEngine already bridges events, validates sensor geometry, and reuses the shared loop, metrics and checkpointing.

So the configuration is:

Reference(
    name="dvs128-gesture-conv-net",
    dataset="dvs128_gesture",
    topology="conv_net",
    topology_params={"in_channels": 2, "num_classes": 11, "input_size": 128},
    ...
)

Read num_classes and the sensor geometry from the dataset registry rather than hardcoding them in two places; DatasetSpec already carries the class count.

3.2 What must change in the training script

Two concrete gaps, both in scripts/train_reference_models.py:

1. Engine selection. _train() does from spikeforge import TrainingEngine and instantiates it directly. Event datasets need EventTrainingEngine. Branch on the registry's modality, not on the dataset name.

2. Full-split scoring. _full_test_accuracy() calls build_loader(...), and build_dataset raises by design for event modality ("train it through the event batch path instead of build_dataset"). An event branch is required: score over the complete held-out set from the test source — every batch, not the EVAL_BATCHES truncation _load_test_batches() applies. The existing helper's contract ("the complete held-out split, not a sample of it") must hold identically for events, or the number is not comparable to the image rows.

Note that this scoring path is only meaningful after §1 lands. Implementing it first against the unsplit source produces a confidently wrong number.

3.3 Cost and size — measure before committing

• Wall clock. Event samples are bridged per-sample, not read from a tensor file; expect this to be materially slower per epoch than the 249 s MNIST conv_net row. _shrink_progress_evaluation() already exists for exactly this reason and matters more here. Time a small-subset run first and report the projection before launching a full one. If a CPU run is implausible, stop and ask rather than quietly reducing the training split — subset is a divisor, and shrinking it changes what the published number means.
• Checkpoint size. conv_net at 128×128 pools to 32×32 with 16 channels → 16 384 flattened features → ~180 k readout parameters, on the order of 1 MB. That likely fits CURATION.md's "few megabytes" wheel budget, but measure it; the six current checkpoints total 1.6 MB.
• If it does not fit, CURATION.md already prescribes the answer: "source": "url" with a checksum, downloaded on demand. Be aware that no catalog entry uses that path today — tests/test_hub_download_routing.py exercises routing, but nothing real has ever been fetched through it. Treat a first use as a feature to verify end to end (download, checksum verify, cancel, cache reuse, corrupted-file rejection), not as a path you can assume works.

3.4 Outputs

documentation/benchmarks.md gains a row; the catalog gains an entry; the README's "What it scores" table gains a row. All three are generated by --publish — do not hand-edit any of them. Update the README's framing so the table is no longer implicitly all-MNIST.

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4. Phase 2 — an audio checkpoint (SSC or SHD)

Only after Phase 1 lands and is reviewed. Gives three modalities: static vision, event vision, audio.

Higher risk, and the reasons are known in advance:

• Split shape differs. SSC uses split: "train"|"test", not a boolean. If §1.1(a) was implemented as a boolean, this phase will require reworking it — which is why §1.1(a) specifies explicit per-split kwargs.
• Geometry is not spatial. SSC is 700 channels, 35 classes. The engine's _check_features() requires input_size == height * width against tonic's sensor_size. fc_legacy with input_size=700 is the likely fit, but the implementer must confirm what tonic reports for SSC's sensor_size before assuming — _shape() in event_loader.py unpacks it as (width, height, channels).
• SHD is the smaller, cleaner sibling and is a legitimate substitute if SSC proves awkward. Both are CC BY 4.0.

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5. Explicitly out of scope

• No state-of-the-art claims. Not in the entry, the notes, the page, the README, or a commit message. The published framing is "a floor the shipped defaults reach, not a ceiling" and it stays.
• No more MNIST-family variants. More rows on the existing axis is the specific thing this plan exists to stop.
• No relabelling bundled as trained. CURATION.md names this as the fabrication the policy exists to prevent.
• No softening of the spec-only target status. spinnaker2, speck and xylo remain registered-but-uninstallable, and the energy figure remains an estimate.
• No third-party weights redistributed. Reference entries are this project's own artifacts.
• No new SNN capability. No new neuron models, encoders or targets; this is a data, provenance and correctness plan.

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6. Sequencing

Strictly ordered. Each step is reviewable on its own.

1. §1 — event train/test split, with the regression test and the changelog entry. Nothing downstream is trustworthy without it.
2. §2 — provenance schema, with backfill of the existing six.
3. §3 — DVS128 Gesture, timing probe reported before the full run.
4. §4 — audio, only after 3 is reviewed.

Steps 1 and 2 are independent of each other and may be done in either order, but both precede step 3.

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7. Acceptance criteria

A reviewer should be able to check each of these without re-deriving anything:

• EventTrainingEngine._epoch_batches(train=True) and (train=False) return demonstrably different data, proven by a test that fails if the split is removed.
• A dataset with no declared test split raises a typed, named error instead of returning training data.
• Every source: "reference" entry carries a concrete dataset_license and a dataset_attribution; validation rejects an entry missing either, and rejects free-text in the licence field.
• The six existing entries are backfilled, and the attributions appear in NOTICE.md and on https://spikeforge.net/hub/.
• A DVS128 Gesture entry exists whose accuracy was measured on the complete held-out split, with the reproduce command in its notes, and whose sha256 and size_bytes match the shipped bytes (re-verify by hashing the packaged file, not by trusting --publish).
• documentation/benchmarks.md, the README table and the catalog agree on every number, because all three were generated.
• Full suite green, ruff clean, mypy clean, scripts/check_wiki_links.py and scripts/check_readme_links.py clean, packaging guards clean.
• Any claim of "verified" is backed by a command in the work report that a reviewer can re-run.

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8. Questions for the maintainer — ask, do not assume

1. CPU or GPU for the event run? If the timing probe in §3.3 projects an implausible CPU run, the options are a GPU run (which changes the hardware line in the benchmark table and makes the number less reproducible for a reader on a laptop) or a smaller configuration. That is a product decision, not an implementation one.
2. MNIST's licence. If it cannot be confirmed from a primary source, confirm that recording it as an unverified candidate is acceptable rather than asserting CC BY-SA 3.0 from secondary sources.
3. Release batching. spikeforge-hub went to 0.2.0 carrying weights. Should each phase cut a release, or should they batch into one? Note the tag-push caveat in ecosystem_listings.md's sibling history: push release tags one at a time, since GitHub suppresses workflow events when several tags arrive in one push.
4. CIFAR10-DVS has no upstream split. Deterministic partition, or declare it unsupported for published accuracy?