Replace the vendored runtime MLP in vcv/src/iml.hpp (DetRng + 3D-weight-store
MLP + Dataset + IML) with a THIN, Rack-free adapter over the shared core:
nisps::ml::MLPCore<nisps::ml::DynamicStorage> (8->[16,24,16]->16, the P2 dynamic
case), nisps::Rng, and the core MLP's own FIFO dataset. Behaviour changes from
the vendored approximation to core-exact firmware/WASM semantics.
- MEMLNaut.cpp: staged/pending weight buffers and patch JSON now use the core's
flat [weights..][biases..] vector (nisps::IML<float>::Weights); patch version
bumped to 3. Double-buffer / single-writer threading discipline unchanged.
- New ctest tests/cpp/test_vcv_iml_parity.cpp: seeded train/infer/move_weights
session through the adapter is memcmp-equal to a bare MLPCore<DynamicStorage>.
- Docs: vcv-module.md delta #5 marked CLOSED (2026-07-18); MAP.md vcv/ updated.
Closes vcv-module.md delta #5.
8 inputs x 16 outputs; per-output LED ring widget (drawLayer+nvgArc); palette
from frontend tokens; OSC bridge verbs for bidirectional browser training;
vendored self-contained iml.hpp (retired nisps-core); compiles against Rack
SDK 2.6.4. See SPEC.md BUILD DELTAS.
Fixes from Opus 4.6 review (C1-C6, I1, I3, I4, I8):
C1: static lastOutputs → per-instance lastOutputsForDelta member
C2: add_example() now on audio thread only (owns iml); worker reads
from mutex-protected staging area (stagedFeatures/stagedLabels)
C3: Worker reads stagedWeightsForWorker (not iml.get_weights()),
eliminating concurrent read/write on iml's MLP
C4: Worker spins on weightsPending before writing pendingWeights,
preventing double-write race
C5: RAND and CLEAR now enqueue Randomize/Clear jobs through worker
instead of directly mutating iml on the audio thread
C6: OSC callbacks stage JSON into oscStagedJson + atomic flag;
audio thread applies in process() (no recv-thread mutation)
Also fixed:
- I4: Separate pendingJob field (enqueueJob no longer overwrites currentJob)
- I8: Removed redundant swapReady atomic
- noiseLevel, cachedNovelty, cachedConfidence now std::atomic<float>
- Worker syncs examples back to iml after training via load_examples()
Replace direct-mutation threading with shadow IML:
- Background thread clones weights from main → shadow IML
- Training and perturbation operate only on shadow instance
- New weights staged in pendingWeights, swapped atomically by audio thread
- Thumbs-down now enqueues Perturb job instead of calling move_weights directly
- Audio thread applies new weights via iml.set_weights() at safe point
- Examples copied to shadow for training, results copied back as weights only
Threading invariant now fully enforced: background thread never writes to
the inference IML. Audio thread applies staged weights between inference calls.
