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f57cddc278
Upstream memlp (github.com/MusicallyEmbodiedML/memlp @ ea777502, the commit
upstream/main pins) applies gradients with RMSProp everywhere: Layer.h:239
ApplyAccumulatedGradients, the m_sq_grad_avg running average at Layer.h:601,
StaticMLP.h:268. nisps/ml/training.hpp shipped SGD only and filed the
difference as an optimiser-choice research question. It was not one.
RMSProp divides each step by the running gradient magnitude, so an upstream
lr is a NORMALISED step; under SGD the same number multiplies the raw
gradient. Every learning rate ported from upstream therefore landed in an
optimiser that reads it differently — most visibly feedback.hpp's
`geo_lr_ = 0.001f // upstream InterfaceRL.hpp:312`, an RMSProp LR pasted
into a single SGD step.
rmsprop_step() ports Layer.h:239 exactly: clip at +/-10, sq = min(0.9*sq +
0.1*g^2, 1e6), adj = min(lr/(sqrt(sq)+1e-6), 1.0), w -= adj*g. The
adjusted-LR clamp stays one-sided as upstream's std::min is, so the negative
lr used by train_targets' "train away from this target" path behaves as it
does upstream. The per-weight squared-gradient average is new persistent
state and lives in the storage policies (FixedStorage arrays /
DynamicStorage arena) so nisps/ stays allocation-free and the firmware's
zero-heap contract holds. It is optimiser state, not model state: excluded
from weight_count()/get_weights()/set_weights(), matching upstream, and
cleared by MLPCore::reset_optimizer_state() (upstream ResetOptimizerState).
draw_weights() deliberately does NOT clear it — upstream's DrawWeights
doesn't either.
Measured with tests/cpp/ml_bench.cpp:
D1 one geometric dislike moves the mapping 1.6e-2, up from 5.3e-5 (~295x),
and repeated presses now CONVERGE on the intended 0.5 push (0.12 at 10,
0.56 at 100) instead of creeping linearly forever.
A4 geometric-vs-Diffuse gap narrows from ~4100x to ~14x in one press.
U4 the upstream-LR positive path actually trains now (range_util 0.71 at
100 ticks/gesture, was 0.016 — it was inert under SGD).
Not fixed by this, and now tracked as ALIGNMENT defect 6d: the dose
asymmetry. lurch_max is still ~1.08 against a [0,1] output range.
Golden vector stages 2 and 3 re-captured; stages 0 and 1 are pre-training
and did not move, which is the cross-check that only the update rule
changed. manifold/public/nisps.wasm rebuilt so parity-check compares like
with like — it FAILED at up to 5e-2 against the stale artifact and PASSES at
2.4e-7 against a fresh one. parity-check.sh only builds the WASM when it is
missing, never when it is stale; noted in MAP.md and filed separately.
ALIGNMENT defect 6 resolved (moved to Recently resolved); 6b's optimiser
cross-reference updated; new defect 6d for the positive-training dose.
Gates: build-cpp-tests 138 tests / ctest 4/4, parity-check PASS, lint-cpp
clean, manifold typecheck clean.
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|---|---|---|
| .github/workflows | ||
| .vscode | ||
| assets/media | ||
| codegen | ||
| docs | ||
| firmware | ||
| manifold | ||
| nisps | ||
| schemas | ||
| scripts | ||
| tests/cpp | ||
| vcv | ||
| .envrc | ||
| .gitignore | ||
| AGENTS.md | ||
| ALIGNMENT.md | ||
| CLAUDE.md | ||
| LICENSE | ||
| MAP.md | ||
| README.md | ||
Neural Interactive Shaping of Parameter Spaces
https://musicallyembodiedml.github.io/memlnaut/approaches/nisps
Firmware
The hardware firmware targets the MEMLNaut RP2350 build and uses repo-local helper scripts for the known-good build configuration:
git submodule update --init --recursive
scripts/build-firmware.sh
scripts/flash-firmware.sh
scripts/build-and-flash-firmware.sh
Notes:
- The scripts build for
rp2040:rp2040:solderparty_rp2350_stamp_xlwithOptimize3. - The build forces C++20 because the firmware uses
std::spanand concepts. build-firmware.shaccepts an optional variant name such asMEMLCeliumorBreakOr. Matching remains case-insensitive, somemlceliumstill works. If you omit it in an interactive shell, the script parsesMEMLNaut-NISPS.ino, prompts for a variant, and rewrites the activeMEMLNAUT_MODE_TYPEbefore building.flash-firmware.shaccepts an optional mountpoint argument, or auto-detects common UF2 bootloader mounts such as/run/media/$USER/RP2350and/run/media/$USER/RPI-RP2.
Manifold (browser app)
Try NISPS in your browser — no hardware required. Manifold is the React front-end running the same C++ engines + ML as the firmware, compiled to WASM:
cd manifold
bun install
bun run dev
Staging deployment: https://meml.lnfinitemonkeys.org/next/
Train a neural network to map input gestures to synth parameters through interactive machine learning: place examples, or use verdict-based feedback (explore-and-place, geometric dislike).
(The former SolidJS playground was retired in July 2026 — archived on branch
archive/playground-solidjs, tag playground-solidjs-final.)