180 lines
10 KiB
Markdown
180 lines
10 KiB
Markdown
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---
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kind: finding
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date: 2026-07-25
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immutable: true
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---
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# Findings — Manifold “Push away” vs upstream geometric dislike
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_Read-only comparison, 2026-07-25. “Confirmed” claims cite source; “Inference” labels
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interpretation. No implementation decision is made here._
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## Scope and source identity
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The hardware repository named in the question,
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[`MusicallyEmbodiedML/MEMLNaut`](https://github.com/MusicallyEmbodiedML/MEMLNaut),
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does not contain the active ML implementation. The firmware repository is
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[`MusicallyEmbodiedML/MEMLNaut-NISPS`](https://github.com/MusicallyEmbodiedML/MEMLNaut-NISPS/tree/701f2d9f1b4698e0ddfa147193928489de12601f),
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whose `src/memllib` submodule pins
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[`MusicallyEmbodiedML/memllib` at `e291192`](https://github.com/MusicallyEmbodiedML/memllib/tree/e291192d8e4f2fca7b79670c4df9c2ec8bdf03cd).
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The local read-only copies of upstream `InterfaceRL.hpp`, `.tpp`, and `.cpp` have Git
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blob hashes `03e9255`, `9ec762b`, and `60204d1`, respectively—the same blobs returned
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by GitHub for that pin. The comparison below is therefore against the exact upstream
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source, not an approximation. Provenance is recorded locally in
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`firmware/MEMLNaut-NISPS/lib/memllib/reference/README.md:1-15`.
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## Confirmed behaviour
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### The mental model is substantially right, but “different” needs a target
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A negative verdict has no supervised label by itself. Upstream defines “different” as:
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take the output that was heard, find the mean output of the four liked positions nearest
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the current control input, and create a new target one unit farther from that liked
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centroid in output space. With no likes—or a degenerate zero-length direction—it uses a
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random direction. The target is clamped to `[0,1]`
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([upstream `InterfaceRL.tpp:698-761`](https://github.com/MusicallyEmbodiedML/memllib/blob/e291192d8e4f2fca7b79670c4df9c2ec8bdf03cd/examples/InterfaceRL.tpp#L698-L761);
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local mirror `firmware/MEMLNaut-NISPS/lib/memllib/reference/InterfaceRL.tpp:698-761`).
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That target is then passed to `synthMapping.TrainBatch`. Inference subsequently reads
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the same MLP through `synthMapping.GetOutput`. Therefore current upstream push-away is
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**weight training on the mapping MLP**, not a separate rejection layer after inference
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([upstream training call](https://github.com/MusicallyEmbodiedML/memllib/blob/e291192d8e4f2fca7b79670c4df9c2ec8bdf03cd/examples/InterfaceRL.tpp#L757-L761);
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[upstream inference path](https://github.com/MusicallyEmbodiedML/memllib/blob/e291192d8e4f2fca7b79670c4df9c2ec8bdf03cd/examples/InterfaceRL.tpp#L869-L890)).
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OU noise and `paramTransformHook` do exist after MLP inference, but are independent of
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the dislike algorithm (`InterfaceRL.tpp:877-890`).
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The Manifold path has the same basic semantics. The UI passes its current post-output-
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pipeline vector to `FeedbackController.dislike`, which calls the WASM geometric-dislike
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entry point (`manifold/src/console/ConsoleApp.tsx:509-535`;
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`manifold/src/feedback/controller.ts:347-373`;
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`manifold/src/engine/wasm-iml.ts:956-978`). The C++ controller computes the target and
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calls `MLPCore::train_targets`, which runs forward propagation, backpropagation and one
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RMSProp update on the same network weights (`nisps/ml/feedback.hpp:395-477`;
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`nisps/ml/mlp.hpp:250-287`).
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### The target maths now matches current upstream, but the training schedule does not
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Current Manifold main now matches upstream’s untapered target formula and constants:
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`kGeometricPushScale=1.0`, `kNegLRBase=1.5`, no `/(1+distance)` taper
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(`nisps/ml/geo_push.hpp:1-110`; upstream `InterfaceRL.hpp:406-414` and
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`InterfaceRL.tpp:723-761`). It also now uses the upstream RMSProp update rather than
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interpreting an upstream RMSProp learning rate as plain SGD
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(`nisps/ml/training.hpp:1-92`).
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The remaining major divergence is dose:
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- Upstream stores the dislike at press time, then its main loop calls `optimise()` on
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subsequent cycles (`InterfaceRL.tpp:38-54,186-232`).
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- Every optimisation scans **all** live negatives and batch-trains them again
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(`InterfaceRL.tpp:673-761`).
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- Upstream computes one liked centroid around the **current live control input** and
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applies it to every negative in that cycle, even if the user has moved away from the
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original disliked position (`InterfaceRL.tpp:698-755`). Manifold instead computes the
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centroid at the just-pressed negative’s stored input (`nisps/ml/feedback.hpp:431-467`).
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- A negative remains at full strength for 2500 ms, then expires; the number of updates
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depends on the mode’s loop rate (`InterfaceRL.hpp:406-414`).
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- Manifold collapses press and optimisation into **one synchronous
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`train_targets` call for only the just-pressed negative**, then proportionally decays
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stored negatives (`nisps/ml/feedback.hpp:395-477`; `nisps/ml/replay.hpp:165-185`).
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There is no background/per-frame feedback optimiser.
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Thus a Manifold click names a strongly displaced target, but takes only one step toward
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it. Upstream keeps walking toward its target for the next 2.5 seconds. This is the most
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direct explanation for a remaining perceptual strength difference.
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As checked on 2026-07-25, the WASM served by
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`https://meml.lnfinitemonkeys.org/next/nisps.wasm` has SHA-256
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`d1c58a59517a00c6f51870ea1ec21194561b81058e22bbfb2e11de4af45c645a`, exactly matching
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`manifold/public/nisps.wasm` on current main. The reported live behaviour therefore
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cannot be explained by production still serving the pre-RMSProp or tapered binary.
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### Upstream also cancels a nearby positive; Manifold does not
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Upstream’s default replay policy is `REPLACE_10_PERCENT`
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(`InterfaceRL.hpp:404-405`). When a negative is stored, a positive within input-space
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distance `0.10` is removed before the negative is added
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(`InterfaceRL.tpp:904-929,950-979`). Manifold’s replay method only deepens or adds a
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negative and leaves positives intact (`nisps/ml/replay.hpp:102-121`). Manifold also
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keeps liked examples in the separate MLP dataset
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(`manifold/src/feedback/controller.ts:375-390`).
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**Inference:** this is less about the first click’s amplitude than persistence. A later
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positive training run can pull the mapping back toward a sound rejected near an
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existing like, whereas upstream removes that local positive from its continuously
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trained replay set.
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### Current measured scale
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On current main, the native behavioural benchmark at shape `2→16→16→16→8`, seed
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`24301`, reports:
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- one geometric dislike: at-point L2 movement `0.05335`;
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- one legacy undirected Diffuse dislike: `0.22626`;
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- repeated geometric dislikes: `0.05314` after 1, `0.33748` after 10, `1.09504`
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after 100.
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Commands:
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```bash
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scripts/bench-ml.sh --native-only --scenario A4_negative_once
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scripts/bench-ml.sh --native-only --scenario D1_geo_anatomy
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```
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These numbers confirm that the current path is no longer inert, but also that a single
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geometric press is still about 4.2× smaller than the legacy random-diffusion gesture in
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this benchmark. They do not by themselves establish the right musical feel.
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At Manifold’s current default PAF shape (`4→10→10→14→33`), the same seeded scenarios
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report one-click movement `0.09654` geometric versus `0.20788` Diffuse (about 2.2×
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smaller), and geometric movement `0.55935` after ten presses. These are vector L2
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distances across 33 parameters, so they establish that weights move; they do not prove
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that the affected parameters produce a perceptually obvious timbral change.
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The more revealing PAF-shape `A12_like_then_dislike` journey dislikes exactly where a
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liked target was taught. With one update, distance from that rejected liked target
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changes from `0.38291` to `0.36030` (`rejection_moved=-0.02261`): the mapping moves, but
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slightly **toward** the particular target the user just rejected. Ten updates change the
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distance to `0.67843` (`rejection_moved=+0.29552`). This is deterministic evidence that
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one update is not sufficient to realise the user-facing semantic in an important
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contradictory-feedback case; it also motivates testing upstream’s nearby-like removal
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separately.
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## Why it likely felt weak
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1. **Fixed today: optimiser mismatch.** Before `f57cddc`, the port used a tiny upstream
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RMSProp learning rate inside plain SGD, reducing one press to roughly `5.3e-5`
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movement.
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2. **Fixed today: superseded push formula.** Before `ec31180`, the port halved the target
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step, used one-third the negative-LR base, and tapered the step by distance—the exact
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case upstream says made “no” ineffective.
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3. **Still present: one update versus a time window of updates.** Manifold performs one
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weight update per click; upstream replays all dislikes repeatedly for 2.5 seconds.
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4. **Still present: highly asymmetric teaching dose.** A Manifold like trains the whole
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positive dataset at `lr=1.0` for up to 1000 iterations, while a dislike takes one
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roughly `0.0015` RMSProp step. Relative to the lurch caused by a like, correction
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still feels small (`ALIGNMENT.md:66-84`).
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5. **Potential persistence mismatch:** nearby likes are retained locally but removed by
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upstream’s default replay policy, so future positive retraining may partly undo the
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rejection.
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## Recommended next experiment
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Do not start by increasing `geo_lr` blindly. That can make a click stronger but does not
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answer whether the intended interaction is one discrete correction or a short-lived
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repulsive constraint.
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Add a deterministic “advance feedback by `dt`” core seam and benchmark three matched
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variants from the same seeded prefix:
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1. current one-shot update;
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2. upstream-style replay of all negatives for 2500 ms, while separately testing whether
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centroid lookup follows each stored negative or upstream’s current live input;
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3. a bounded discrete equivalent (for example 10/25/50 steps at press time) that avoids
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wall-clock ownership in the core.
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For each, report at-point movement, neighbourhood rings, global blast ratio, collateral
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movement at liked positions, and like→dislike→like persistence. Then perform an
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in-Manifold blind A/B at the real per-mode output arities and choose the smallest dose
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that makes one rejection obvious without damaging liked regions. Test the nearby-like
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removal policy as a separate axis rather than coupling it to dose.
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