memlnaut-nisps/docs/adr/rl-feedback-design.md

RL / Feedback Learning-Engine Design — Manifold

Design sign-off. Dated 2026-06-27. Supersedes the AVOID-out-of-scope decision in docs/specs/plans/feedback-modes-port-spec.md §2.5/§7 (reclassified kind: plan, status: executed 2026-07-21). Author: lead architect, synthesising a 4-candidate / 4-judge design panel against verified source.

---

0. Decision summary

Setting	Default	Also selectable
FEEDBACK_MODE	Explore and place (Mode 2, positive-only)	Geometric dislike (Mode 1)
SOLO_MODE	MaskGradients (final-layer column freeze)	ZeroLoss, DontCareExample

Winner of the panel: Sift (musician-UX judge topPick, skeptic topPick; 2nd on feasibility and ML). It is the only candidate that does UX design rather than only porting design, and it satisfies the "Mode 2 is NEVER a dislike" requirement at the level of the performer's mental model.

Grafted onto Sift's felt-loop spine:

• Minimal-Divergence — the only candidate whose tree-read is correct (verified below): the feedback C API, MLHandle.feedback, the 0xFEEDBACC0DE salt, CMakeLists.txt:59 registration, and parity Stage 5 ALL already exist. Its append-only FeedbackAction enum discipline and controller-returns-action / caller-owns-training boundary contract are kept verbatim.
• Anchored-Manifold — the only candidate with correct provenance (the upstream tip is 0a541cc "highlighting", verified; not the e291192/abe93ec the others cite). Its one-ReplayStore-three-features compression, its bit-identical-frozen-column solo ctest, and its explicit TrainBatch-vs-SGD divergence flag are adopted.
• Anchor-First — keep replay/geometric logic OUT of the MLP kernel (only minimal backprop hooks go in), and port the posMemCount==0 cold-start fallback faithfully so dislikes-before-any-like don't destabilise the net.

---

1. Verified ground truth (read this before building)

All claims below were read from source on 2026-06-27. Where the four candidates disagreed, the verified fact decides.

1.1 The scaffold already exists (Minimal-Divergence was right)

• nisps/wasm/bindings.cpp:95-116 — struct MLHandle already holds nisps::ml::FeedbackController<DefaultMLP> feedback; + feedback_static_scratch{}, constructed feedback(seed ^ 0xFEEDBACC0DEull).
• nisps/wasm/bindings.cpp:439-535 — the 9 nisps_ml_feedback_* C functions (set_mode/get_mode/exploring/learning_paused/set_focus/down/up/drag/static_output) are already wired.
• nisps/CMakeLists.txt:58-59 — test_mlp_rl.cpp and test_mlp_feedback.cpp are already registered in nisps_core_tests.
• tests/cpp/parity_check.cpp:86 — kVersion = 2u already; :198-205 — Stage 5 (feedback) already exists seeding FeedbackController<ParityMLP> fb(kSeed ^ kFeedbackSalt).

Candidates 2/3/4 proposed adding MLHandle.feedback, registering test_mlp_feedback.cpp, and creating Stage 5. That is phantom work — they already exist. This design edits the existing on_down cases and extends the existing enum/API/parity-stage; it does not re-scaffold them.

1.2 The current feedback.hpp (untracked, verified nisps/ml/feedback.hpp:1-214)

• enum FeedbackMode {Avoid=0, RandomiseOutputs=1, RandomiseMlp=2} (:47-51); enum FeedbackAction {None=0,…Restore=7} (:56-65).
• on_down Avoid case (:103-105) calls mlp.move_weights(speed, spread, pin_mask) — the undirected-diffusion placeholder the requirement now forbids for Mode 1.
• RandomiseMlp on_down (:113-119): 1st press enters (snapshot_ + draw_weights), 2nd press cancels. Snapshot/restore is a byte-exact get_weights/set_weights round-trip (:186-193).
• The controller has its own per-instance Rng rng_ (:210), seeded independently.

1.3 The latest upstream algorithm (Anchored-Manifold's provenance was right)

The actual upstream tip is 0a541cc "highlighting" at /home/w1n5t0n/src/MEMLNaut-NISPS-upstream/src/memllib/examples/InterfaceRL.{hpp,cpp}. Constants verified:

• InterfaceRL.hpp:293 kGeometricPushScale = 0.5f; :294 kMaxDislikeMultiplier = 16; :296 kCentroidK = 4.
• InterfaceRL.cpp:42-66 _perform_dislike_action(): nearby negative within Euclidean 0.05f of controlInput → reward = max(reward-1, -16); else storeExperience(-1, controlInput, action); then dislikeMultiplier_ = min(*2, 16).
• InterfaceRL.cpp:602-627 k-NN centroid: average the kCentroidK=4 positive memories nearest controlInput → meanPositiveAction.
• InterfaceRL.cpp:664 proportional decay reward += 0.0025f * max(|reward|, 1.0f); expired items removed; dislikeMultiplier_ halved per expiry (:752-760).
• InterfaceRL.cpp:713 pushStep = clamp(|avgRewardNeg|, 0.25f, 1.0f) * kGeometricPushScale.
• InterfaceRL.cpp:721-735 per negative: dir[j] = neg_action[j] - meanPositiveAction[j]; len = ||dir||; useRandom = (len <= 1e-4f); effectivePushStep = pushStep / (1 + len); for each j gated by activeDims_: d = useRandom ? (rand()&0xFF/127.5 - 1) : dir[j]/len; target[j] = clamp(neg_action[j] + d*effectivePushStep, 0, 1).
• InterfaceRL.cpp:742-743 negLRRatio = 0.5f - 0.4f*negFraction; train geometric targets at lr*negLRRatio.
• InterfaceRL.cpp:746 cold-start fallback when posMemCount==0: TrainBatch(tsNegative, lr * 0.1f * avgRewardNeg, …) — negative-LR training, no geometric push.

1.4 The deepest parity hazard (Anchored-Manifold / Anchor-First flagged it)

Upstream optimise() trains with TrainBatch (shuffled, batchSize≈8, separate positive/geometric batches at two distinct learning rates). The nisps MLP::train() (mlp.hpp:256-300) is unshuffled per-sample SGD over the dataset insertion order (its own comment: "TrainBatch shuffles, but we're not implementing batch yet"), has no reward field and no train-toward-arbitrary-targets entry. Reproducing the two-batch / dynamic-negLRRatio dynamics on a non-batch trainer is the real browser != firmware line. We accept behavioural (not bitwise) parity with firmware here, and pin native == WASM at 1e-5. Recorded in ALIGNMENT.md.

1.5 The injection points the masking/solo mechanism needs

• mlp.hpp:281 — the per-sample std::array<float,NOut> deriv{} is the exact place to zero the output-error of non-soloed / don't-care dims before backprop_ (:291). One branch per output dim.
• rl.hpp:74-99 — move_weights_layer already skips the final-layer weight column + bias when is_final_layer && output_pin_mask[node] != 0. Solo's column-freeze is the bitwise inverse of this mask. Note the existing rule (:88-96): the RNG is advanced even on skipped weights so the stream is pin-independent — keep that invariant.
• mode-runtime.ts:548-565 thumbsDown currently builds a pin mask and calls moveWeights — no example stored, no training. thumbsUp (:523-546) computes the (features, labels) and calls addExample + trainOnCurrent.

---

2. FEEDBACK_MODE = two operator-switchable modes

The selector is exploration-store.ts state feedbackMode: 'explore-and-place' | 'geometric-dislike', surfaced in the learning-behaviour dock panel (§7). British spelling in all copy ("randomise", "auditioning", "centre", "behaviour").

2.1 Mode 1 — "Geometric dislike" (negative example + geometric push-away)

Felt loop: you have a region you like; thumbs-down carves the current sound away from the liked centroid. Directed repulsion, audibly directional — the fix for today's inaudible undirected diffusion (findings-feedback-behaviour.md §5).

Algorithm — port InterfaceRL.cpp (0a541cc) into nisps core, keeping parity. Because nisps has no async optimise() driver, the press-time half (_perform_dislike_action) and the optimise half collapse into one synchronous core call.

On thumbs-down at controlInput x_neg with heard action a_neg:

1. replay.deepen_or_store_negative(x_neg, a_neg, radius=0.05f) — nearby negative → reward = max(reward-1, -16.f); else store reward=-1. dislikeMultiplier_ = min(*2, 16). (InterfaceRL.cpp:42-66)
2. If replay.positive_count() == 0 → cold-start fallback: train_targets(x_neg → a_neg, lr * 0.1f * avgRewardNeg, …) (InterfaceRL.cpp:746). Surface a one-time UI prompt "like a few sounds first to teach the system what to move away from" (closes the cold-start gap all four judges flagged).
3. Else: replay.knn_positive_centroid(x_neg, k=4, &mean) (:602-627); pushStep = clamp(|avgRewardNeg|, 0.25f, 1.0f) * 0.5f; per active/soloed dim j: dir[j]=a_neg[j]-mean[j], len=||dir||, effectivePushStep=pushStep/(1+len), d = (len<=1e-4f) ? rng_.next_float_signed() : dir[j]/len, target[j]=clamp(a_neg[j]+d*effectivePushStep, 0, 1); non-active dims keep a_neg[j].
4. train_targets(x_neg → target, lr * negLRRatio, …) with negLRRatio = 0.5f - 0.4f*negFraction.
5. replay.decay_negatives() each call: reward += 0.0025f*max(|reward|,1), evict reward > -0.01f.

Determinism carve-out: the upstream useRandom branch uses libc rand()&0xFF; we substitute rng_.next_float_signed() (the controller's own per-instance nisps::Rng). Both native and WASM run the same Rng from the same seed → 1e-5 parity holds. This branch only fires when a disliked action sits exactly on the centroid. Record as the single deliberate firmware divergence in ALIGNMENT.md.

On thumbs-up (Mode 1): replay.store(+1, x, a) so the centroid sees positives; caller also runs the existing addExample+train like today.

Files touched: edit feedback.hpp on_down Avoid case (:103-105) → route to the geometric path; add replay_ member; on_up LikeStore → replay.store(+1,…). New nisps/ml/replay.hpp + nisps/ml/geo_push.hpp (§4). move_weights stays reachable as a legacy Diffuse sub-mode for A/B comparison (cheap insurance, lets the A/B-compare feature contrast geometric vs diffusion — graft from Minimal-Divergence).

2.2 Mode 2 — "Explore and place" (positive-only; NEVER a dislike) — DEFAULT

Felt loop (Sift's north star): "randomise, explore, oh I like that → put it in that corner; meh, randomise…". The performer never reasons about "away from what" — they collect sounds they like. Place-by-corner is the highest audible-effect-per-action gesture in the system: one drop = one heard anchor the real model then interpolates.

Built on the existing RandomiseMlp scaffold (feedback.hpp:113-119,183-201) but to the stricter spec. Lifecycle state machine in the reworked FeedbackController, FeedbackMode::ExploreAndPlace:

1. ENTER (verdict-down/explore): enter_scratchpad(mlp, spread) — snapshot the real trained net into snapshot_ (the set-aside model; existing get_weights round-trip), set learning_paused_=true (gates trainOnCurrent + auto-explore, mirroring upstream optimiseSometimes early-return), then draw_weights(spread) → random scratchpad net.
2. SCRATCHPAD OPS — exactly two, position-agnostic, never trained, never a dislike:
   • reroll() = draw_weights(spread) again ("meh, randomise…"). Re-press while exploring re-rolls (returns Reroll); a dedicated cancel gesture / mode-switch aborts. (This changes the old 2nd-press-cancels semantics — deliberate; rewrite the affected ctest.)
   • nudge(speed, spread) = a small move_weights on the scratchpad with a templated undo ring scratch_undo_ (depth D; WASM D=4, firmware D=2 via template param — Sift's free SRAM tunable). undo_nudge() pops. Reuses the byte-exact get_weights/set_weights round-trip (graft: no new parity surface). New actions ScratchNudge, ScratchUndo.
   • The scratchpad is NEVER trained and NEVER reads replay — auditioning is pure forward inference as the user sweeps the joystick.
3. PLACE (user likes a sound): press → choose a location in input space → release. place_begin() freezes the current scratchpad output vector into placed_out_ (so the heard sound is held while the user aims; emitted via the existing static_output bypass hook so the audition doesn't change). The TS runtime owns the "aim" UI (a JoyMap drag). On release: caller sets the scratchpad input to chosen_input, runs mlp.process(), reads outputs, calls place_commit(chosen_input, scratch_out_at_chosen) → appends a positive anchor and returns AnchorPlaced. The controller does NOT itself run inference (keeps it pure — Minimal-Divergence's boundary contract).
4. WARM-START INTERPOLATION (explicit "Done"/finalise gesture): restore the set-aside net (set_weights(snapshot_)), then warm-start it to interpolate all placed anchors. Decision (graft from all four risk-lists): warm-start is ADDITIVE — anchors are added to the existing dataset, NOT clear_examples()+refit — so the user's prior thumbs-up likes are not clobbered. The caller loops addExample(input, output) over replay.anchors() then train(lr, maxIter, minErr) (caller owns training). "Warm-start" = begin from the restored real net, not a fresh draw, so prior structure is preserved. Returns WarmStarted; learning_paused_ flips false.
5. ABORT (mode-switch / cancel): restore the set-aside net, discard anchors not yet finalised.

Anchor store = the same ReplayStore instance, positives only (§4). Coupling decision (Sift's flag): unifying Mode-1 replay and Mode-2 anchors into one store means a placed anchor also feeds Mode-1's positive centroid. This is a feature (mixed-mode sessions), but expose an operator toggle unifyMemories (default on) so they can be isolated into two ReplayStore instances if surprising.

Why Mode 2 is the default: highest audible-effect-per-action, no negative concept to learn, and it directly realises the operator's stated felt loop. Mode 1 is the precision/sculpting tool for when you already have a liked region.

---

3. SOLO / arm per output

State: solo_mask_ : std::array<std::uint8_t, kNOut> on the controller (1=armed/soloed, 0=frozen; all-zero / empty ⇒ none soloed ⇒ normal training). API set_solo_mask(span) / clear_solo_mask(), C API nisps_ml_feedback_set_solo(ml, mask, n) (mirrors the existing set_focus at bindings.cpp:485). One operator setting SOLO_MODE {MaskGradients=0, ZeroLoss=1, DontCareExample=2}.

All three flow through one injection point — the per-sample deriv array at mlp.hpp:281, fused per Sift's graft (zeroing the loss on non-soloed outputs is the don't-care mask).

3.1 MaskGradients (DEFAULT) — column-freeze

Predictable: "only this output moves." Derive pin_mask = bitwise-NOT(solo_mask) over outputs. In a new train_masked(lr, max_iter, min_err, out_mask): zero deriv[j] for non-soloed j before backprop_, and freeze hidden layers when any solo is active (skip layer0_/1_/2_.apply_grad; apply only layer3_ for soloed columns). Result: non-soloed final-layer columns AND all hidden weights are bit-identical after training. This is the exact inverse of rl.hpp:74-99's pin gating — minimal new code, reuses proven machinery.

3.2 ZeroLoss (selectable) — expressive-but-bleeds

Zero deriv[j] for non-soloed j but let hidden layers update. The soloed output gets the full network's expressive capacity, but non-soloed outputs can drift via shared hidden features. One-line variant of 3.1 (drop the hidden-layer freeze).

3.3 DontCareExample (selectable) — most faithful to "store with a mask"

Each ReplayStore item carries uint8 mask[NOut]. An example placed while output k is soloed stores mask = {0…1@k…0}; train_masked consumes the per-example mask so other outputs at that input are never pulled toward a stale label. Survives later training. Costs Cap*NOut bytes.

3.4 Honest limit (Sift's correctness graft — into product copy + ALIGNMENT.md)

On a shared-trunk MLP, NO realisation both fully isolates AND stays expressive. MaskGradients is predictable but weak (only the final linear column moves; hidden capacity frozen). ZeroLoss is expressive but bleeds through shared hidden weights. State this to the operator in the dock panel ("solo freezes the rest as far as a shared network allows") rather than overselling "leaves others unchanged". MaskGradients is the safe default because its guarantee is provable (§6.3 bit-identical ctest).

Solo is honoured in both modes: Mode-1 geometric target only pushes soloed dims (unify solo_mask_ with the activeDims_ gate at InterfaceRL.cpp:730 — Anchor-First's graft); Mode-2 warm-start trains anchors only on soloed dims when solo is active.

---

4. Core component changes (keep replay OUT of the MLP kernel — Anchor-First)

New nisps/ml/replay.hpp

ReplayStore<NIn, NOut, Cap> — fixed std::array ring (no heap; WASM Cap=64, firmware Cap=16-32 via template param — Anchored-Manifold's free SRAM/centroid tradeoff). Item {std::array<float,NIn> input; std::array<float,NOut> action; float reward; std::uint8_t mask[NOut];}. Methods (all deterministic, per-instance nisps::Rng, no libc rand):

• deepen_or_store_negative(x, a, radius=0.05f) (InterfaceRL.cpp:42-66)
• store(reward, x, a, mask) / positive_count()
• knn_positive_centroid(x, k=4, &mean, &count) — linear scan, fixed top-k insertion (no std::sort, no heap). Deterministic tie-break by index and fixed accumulation order so native==WASM (the classic float-sum parity trap).
• decay_negatives() (reward += 0.0025f*max(|reward|,1), evict > -0.01f)
• anchors() / anchor_count() accessors for Mode-2 warm-start replay.

New nisps/ml/geo_push.hpp

Pure free function compute_push_targets(replay, control_input, solo_mask, rng, &target_buf) implementing InterfaceRL.cpp:602-738 exactly. Writes into a caller-supplied scratch buffer (no heap). Keeps the geometric math out of both MLP and controller.

nisps/ml/mlp.hpp — minimal backprop hooks only

• train_targets(span inputs, span targets, span sample_weights, span out_mask, lr) — trains toward computed target vectors (Mode-1 geometric batch; targets are computed, not stored labels). Reuses forward_/backprop_/apply_grad.
• train_masked(lr, max_iter, min_err, span out_mask) — the solo path: zeroes deriv[j] at :281 for masked j; in MaskGradients freezes hidden apply_grad.
• Factor the inner forward→loss→backprop of train() into a train_pair_ helper so both reuse the proven backprop (Minimal-Divergence graft). No replay/centroid/anchor logic in MLP.

nisps/ml/feedback.hpp — edits (append-only enum)

• Add ReplayStore<NIn,NOut> replay_, solo_mask_, scratch_undo_ ring (templated depth), placed_out_, SoloMode field.
• Edit on_down Avoid case → geometric push via replay_ + geo_push + train_targets; keep a Diffuse legacy sub-mode.
• Edit RandomiseMlp/ExploreAndPlace on_down so re-press = Reroll.
• Add nudge/undo_nudge, place_begin/place_commit, finalise/abort, set_solo_mask/clear_solo_mask, set_solo_mode.
• Append new FeedbackAction values keeping existing numeric values stable: GeometricPush=8, ScratchNudge=9, ScratchUndo=10, AnchorPlaced=11, WarmStarted=12 (Minimal-Divergence's single most parity-safe decision — never renumber the TS↔C++ contract).

nisps/wasm/bindings.cpp — extend the existing block (:439-535)

Add: nisps_ml_feedback_set_solo(ml,mask,n), _set_solo_mode(ml,mode), _nudge(ml,speed,spread), _undo_nudge(ml), _place_begin(ml,current_out), _place_commit(ml,chosen_input,scratch_out), _anchor_count(ml), _get_anchors(ml,buf), _finalise(ml), _abort(ml). Existing _down/_up/_drag/_static_output stay; _down now returns the new action ints. Reuse the existing scratch buffers + guard/cast style.

TS

• playground/src/ml/types.ts — add the new _nisps_ml_feedback_* decls; extend FeedbackMode/FeedbackAction/add SoloMode enums (numeric parity with C++).
• playground/src/ml/wasm-iml.ts — thin wrappers (feedbackNudge, feedbackPlaceCommit, feedbackFinalise, feedbackSetSolo, getAnchors); reuse pinMaskBuf (:225); add a small anchor heap buffer.
• playground/src/modes/mode-runtime.ts — route thumbsDown (:548) per active feedbackMode; Mode-2 wires press→aim→release to place_begin/place_commit; a "Done" button → finalise → loop getAnchors→addExample→train. Gate trainOnCurrent (:516) and auto-explore on learning_paused().
• playground/src/stores/exploration-store.ts — add feedbackMode, soloMode, soloMask, exploring, anchorCount, unifyMemories + selectors.

---

5. Phased integration plan (TS-prototype-first; each step gated on parity-check.sh)

Audible validation is the gate, not green tests. Prototype the arithmetic in pure TS — driven through the EXISTING WasmIML primitives (drawWeights, moveWeights, addExample, train, getWeights/setWeights, setInput/process/getOutputs, verified at wasm-iml.ts:293-476) — before any new C++.

Phase 0 — TS prototype (no new WASM)

A throwaway TS module mirrors ReplayStore + geo_push + train_masked, computed in TS and fed to the real net via existing calls. Oracles:

• Mode 1: like 2-3 sounds in distinct corners; dislike a sound near one like; confirm the heard 126-vector at that input moves audibly AWAY from the liked timbre (not random wobble). A/B against today's moveWeights thumbs-down using the existing A/B-compare feature — directionality must be perceptible (Sift's oracle).
• Mode 2: randomise → sweep-audition → "like that" → place in a corner → randomise → place opposite corner → Done → sweep the JoyMap and confirm a smooth morph between the two placed timbres, and that re-randomising no longer destroys them.
• Solo: solo output 0, train, confirm via the heatmap / weight-health views that only that param's mapping changed (MaskGradients: bit-stable elsewhere).
• Use window.__nisps (setInputs/getOutputs/train/thumbsDown/getLayerStats) for scripted Playwright checks.

Gate: felt loops audibly correct → freeze kCentroidK, pushStep, warm-start iteration count, undo depth. Only then crystallise.

Phase 1 — C++ core

Add replay.hpp, geo_push.hpp, the mlp.hpp hooks, the feedback.hpp edits. Build nisps_core_tests (-Wall -Wextra -Werror). Gate: new ctest cases green (§6.1); scripts/parity-check.sh still 1e-5 (existing Stage 5 unchanged at this point).

Phase 2 — C API

Add the new nisps_ml_feedback_* exports. Rebuild WASM (scripts/build-wasm.sh). Gate: parity-check.sh green after Stage-5 extension (§6.2); kVersion bump.

Phase 3 — TS FFI

types.ts decls + enums, wasm-iml.ts wrappers. Gate: bun run typecheck; a TS↔WASM smoke test reproducing a Phase-0 loop through the real FFI matches the TS prototype's outputs; parity-check.sh green.

Phase 4 — UI selector in the learning-behaviour dock panel

exploration-store state + the dock-panel FEEDBACK_MODE selector ("Explore and place" / "Geometric dislike"), SOLO_MODE selector, exploring/anchor indicators, the cold-start prompt, the honest solo-limit copy (British spelling). Gate: bunx playwright test (the e2e felt-loop specs); full scripts/run-all-tests.sh (chokepoint E) green.

Phase 5 — firmware readiness (gated, not on the critical path)

Confirm RP2350 SRAM budget with firmware Cap/undo-depth template params (ReplayStore<2,126,16> + anchors + scratch_undo_ depth 2). Verify chokepoint A (audio correct) / B (no perf regression) before flashing. Re-pin the orphaned memllib 4733ca0 to 0a541cc.

---

6. Parity + perf contract

Native==WASM within 1e-5 is preserved because every new operation is deterministic f32 arithmetic on the per-instance nisps::Rng (no libc rand() anywhere). Both sides run the same Rng from the same seed (kSeed ^ kFeedbackSalt, matching the MLHandle 0xFEEDBACC0DE salt).

6.1 Host ctest (tests/cpp/test_mlp_feedback.cpp, already registered)

Rewrite the cases that encode the old 2nd-press-cancels state machine (deliberate break). Add: replay dedup/deepen at 0.05; k-NN centroid selection + deterministic tie-break; geometric push direction sign (target moves away from centroid); taper; cold-start posMemCount==0 fallback; solo bit-identical assertion — after train_masked with MaskGradients, non-soloed final-layer columns are == (not 1e-5-near) to pre-train (Anchored-Manifold's stronger-than-parity oracle); scratchpad-never-trained invariant; place→warm-start reachability (both anchors reproduced at their corners); determinism under fixed seed.

6.2 Native↔WASM parity (parity_check.cpp + parity_wasm.mjs, bump kVersion)

Extend Stage 5: (S5a Mode 1) seed ParityMLP+ReplayStore, store 3 fixed positives + 1 negative, run geometric push (centroid→target→train_targets), push 126 post-train probe outputs both sides. (S5b Mode 2) enter scratchpad, reroll×2, nudge, undo, place 2 anchors at fixed inputs, finalise (warm-start at fixed lr/iters/seed), push probe outputs + 12 weight probes. (S5c Solo) set solo_mask={1,0,…}, train_masked, assert non-soloed columns byte-identical pre/post. scripts/parity-check.sh float32-diffs at 1e-5 — new floats covered automatically.

6.3 Perf contract

replay.hpp + anchors + solo_mask_ + scratch_undo_ are all fixed std::array (Cap/depth compile-time, zero heap). Hot loops O(Cap*NOut) over fixed arrays; roll/centroid/train_pair_ marked NISPS_FORCE_INLINE where hot; .f on every literal (0.0025f, 0.25f, 0.5f, 16.f, 0.05f). No virtual dispatch (plain template). Feedback runs on the control core, never the audio ISR (mirrors firmware loopCallback) → chokepoint B structurally satisfied. SRAM budget tuned per-target via Cap/depth template params.

---

7. UI — learning-behaviour dock panel

• FEEDBACK_MODE segmented control: Explore and place (default) / Geometric dislike. One-line description per mode in the operator's language.
• SOLO_MODE selector (visible when any output is armed): Mask gradients (default) / Zero loss / Don't-care example, with the honest "as far as a shared network allows" caveat.
• Exploring indicator + anchor count during Mode-2 sessions; a Done / finalise button and a cancel gesture; nudge + undo controls.
• Cold-start prompt (Mode 1, zero positives): "Like a few sounds first so the system knows what to move away from."
• unifyMemories toggle (advanced): share likes/dislikes across modes (default on).
• Copy uses British spelling throughout.

---

8. ALIGNMENT.md updates (same commit as the code)

1. RETRACT the feedback-modes-port-spec.md §2.5/§7 "geometric push out of scope / AVOID = move_weights" accepted-divergence note — Mode 1 now ports the latest upstream. Without this, a future session will "rediscover" the geometric push as scope creep.
2. Record the single deliberate firmware divergence: the useRandom degenerate branch uses nisps::Rng, not libc rand() (value generated, never compared; native==WASM holds).
3. Record the TrainBatch-vs-unshuffled-SGD behavioural divergence: nisps trains per-sample SGD, not shuffled two-LR batches — browser != firmware behaviourally, by design (separate verification targets).
4. State the shared-trunk solo limit as accepted: no realisation both perfectly isolates and stays expressive.
5. Re-pin orphaned memllib 4733ca0 → 0a541cc.