docs/interactive-ux/FEASIBILITY_MATRIX.md

Feature feasibility matrix

Scoring convention

All scores are between 0.0 and 1.0.

For positive dimensions, higher is better:

• Feasibility: likelihood this can be implemented robustly with the current project direction.
• Value UX: how much nicer/faster the application feels.
• Value quality: expected improvement in extraction correctness.
• Realtime fit: how well the interaction can update from cached features without full reruns.
• MVP fit: whether it belongs in the first serious interactive version.

For cost dimensions, higher is harder/more expensive:

• Complexity: algorithmic/product complexity.
• Effort: implementation effort.

Matrix

Interaction	Feasibility	Complexity	Effort	Value UX	Value quality	Realtime fit	MVP fit	Verdict
Move sample to cluster → auto-tune clustering	0.90	0.55	0.45	0.95	0.90	0.85	0.95	Build early
Pull sample out → protect distinction	0.90	0.55	0.45	0.90	0.90	0.85	0.95	Build early
Lock confirmed cluster identity	0.95	0.25	0.20	0.80	0.75	0.95	1.00	Build immediately
Outlier-first review queue	0.95	0.30	0.25	0.90	0.80	0.95	1.00	Build immediately
Low-confidence visual emphasis	0.95	0.25	0.20	0.85	0.65	0.95	0.95	Build immediately
Bleed brush/suppression	0.85	0.55	0.50	0.90	0.85	0.80	0.85	Build early
Click-to-add missed onset	0.90	0.45	0.40	0.90	0.80	0.90	0.90	Build early
Cluster naming as reusable semantic hint	0.75	0.65	0.55	0.75	0.65	0.70	0.40	Useful, not first
Star/favorite sample → optimize around it	0.95	0.35	0.30	0.75	0.70	0.90	0.75	Build early
Explain this cluster	0.85	0.50	0.45	0.80	0.65	0.85	0.80	Build early
Live counterfactual parameter previews	0.80	0.70	0.65	0.90	0.75	0.75	0.55	High value, later
Temporal pattern supervision	0.75	0.70	0.65	0.80	0.85	0.70	0.45	Later
Reconstruction-error-driven correction	0.70	0.80	0.75	0.85	0.90	0.55	0.35	Later, powerful
Multi-resolution semantic clustering	0.80	0.70	0.65	0.85	0.80	0.75	0.50	Later
Auto-clean this family	0.80	0.60	0.55	0.75	0.80	0.75	0.50	Later
Drag clusters in semantic space	0.70	0.75	0.75	0.90	0.65	0.65	0.30	Cool, not first
Cluster gravity / physics metaphor	0.60	0.80	0.80	0.80	0.50	0.60	0.20	Risky novelty
Context-aware classification	0.65	0.80	0.75	0.65	0.80	0.55	0.25	Researchy
Teach mode across songs	0.70	0.85	0.80	0.85	0.85	0.60	0.25	Later platform feature
Predictive batch questions	0.85	0.55	0.50	0.90	0.80	0.80	0.70	Build after uncertainty scoring

Highest ROI set

Rank	Feature	Why
1	Lock confirmed cluster identity	Easy and prevents frustrating reclustering drift
2	Outlier-first review queue	Huge UX gain from simple uncertainty ranking
3	Move sample to cluster as supervision	Core differentiator; directly improves results
4	Pull sample out / protect distinction	Required counterpart to positive supervision
5	Click-to-add missed onset	Direct correction beats indirect threshold tweaking
6	Bleed brush	Removes many false positives quickly
7	Explain cluster	Makes the system debuggable and trustworthy
8	Predictive batch questions	Multiplies the effect of each correction
9	Counterfactual previews	Makes advanced tuning understandable
10	Reconstruction-error correction	Very powerful, but architecturally heavier

Technical conclusion

The most feasible "magic" is not heavy ML. It is constraint-aware clustering, cached feature vectors, uncertainty scoring, and local recomputation.

That foundation should be implemented before adding higher-risk semantic-space or personalized-model features.

Implementation alignment as of 2026-05-12

Interaction	Current status	Notes
Move sample to cluster → auto-tune clustering	partial	Implemented as semantic state mutation with force-cluster/must-link constraints and heuristic move suggestions. True constrained local reclustering is still open.
Pull sample out → protect distinction	partial	Implemented as a new user cluster plus cannot-link and split suggestions. True constrained reclustering is still open.
Lock confirmed cluster identity	done	Lock/unlock persists in supervision_state.json and appears in the UI. Replay into future reruns is still open.
Outlier-first review queue	done	Implemented with heuristic confidence/priority. Feature-margin and reconstruction-impact ranking remain open.
Low-confidence visual emphasis	done	Low-confidence and suppressed hits are visually distinguished in the hit table and review queue.
Bleed brush/suppression	partial	Suppress selected hit and similar suppression suggestions are implemented. Region brush and restore are still open.
Click-to-add missed onset	todo	Waveform click currently auditions nearest existing hit only.
Cluster naming as reusable semantic hint	todo	User clusters receive generated labels; explicit rename/semantic hinting is not implemented.
Star/favorite sample → optimize around it	partial	Favorite pins representative in semantic state; artifact re-export does not yet honor it.
Explain this cluster	done	Explanation endpoint and UI drawer are implemented.
Predictive batch questions	partial	Suggestion inbox exists; exact diff previews and richer question phrasing are open.
Live counterfactual parameter previews	todo	Not implemented.
Reconstruction-error-driven correction	todo	Not implemented.
Multi-resolution semantic clustering	todo	Not implemented.
Auto-clean this family	todo	Not implemented.
Drag clusters in semantic space	backlog	Not implemented.
Cluster gravity / physics metaphor	backlog	Not implemented.
Context-aware classification	backlog	Not implemented.
Teach mode across songs	backlog	Not implemented.

Revised highest-ROI next set

Rank	Feature	Why now
1	Supervised re-export	Makes current semantic edits affect the downloadable sample pack.
2	Force-onset from waveform	Adds the missing direct correction primitive for missed hits.
3	Suppression restore	Required safety counterpart to suppression.
4	Cached feature refs	Unlocks real local reclustering and better confidence.
5	Diff preview for suggestions	Makes batch suggestions safer and more trustworthy.
6	Constraint violation detection	Prevents silent conflicts once constraints become richer.
7	Browser tests	Protects the increasingly stateful UI from regressions.

Pass 5 implementation status

Implemented after initial alignment:

• supervised edited-state export under supervised/,
• add-onset waveform mode backed by POST /api/jobs/{job_id}/hits/force-onset,
• suppressed-hit restore backed by POST /api/jobs/{job_id}/hits/{hit_id}/restore,
• exact suggestion diff previews in API state and UI,
• validation via scripts/test_supervised_export_and_force_onset.py.

Still open:

• cluster merge/relabel/split workflows,
• cached feature-vector local reclustering,
• edited-vs-original comparison,
• browser-level UI tests.