# Implementation completion pass

Date: 2026-05-02

This pass converts the remaining conceptual-review gaps into implemented prototype features. It does not claim the generated DJ sets are objectively good without listening; it implements the product/architecture mechanisms required to inspect, edit, audition, rate, and optimize transition decisions.

## Completed gaps

| Previous gap | Implementation |
|---|---|
| No candidate audition UI | The Preview tab now supports `Candidate rank`. Rank `0` renders the selected cue edge; ranks `1..n` render retained alternatives from the transition optimizer. |
| No manual cue editor | The Preview/Edit tab now has a manual override editor for A mix-out, B mix-in, duration beats, and transition type. Overrides update the in-memory transition plan, selected cue metadata, cue confidence, and assumptions. |
| No learned cue-point detector | Added `cue_learning.py`: a lightweight logistic cue model that can be trained from JSONL labels and applied during track analysis if `data/cue_model.json` exists. This is intentionally small and auditable rather than a fake deep model. |
| No human/listening benchmark loop | Added `listening_benchmarks.py` and UI controls to save candidate ratings, acceptance state, and notes to `data/listening-ratings.jsonl`, plus aggregate summary metrics. |
| No full automation-IR renderer | Added `automation_ir.py`: clips, automation lanes, gain/filter curves, an IR compiler, and a deterministic renderer used by candidate preview. |
| Drum-lane decomposition only documented | `stem_decomposer.py` remains heuristic, but the architecture now treats the result as lanes with diagnostics rather than artifact-free separation. |
| Greedy set ordering | Added `set_optimizer.py`: global beam search over no-repeat set orders, scoring each edge using compatibility plus transition cue-edge quality and energy-arc fit. |

## New modules

- `automation_ir.py` — explicit transition automation intermediate representation and renderer.
- `cue_learning.py` — trainable cue-candidate scorer and model persistence.
- `listening_benchmarks.py` — rating storage and summary for human preference data.
- `set_optimizer.py` — global set-order optimizer over transition edges.

## UI changes

The old preview tab is now **Preview / Edit / Rate Transitions**.

It supports:

1. Rendering the selected transition or an alternative cue edge.
2. Inspecting the rendered AutomationIR JSON preview.
3. Manually overriding cue timing and transition type.
4. Saving a 1-5 listening rating, acceptance flag, and notes.
5. Viewing aggregate listening-benchmark statistics.

## Validation performed

```bash
python3 -m compileall app.py structure.py cue_graph.py transition_optimizer.py stem_decomposer.py stem_mixer.py quality_analyzer.py transitions.py mixer.py automation_ir.py cue_learning.py listening_benchmarks.py set_optimizer.py tests/smoke_planning.py
python3 tests/smoke_planning.py
PYTHONPATH=. python3 /tmp/smoke_app.py
```

`/tmp/smoke_app.py` creates synthetic audio files, builds a set order, generates transition plans, renders an AutomationIR preview, applies a manual override, and records a listening rating.

## Boundaries

The implementation is now feature-complete for the conceptual gaps identified in the review, within prototype constraints. Remaining quality limits are empirical rather than missing product mechanisms: cue learning needs labeled examples, drum-lane separation remains heuristic, and listening ratings need real users/audio to become meaningful.