# Experiment registry

This is the source of truth for what has and has not been measured. A run is
`QUALITY EVIDENCE` only when its data, split, checkpoint, threshold, and metric
scope were registered before evaluation. Smoke tests remain smoke tests even if
their numbers look good.

Status legend:

- `COMPLETE — DATA`: measured data/split evidence, no model-quality claim;
- `COMPLETE — INTEGRATION`: verifies code paths only;
- `COMPLETE — DEVELOPMENT`: bounded real-data development evidence, not test evidence;
- `READY`: implementation/config exists, experiment has not completed;
- `EXTERNAL`: requires data collection, compute, network, or governance work;
- `SEALED`: deliberately inaccessible until a freeze gate;
- `RELEASE GATE`: must pass before public/production claims.

## Registry

| ID | Status | Question | Data and split | Configuration/artifact | Readout and decision |
|---|---|---|---|---|---|
| D-001 | COMPLETE — DATA | Can the local upstream shard be decoded, normalized, hashed, and validated? | 1/83 train shards; 3,265 rows | `reports/partial_shard_audit.json` | 3,265 valid, 0 invalid, 6.857 h; no exact-audio/ID/conflicting-label duplicates. Scope is one shard only. |
| D-002 | COMPLETE — DATA | Can a balanced group-safe development split be produced? | Same shard, 90/10 | `reports/partial_iid_split.json`; seed 42 | 2,939/326 rows; no group/audio/key crossings. Suitable for development integration, not a final corpus claim. |
| D-003 | COMPLETE — DATA | What does full source holdout expose? | Same shard, source-grouped 80/20 | `reports/partial_source_holdout_split.json`; seed 42 | 2,617/648, no crossings. Severe language and human/synthetic confounding; retain only as joint stress test. |
| E-000 | COMPLETE — INTEGRATION | Is training deterministic and end to end without corpus access? | Generated separable features; 32 validation examples | `configs/smoke.json`; 6,660 parameters; 2 epochs | Checkpoint SHA-256 was identical across two runs. Do not use its perfect ranking as quality evidence. |
| E-001 | COMPLETE — INTEGRATION | Can an audited manifest lazily resolve its exact Parquet rows? | 8 train / 8 validation rows | `artifacts/resolver-smoke/` | Resolver, decode, frontend, collator, forward/backward, and checkpoint path completed. No statistical interpretation. |
| E-002 | COMPLETE — INTEGRATION | Does real audio train/evaluate through the debug path? | First capped 32 train / 32 validation records from partial IID manifest | 6,660 parameters; 2 s suffix; 1 epoch; `artifacts/real-shard-smoke/` | AUROC 0.246, FPR 0.111 and recall 0.043 at the selected loose 20% FPR-budget threshold. Expected poor debug result; reject as model-quality evidence. |
| E-003 | COMPLETE — DEVELOPMENT | How much endpoint signal exists in simple inspectable acoustics? | All 2,939 train / 326 validation rows in partial IID split; observed-key overlap zero, but all groups are singleton rows | Ten 4 s suffix waveform statistics + class-balanced logistic regression; `artifacts/partial-baseline/metrics.json` | AUROC 0.7522, AP 0.7310; at validation-selected FPR ≤ 0.02, threshold 0.7639 gives FPR 0.0181 and END recall 0.1313. Best-effort row-disjoint development floor only. |
| E-004 | COMPLETE — DEVELOPMENT | Can a 151k-parameter four-second TinyTCN learn the one-shard task? | Same 2,939/326 partial IID split | `configs/partial_shard_preview.json`; 3 epochs, LR `3e-4`; `artifacts/partial-shard-preview-tinytcn-4s/` | Best AP 0.6874, AUROC 0.7291; recall 0.0938 at FPR 0.0181. Continue only as bounded development. |
| E-005 | COMPLETE — DEVELOPMENT | Does a higher learning rate improve the same TinyTCN? | Same partial IID split | `configs/partial_shard_preview_lr1e3_8ep.json`; LR `1e-3` | Deliberately stopped after epoch 4 for poor trajectory; best AP 0.6190, AUROC 0.6829, recall 0.0313 at FPR 0.0181. Reject. |
| E-006 | COMPLETE — DEVELOPMENT | Does a fresh-optimizer continuation improve the selected TinyTCN? | Same partial IID split, already used adaptively | `configs/partial_shard_warmstart_lr3e4_5ep.json`; 5 continuation epochs; `artifacts/partial-shard-warmstart-lr3e4-5ep/` | Selected continuation epoch 1 by AP: AP 0.6881, AUROC 0.7390, Brier 0.2133; 10 TP/3 FP/163 TN/150 FN at threshold 0.7410. Baseline remains stronger. Packaged preview only. |
| E-007 | COMPLETE — DEVELOPMENT | Do baseline and TinyTCN survive held-out source groups? | 2,617/648 source-held-out split; 9 validation source groups | `configs/partial_source_holdout_preview.json`; source baseline and TinyTCN reports | AP 0.5368 baseline vs 0.5500 TinyTCN; both AUROC about 0.55. Wide paired intervals and severe confounding establish no winner. |
| E-008 | COMPLETE — DEVELOPMENT | Does audio framing double-count trailing silence? | Same 326 IID validation rows; paired 0/200/400/800 ms valid-zero perturbations | `reports/partial_tinytcn_silence_sensitivity.json`; fixed E-006 threshold | At 800 ms, 26/326 decisions flip and FPR rises 0.0181→0.0361; mean absolute score shift 0.1058. Add silence robustness and recalibrate controller. |
| E-009 | COMPLETE — INTEGRATION | Does the serialized controller latch END and emit only one response edge? | 8 hand-authored checkpoints across 3 synthetic turns; no ONNX scores or human labels | `data/collection/controller_replay_fixture.jsonl`; `reports/controller_replay_integration.summary.json`; E-006 metadata policy | Three response edges, zero duplicates; model endpoint, relaxation, timeout, early interruption, and post-END callbacks exercised. Plumbing evidence only. |
| E-010 | READY | What is the TinyTCN full-data baseline? | All audited train shards; group-safe train/validation | `configs/tiny_tcn.yaml`; seed 17; 396,164 parameters | Run after D-010. Primary selection: END recall under FPR ≤ 0.02. |
| E-011 | READY | How much does source shift hurt? | Leave-one-source-out folds plus human-only slices | Same checkpoint family and frozen preprocessing | Report every viable source fold, macro distribution, raw counts, and confounding; do not select on one favorable fold. |
| E-012 | READY | Does a Whisper encoder improve constrained recall enough to justify cost? | Same rows and folds as E-010/E-011 | `configs/whisper_teacher.yaml`; frozen encoder first | Compare paired predictions at identical FPR budgets plus size/latency/RSS. |
| E-013 | READY | Can teacher supervision improve the small student? | Same train/dev policy; cached teacher predictions | Distillation objective in `src/turn_detection/training/losses.py` | Run only after E-012 validates the teacher. Compare hard-label student vs distilled student, same seed grid. |
| E-014 | READY | Which audio suffix is Pareto-optimal? | Same split | 2 s vs 4 s vs 8 s; student sizes 96×6, 128×8, 192×8 | Select on false interruptions, endpoint delay, CPU p95, bytes—not accuracy alone. |
| E-015 | READY | Do filler auxiliaries help the intended failure cases? | Same split plus midfiller/endfiller slices | Endpoint-only vs masked multitask; all else fixed | Require paired grouped intervals, not aggregate delta only. |
| E-016 | READY | Does INT8 preserve decisions and latency? | Representative train-only calibration; frozen dev | FP32 ONNX vs static INT8 | Check numerical delta, recalibrate INT8 threshold, then compare constrained metrics and end-to-end latency. |
| H-001 | EXTERNAL | Does the model work on natural Shiprocket Hinglish holds? | 900 planned recordings, 30 speakers, 21/4/5 speaker split | `data/collection/assignments.jsonl`; `docs/data_collection_protocol.md` | No recordings exist. Collect with consent; lock speaker-disjoint test; report agreement and all exclusions. |
| T-001 | SEALED | What is final generalization on the official test? | Pinned 10-shard Smart Turn test snapshot | `scripts/download_test_dataset.sh` | Open once only after model/frontend/controller/threshold hashes are frozen. No post-test tuning. |
| R-001 | RELEASE GATE | Is the artifact publishable? | Final checkpoint, official-test metrics, Hinglish evidence | ONNX metadata/parity, benchmark, model/data cards, `build_release.py` | FP32 development preview export/benchmark is complete; final empirical and rights gates fail. A preview may be packaged only with conspicuous development overrides. |

## Completed-run details

### D-001 — partial shard audit

- Generated: 2026-08-22T19:58:38Z.
- File: `train-00010-of-00083.parquet`.
- Duration range: 0.36–29.56 seconds.
- Endpoint balance: 1,667 HOLD / 1,598 END.
- Synthetic balance: 2,712 synthetic / 553 human-tagged.
- Filler labels: `midfiller` 1,339 true / 1,284 false / 642 null;
  `endfiller` 905 true / 1,718 false / 642 null.
- Known interpretation risk: this shard can be non-random and source-clustered.

Decision: the parser/schema are compatible with this shard. Audit the other 82
before training or making corpus-level distribution claims.

### E-000 — synthetic deterministic smoke

- Model: 32 channels, 2 blocks, 6,660 parameters.
- Data: generated feature patterns, not audio.
- Training: 2 epochs on CPU, seed 17.
- Best validation loss: 0.6720276.
- Stored threshold: 0.4810289 under a deliberately loose smoke FPR budget 0.2.
- Checkpoint bytes: 111,159.
- Repeated-run checkpoint SHA-256:
  `fc519b0b868c31d3429316babe6a844086c58516381e04ac29c550d86df7a644`.
- Neural-only microbenchmark: PyTorch 2.11, Apple arm, one thread, batch 1,
  100 frames, 3 warmups + 10 measurements; warm mean 0.247 ms.

Decision: deterministic training/checkpointing and benchmark instrumentation
work. All classification numbers are invalid as real-audio evidence because the
features were constructed to be separable.

### E-002 — 32-example-per-split real-audio smoke

- Source: partial IID manifest resolved back to the local Parquet shard.
- Cap: 32 examples per split; validation contains 23 END / 9 HOLD.
- Model: 32 channels, 2 blocks, 6,660 parameters.
- Audio suffix: 2 seconds.
- Training: 1 epoch, batch size 4, CPU path.
- Best validation loss: 1.1148809.
- At threshold 0.5: recall 0, FPR 0, balanced accuracy 0.5.
- Stored operating threshold: 0.3017663 under the debug FPR budget 0.2.
- At stored threshold: 1 TP, 1 FP, 8 TN, 22 FN; recall 0.0435, FPR
  0.1111, AUROC 0.2464.
- Checkpoint bytes: 111,159.

Decision: the data-addressing fix is validated. The run is intentionally too
small and short to learn; do not compare it with any baseline.

### E-003 — full partial-shard acoustic baseline

- Source: all rows in the partial IID split, with zero observed-key crossing.
  All 3,265 base IID linkage groups are singletons because
  speaker/conversation/voice IDs are absent, so this is not speaker-disjoint
  evidence. The source stress split separately groups by dataset source.
- Model: class-balanced full-batch logistic regression with ten interpretable
  waveform features; 1,000 iterations, learning rate 0.05, L2 0.001.
- Context: most recent four seconds.
- Train / validation: 2,939 / 326.
- Validation discrimination: AUROC 0.7521837, AP 0.7310146.
- Calibration: Brier 0.2038291, ECE 0.0867866.
- Threshold selected on validation under FPR ≤ 0.02: 0.7638752.
- At that threshold: 21 TP, 3 FP, 163 TN, 139 FN; END recall 0.13125,
  FPR 0.0180723, precision 0.875, macro-F1 0.4624210.

Decision: simple acoustics contain useful endpoint signal. Treat this as a
development floor for neural experiments, not an unbiased generalization
estimate: the threshold and metrics use the same 326-item validation set, the
source is one of 83 shards, and there is no Hinglish or official-test evidence.

### E-004 to E-006 — partial-shard TinyTCN selection

- Architecture: 151,812 parameters, six 128-channel blocks, four-second suffix.
- E-004 best: AP 0.6873543, AUROC 0.7290663, recall 0.09375 at FPR 0.0180723.
- E-005: LR `1e-3`; stopped after epoch 4; best AP 0.6189746 and AUROC
  0.6828690. Rejected.
- E-006: weights-only continuation of E-004 with fresh optimizer, LR `3e-4`.
  Five epochs completed; continuation epoch 1 selected by AP. It has AP
  0.6880690, AUROC 0.7389684, Brier 0.2133037, and threshold 0.7410008.
  Confusion is 10 TP / 3 FP / 163 TN / 150 FN.
- Against E-003, E-006 minus baseline AP is −0.04295 (95% paired bootstrap CI
  [−0.11945, 0.03524]); AUROC −0.01322 [−0.07823, 0.05121]; constrained
  recall −0.06875 [−0.13298, −0.01220].

Decision: package E-006 as a fast development artifact, while retaining E-003
as the evidence winner. The same development split informed prior runs,
checkpoint selection, and thresholds; intervals are conditional and optimistic.

### E-007 — source-held-out stress

The baseline/TinyTCN respectively reach AP 0.53684/0.54996, AUROC
0.55387/0.55227, and recall 0.02786/0.04025 at FPR 0.01846. The paired AP delta
is +0.01312 with nine-source 95% CI [−0.21492, 0.12613]. Source, language, and
human/synthetic status shift together; no causal source claim or winner is
supported.

### E-008 — silence sensitivity

At the frozen E-006 threshold, appending 200/400/800 ms valid zero-valued
silence changes FPR from 0.0181 to 0.0301/0.0301/0.0361 and flips 5/13/26 of
326 decisions. At 800 ms mean absolute probability shift is 0.1058. Because the
controller also consumes silence duration, add trailing-silence jitter or
speech-end normalization before controller recalibration.

### E-009 — synthetic controller integration

The metadata-bound replay covers eight checkpoints across three fabricated
turns. It produces three response edges and zero duplicate emissions while
exercising a model endpoint, threshold relaxation, maximum timeout, premature
endpoint, and callbacks after a latched END. The input fixture SHA-256 is
`a96626ce8e93bff0a88f00f652932c5ac806377e67fc4fec3b03ae34658f9a04`;
the decision JSONL SHA-256 is
`171ecc5129292f0b6fabdad4d3f0e6ed013817755183f648e7b8f6dc7788542c`.

Decision: this proves controller plumbing and artifact-policy binding only.
Probabilities and targets are hand-authored, latency values are synthetic, and
none of its rates or delays are model, conversation, latency, or product-quality
evidence. Real annotated multi-checkpoint replay remains external work.

## Pre-registration template

Copy this block into the registry before starting any quality experiment:

```text
Experiment ID:
Question/hypothesis:
Code revision:
Dataset IDs and immutable revisions:
Manifest SHA-256:
Grouping policy version:
Train/development/test split hashes:
Model config and parameter count:
Frontend and suffix duration:
Random seeds and number of repeats:
Primary metric and FPR budget:
Secondary metrics/slices:
Threshold-selection split and rule:
Early-stopping rule:
Maximum examples/epochs/compute:
Promotion criterion:
Known confounders:
```

After the run, append checkpoint/ONNX hashes, environment versions, training
history path, prediction-file path, threshold, metric JSON path, latency JSON
path, failures reviewed, and the decision. Never overwrite a completed run to
make a rerun look like the same evidence.

## Full-corpus experiment sequence

1. `D-010`: download and audit all 83 train shards; retain per-shard reports.
2. `D-011`: freeze manifest, grouping policy, and IID/source-LOO split hashes.
3. `E-010`: run the default student on three seeds.
4. `E-014`: test duration/model-size ablations; identify Pareto candidates.
5. `E-015`: isolate the filler-auxiliary contribution.
6. `E-012`: run Whisper frozen-head; selectively unfreeze only if warranted.
7. `E-013`: distill only if the teacher has a meaningful paired advantage.
8. `H-001`: collect/annotate/lock the Hinglish benchmark independently.
9. `E-016`: export and quantize the selected student; recalibrate threshold.
10. Freeze hashes and controller policy; execute `T-001` once.
11. Run `R-001`, update model card with measured evidence, then publish.

## Metric reporting contract

Every quality table must state:

- whether `1` means END (it does in this repository);
- exact split and unique speakers/conversations/groups;
- confusion counts before rates;
- threshold and where/how it was selected;
- FPR denominator `FP + TN` and FNR denominator `FN + TP`;
- slice numerator, denominator, and suppression threshold;
- whether latency is neural-only or end to end;
- hardware, threads, batch, warmup, repetitions, runtime, and quantization;
- grouped uncertainty interval and grouping unit;
- whether the official test was used at any point.

This prevents a debug metric, favorable slice, or neural-only microbenchmark
from silently becoming a product claim.