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# TriDrive — benchmark: labels, human evaluation, AIDE protocol and every result table
Anonymous submission under double-blind review. Every file under `tables/` and `aide_protocol/` is text (Markdown, JSON, CSV,
LaTeX, Python) and was passed through the release pseudonymisation described at the end; **no number was changed** — only
identifiers, paths, hosts and annotator names were rewritten. `labels/` and `human_eval_299/` have their own READMEs.
## Sub-folders
| Path | Contents |
|---|---|
| `labels/` | Warning-probe label tables: the 979 consensus-labelled training anchors (562 manual / 417 engaged) and the gold discrimination set (512-row anchored layout; 509-row headline set) — see its own README |
| `human_eval_299/` | The pre-registered 299-clip human evaluation: clip manifest, three-stage labels, frozen thresholds, the pre-registration document (byte-identical, hash included) and the result tables — see its own README |
| `aide_protocol/` | AIDE evaluation protocol: official split lists, protocol constants and metric code, cache builder, Gate-0 verification script and its record (details below) |
| `tables/release_tables/` | Every result table behind the paper, as frozen for the paper's artefact release: `aide/` (143 files: `*.md` + `*.json` reports, `results.csv`, `kv2/`, `mirror_completion/`, `pretrain_variants/`), `baton/` (225 files: BATON programme tables, alert timing, DMS coverage / fairness, hold-out stage, and the release-time copies of the two ledgers) and `baselines_dms/` (100 files: phase-1 extractor measurements, read-out selections, dry runs, repro report) |
| `tables/paper_tables_iclr/` | The 53 LaTeX tables of the manuscript and its appendices, generated by `code/paper/tables/gen_tables.py` from the markdown tables above (every number copied verbatim; the source table is named in a comment at the top of each file) |
| `tables/ledgers/` | The current versions of the two append-only run ledgers of the BATON programme: `paired_compare.csv` (56,990 rows — every paired device-cluster bootstrap comparison, written by `code/eval/paired_compare.py`) and `cwm3_quick_metrics.csv` (158,967 rows — quick metrics of every training run); `release_tables/baton/` holds the copies frozen for the artefact release |
| `tables/aide_extra/` | AIDE material not in the release tables: MotionBERT official-resolution quantiles, the EMA confound check, the Driver-WM quantile sweep, head-to-head tables, the Driver-WM v2 audit and the Fig. 3b readings |
Sub-folder file counts: `release_tables` 464, `paper_tables_iclr` 53, `ledgers` 2, `aide_extra` 9, `aide_protocol` 12.
## `aide_protocol/`
| File | Origin in `code/aide/` | What it is |
|---|---|---|
| `splits_training.csv`, `splits_validation.csv`, `splits_testing.csv` | `data/` | The official AIDE split lists (1884 / 405 / 609 clip rows, header excluded) |
| `protocol.py` | `data/` | Protocol constants (driver selection rule, sample grid, horizons, PCK threshold); `AIDE_ROOT` is read from the environment |
| `build_cache.py` | `data/` | Builds the AIDE keypoint cache from the official annotations (`python aide/data/build_cache.py [--workers 8]`) |
| `verify_protocol.py`, `verify_protocol.json` | `data/`, `results/cache/` | Gate 0: reproduces the published zero-velocity numbers on the official test split; the JSON is the recorded verification |
| `cache_summary.json` | `results/cache/` | Summary of the built cache: clips per split, detection statistics, label histogram, the locked driver rule and sample grid |
| `metrics.py`, `harness.py` | `eval/` | Protocol metrics (All-MPJPE, PCK, high-motion subsets) and the evaluation harness (pred npz → metrics.json + rows of `results.csv`) |
| `protocol.md`, `driverwm_kp_port.md` | `docs/` | Protocol documentation, and the notes of the port of the Driver-WM reproduction to keypoint part tokens (architecture-attribution experiment) |
The files are copied flat here for reading; the importable versions live at the paths given in the second column.
## `tables/ledgers/` columns
* `paired_compare.csv`: `run_stamp,tag,regime,split,cell,metric,a,b,value_a,ci_a_lo,ci_a_hi,value_b,ci_b_lo,ci_b_hi,delta,delta_ci_lo,delta_ci_hi,frac_boot_d_gt0,n,n_pos,n_dev,n_boot`
* `cwm3_quick_metrics.csv`: `run_stamp,stage,variant,seed,regime,split,branch,chain,metric,value,base_rate,n,n_rows_predicted,best_epoch,val_objective,run_dir` (`run_dir` is the pseudonymised run directory)
## `tables/paper_tables_iclr/`
`tab_aide_5seed.tex`, `tab_aide_analytic.tex`, `tab_aide_geometry.tex`, `tab_aide_interventions.tex`, `tab_aide_semantic.tex`, `tab_app_identity.tex`, `tab_app_power.tex`, `tab_app_transfer.tex`, `tab_baton_exp3P.tex`, `tab_baton_k4.tex`, `tab_baton_main.tex`, `tab_branch_arch.tex`, `tab_driverwm_quantile.tex`, `tab_e4.tex`, `tab_ecan.tex`, `tab_face.tex`, `tab_future_state.tex`, `tab_gzfc.tex`, `tab_id_map.tex`, `tab_input_ablation.tex`, `tab_label_check.tex`, `tab_main_aide.tex`, `tab_main_baton_interaction.tex`, `tab_main_baton_interaction_ecan.tex`, `tab_main_kpf_gain.tex`, `tab_main_transfer.tex`, `tab_main_warning.tex`, `tab_modality_ablation.tex`, `tab_negative_summary.tex`, `tab_operating_points.tex`, `tab_road_dynamics.tex`, `tab_robustness.tex`, `tab_rssm.tex`, `tab_supp_arch_attribution.tex`, `tab_supp_baton_interaction_visual.tex`, `tab_supp_calibration.tex`, `tab_supp_driverwm_mirror.tex`, `tab_supp_driverwm_quantile.tex`, `tab_supp_formulation_abs.tex`, `tab_supp_hm_h5_arms.tex`, `tab_supp_hm_subsets.tex`, `tab_supp_holdout.tex`, `tab_supp_kpf_forecaster.tex`, `tab_supp_kv2.tex`, `tab_supp_master_aide.tex`, `tab_supp_motionbert_faithful.tex`, `tab_supp_oracle_diag.tex`, `tab_supp_pretrain_variants.tex`, `tab_supp_risk_ranking.tex`, `tab_supp_transfer.tex`, `tab_verified_mask.tex`, `tab_warning_head.tex`, `tab_warning_rmv.tex`
## Pseudonym scheme
BATON devices and routes appear only as pseudonyms, consistent across this bucket and with the public BATON release:
`driver_N` for a device (the four real-name device folders of the corpus are mapped the same way), and
`driver_N__route_M__route_a-b` for a route segment group (`route_a-b` = the segment range of the original recording). A recording id that occurs outside a complete device/route/segment token is replaced by `route_M` when the private map resolves it and otherwise by a stable date-free pseudonym `rid_<10 hex>`; no raw recording id or recording timestamp remains. File-system paths
were rewritten to `/home/USER/...`, hosts and IP addresses to `REDACTED` / `REDACTED_IP`, annotator names to `[annotator]`, e-mail
addresses to `[email]`.
## Not included
* **Per-sample prediction dumps** of the joint model and of the baselines (`pred_layout_*.npz`, ≈ 21 GB): the metrics computed
from them are in `tables/`; available on request after the review period.
* **AIDE-derived caches and per-clip predictions** (`aide_kp.npz`, `aide_kin11.npz`, `baton_pose_corpus.npz`, `pred_*.npz`): AIDE
is obtained from its authors; `aide_protocol/build_cache.py` rebuilds the cache and `code/aide/` the predictions.
* The one figure of the release tables (`aide/pareto_all_vs_hm.png`, drawn by `code/aide/eval/baselines_analytic.py`; its table is
`aide/tradeoff_all_vs_hm.md`) and the crop of Fig. 3b of the Driver-WM paper (the readings taken from it are
`aide_extra/driverwm_v2_fig3b_readings.json`).

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