---
title: AURORA-Workflow-1 — Dataset specification
slug: aurora-workflow-1-spec
type: spec
status: active
dataset_version: v1.0.0
owner: unassigned
created: 2026-04-26
updated: 2026-04-26
sources:
  - ../../research/methodology/methods-and-controls.md
  - ../../research/methodology/data-collection.md
  - ../../research/methodology/data-management.md
  - ../../architecture/engineering-spec/schemas/event.schema.json
  - ../../architecture/engineering-spec/schemas/episode.schema.json
---

# AURORA-Workflow-1 — Dataset specification

The primary structured-workflow dataset for Stage 1. Used by H1 (event-vs-token), H2 (sparse routing), H3 (continual learning), H4 (episodic-semantic memory), and several benchmark families (LDA, EUT, ESC, HUB).

## Headline numbers

| Property | Value |
|---|---|
| Domains | 6 |
| Workflows per domain | 50 |
| Episodes per workflow | 200 |
| **Total episodes** | **60 000** (6 × 50 × 200) |
| Events per episode (per-domain mean) | 80–200 (cross-domain weighted mean ≈ 133; per-domain values are locked in each `domains/<slug>/grammar.md`) |
| Adversarial-exception rate | 10 % |
| Temporal rule-replacement rate | 20 % |
| Manual audit sample | 5 % per domain |
| Splits | train / validation / **calibration** / test / OOD / compositional-holdout |
| Compositional-holdout fraction | 20 % |
| Calibration-split fraction | 5 % (added per [ADR-0006](../../architecture/engineering-spec/decisions/0006-planner-objective-weights.md) for planner-objective-weight calibration) |
| Seeds | 100 (master); per-experiment subset of 20 |
| Estimated raw size | ~80 GB |

> *Note on totals.* The 60 000-episode total is `domains × workflows × episodes-per-workflow`. The events-per-episode figure varies by domain (e.g. invoice-triage ≈ 120, lab-sample-routing ≈ 200, household-maintenance ≈ 80) — see each grammar's "Episode generation parameters" section for its exact value.

## The six domains

Each domain has a standalone rule grammar at `domains/<slug>/grammar.md`:

| Slug | Folder | Used by |
|---|---|---|
| invoice-triage | [domains/invoice-triage/grammar.md](domains/invoice-triage/grammar.md) | LDA, EUT, HUB, `inventory-business` gym |
| appointment-scheduling | [domains/appointment-scheduling/grammar.md](domains/appointment-scheduling/grammar.md) | LDA, HUB, `household-scheduling` gym |
| inventory-reorder | [domains/inventory-reorder/grammar.md](domains/inventory-reorder/grammar.md) | LDA, EUT, HUB, `inventory-business` gym |
| lab-sample-routing | [domains/lab-sample-routing/grammar.md](domains/lab-sample-routing/grammar.md) | LDA, ESC |
| issue-ticket-escalation | [domains/issue-ticket-escalation/grammar.md](domains/issue-ticket-escalation/grammar.md) | LDA, `code-maintenance` gym |
| household-maintenance-planning | [domains/household-maintenance-planning/grammar.md](domains/household-maintenance-planning/grammar.md) | LDA, LDB, HUB, `household-scheduling` gym |

## Generation pipeline

```
domain rule grammar (vN.0)
   ↓
seeded simulator (per-domain)
   ↓
typed event stream conformant to event.schema.json
   ↓
adversarial-exception injection (10% of episodes; per-domain exception classes)
   ↓
temporal rule replacement (20% of episodes; vN.0 → vN+1.0 over the timeline)
   ↓
manual audit (5% sample per domain; pass/fail recorded)
   ↓
split partitioning (leakage-prevention rules per domain)
   ↓
write to content-addressed object store (ADR-0001) + manifest entry
   ↓
DuckDB catalog row per episode (ADR-0002)
```

Each stage produces a Provenance Ledger entry; the dataset version is anchored at the final step.

## Episode structure

Every episode conforms to [episode.schema.json](../../architecture/engineering-spec/schemas/episode.schema.json). Required fields populated by the generator:

- `episode_id` — UUIDv7 derived from `(domain_seed, episode_index)`.
- `domain` — one of the six slugs.
- `actor_role` — randomly drawn from the domain's actor pool.
- `event_sequence` — list of typed events; mean length 200, distribution per-domain.
- `state_before` / `state_after` — opaque-object snapshots produced by the simulator.
- `available_actions` — populated from the domain's action vocabulary.
- `correct_action` — set by the simulator; may be a single string, a set of equivalent strings, or `null` when the only valid response is a clarification question.
- `valid_clarification_questions` — populated for episodes where the simulator's hidden state would justify a clarification.
- `risk_level` — highest-risk action available in the episode.
- `rule_version` — the active rule-grammar version (e.g. `invoice-triage@v1.0` or `invoice-triage@v2.0` for replaced episodes).
- `exception_flag` — `true` for the 10 % adversarial-exception episodes.
- `provenance_id` — the Provenance Ledger entry where this episode was first recorded.
- `split` — `train` / `validation` / `calibration` / `test` / `ood` / `compositional_holdout`.

## Splits

| Split | Fraction | Purpose | Leakage rules |
|---|---|---|---|
| train | 55 % | Hyperparameter search + apprenticeship | All rule combinations from the train-pool grammar; entity names from the train pool only. |
| validation | 9 % | HP-budget selection (50 random configs) | Rules and entity names disjoint from test/OOD/calibration. |
| **calibration** | **5 %** | Planner-objective-weight calibration ([ADR-0006](../../architecture/engineering-spec/decisions/0006-planner-objective-weights.md)) and per-term scale-factor estimation | Rules and entity names disjoint from test/OOD; never used for HP-budget selection. |
| OOD | 10 % | Distribution-shift evaluation | Reserves a per-domain OOD-grammar variant: unseen rule version, unseen entity-name pool, intensified exception rate. |
| test | 15 % | Final hypothesis evaluation | Held until pre-registration is filed; never seen during HP search or calibration. |
| compositional-holdout | 6 % | Compositional generalisation | Reserves rule-pair combinations not present in any other split (per [methods-and-controls.md](../../research/methodology/methods-and-controls.md) §4). |

Test, OOD, calibration, and compositional-holdout splits are SHA-256-checksummed and signed; the test/OOD/compositional-holdout content is not exposed to any system before pre-registration is filed. The calibration split is exposed to the planner-weight search after pre-registration of the search procedure but before pre-registration freeze.

> Sum: 55 + 9 + 5 + 10 + 15 + 6 = 100 %.

## Adversarial-exception injection (10 %)

The 10 % rate is fixed across domains; the *kinds* of exceptions are per-domain (each grammar lists 5–8 exception classes). Injection procedure:

1. Sample 10 % of episodes uniformly across the train + OOD splits.
2. For each sampled episode, choose one exception class from the domain's grammar.
3. Generate the exception-modified event stream while leaving `correct_action` consistent with the exception (i.e. the *correct* action under the exception, not the rule's default).
4. Set `exception_flag=true`.

Exception episodes are critical for the H4 exception-recall metric and the M3 exception-preservation gate.

## Temporal rule replacement (20 %)

For each domain, rule grammar `vN.0` is in effect for the first 60 % of the timeline; a transition window from 60 %–80 % phases in `vN+1.0` (probability of v1 vs v2 ramps linearly); the final 20 % uses `vN+1.0` exclusively.

Half of the OOD split episodes use yet a third grammar `vN+2.0` to test adaptation to genuinely unseen rule versions.

The grammar's `rule-versioning policy` section per-domain documents the v1 → v2 substantive changes.

## Reproducibility manifest

Every published dataset version ships:

- `seed_manifest.json` — master seed + per-domain seeds + per-episode seed-derivation rule.
- `grammar_versions.json` — grammar version per episode (matches the `rule_version` field).
- `commit_pin.json` — git commit SHA of the generator at dataset build time.
- `checksums.json` — SHA-256 of every artefact file.
- `audit_log.csv` — manual-audit pass/fail per audited episode.
- `provenance_anchor.json` — Merkle root from the Provenance Ledger at dataset build time.

These files live alongside the dataset under the content-addressed object store (per [ADR-0001](../../architecture/engineering-spec/decisions/0001-content-addressed-immutable-fs-storage.md)).

## Manual audit (5 %)

Per-domain auditors run through 5 % of the episodes against the audit checklist in each grammar file. Failures trigger:

- For systematic failures (>1 % pass rate fall): generator bug; halt dataset build, fix, re-run.
- For isolated failures: log to `audit_log.csv`; episode is excluded from the build.

The audit must complete and pass before any test-set evaluation is permitted.

## Versioning

`v1.0.0` is the first publishable version. Bump policy:

- Patch (`v1.0.1`): bug fix in the generator; no semantic change to episodes; existing dataset is repaired in place if possible, or republished with the same content hash if not.
- Minor (`v1.1.0`): new domain added or new exception class; existing episodes unchanged; new episodes appended.
- Major (`v2.0.0`): change to event schema, episode schema, or episode-distribution that invalidates pre-registration. Triggers re-pre-registration.

## Privacy

All synthetic. No PII. Vendor / customer / patient names drawn from a hardcoded synthetic-name pool. The dataset is `privacy_tier='public'` by default; downstream events derived from interaction with the dataset inherit higher tiers per [event.schema.json](../../architecture/engineering-spec/schemas/event.schema.json) rules.

## Cross-references

- [research/methodology/methods-and-controls.md](../../research/methodology/methods-and-controls.md) — methodology source for the canonical numbers.
- [research/methodology/data-collection.md](../../research/methodology/data-collection.md) — collection protocol.
- [research/methodology/data-management.md](../../research/methodology/data-management.md) — storage layout, metadata catalog.
- [data/aurora-federated-1/spec.md](../aurora-federated-1/spec.md) — companion federated dataset.
- [architecture/engineering-spec/pre-model-systems/02-training-gyms.md](../../architecture/engineering-spec/pre-model-systems/02-training-gyms.md) — the gyms that consume this dataset.

## Open questions

- Whether 5 % is enough for manual audit at 60 000 episodes (= 3 000 episodes auditable). Currently fixed; revisit after first build.
- Whether the OOD split should also include an "exception-rate spike" condition (e.g. 25 % exceptions) in addition to unseen rule versions. Currently no; `exception_flag=true` rate stays at 10 % across splits except where explicitly noted in a domain grammar.
- Whether the compositional holdout should be expanded for H1's compositional-generalisation claim. Currently 5 %; revisit if pre-registration negotiation increases sample-size demand.
- Whether dataset versions should be cross-referenced from the Memex wiki entity page for the project, or only from this spec. Currently only from this spec; the wiki cross-link is a one-line addition.