title: AURORA-Workflow-1 — Dataset specification
slug: aurora-workflow-1-spec
type: spec
status: active
dataset_version: v1.0.0
owner: unassigned
created: 2026-04-26T00:00:00.000Z
updated: 2026-04-26T00:00:00.000Z
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 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 | LDA, EUT, HUB, inventory-business gym |
| appointment-scheduling | domains/appointment-scheduling/grammar.md | LDA, HUB, household-scheduling gym |
| inventory-reorder | domains/inventory-reorder/grammar.md | LDA, EUT, HUB, inventory-business gym |
| lab-sample-routing | domains/lab-sample-routing/grammar.md | LDA, ESC |
| issue-ticket-escalation | domains/issue-ticket-escalation/grammar.md | LDA, code-maintenance gym |
| household-maintenance-planning | 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. 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, ornullwhen 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@v1.0orinvoice-triage@v2.0for replaced episodes).exception_flag—truefor 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) 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 §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:
- Sample 10 % of episodes uniformly across the train + OOD splits.
- For each sampled episode, choose one exception class from the domain's grammar.
- Generate the exception-modified event stream while leaving
correct_actionconsistent with the exception (i.e. the correct action under the exception, not the rule's default). - 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 therule_versionfield).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).
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 rules.
Cross-references
- research/methodology/methods-and-controls.md — methodology source for the canonical numbers.
- research/methodology/data-collection.md — collection protocol.
- research/methodology/data-management.md — storage layout, metadata catalog.
- data/aurora-federated-1/spec.md — companion federated dataset.
- 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=truerate 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.