README.md

metadata

pretty_name: procedural-typed-decisions
language:
  - en
license: apache-2.0
task_categories:
  - text-classification
tags:
  - tasksource
  - jev
  - system-one
  - procedural
  - synthetic
  - multi-question
configs:
  - config_name: arithmetic
    data_files:
      - split: train
        path: arithmetic/train-*.parquet
      - split: validation
        path: arithmetic/validation-*.parquet
      - split: test
        path: arithmetic/test-*.parquet
  - config_name: entity_belief_tracking
    data_files:
      - split: train
        path: entity_belief_tracking/train-*.parquet
      - split: validation
        path: entity_belief_tracking/validation-*.parquet
      - split: test
        path: entity_belief_tracking/test-*.parquet
  - config_name: event_state_reconstruction
    data_files:
      - split: train
        path: event_state_reconstruction/train-*.parquet
      - split: validation
        path: event_state_reconstruction/validation-*.parquet
      - split: test
        path: event_state_reconstruction/test-*.parquet
  - config_name: evidence_sufficiency
    data_files:
      - split: train
        path: evidence_sufficiency/train-*.parquet
      - split: validation
        path: evidence_sufficiency/validation-*.parquet
      - split: test
        path: evidence_sufficiency/test-*.parquet
  - config_name: multi_view_adjudication
    data_files:
      - split: train
        path: multi_view_adjudication/train-*.parquet
      - split: validation
        path: multi_view_adjudication/validation-*.parquet
      - split: test
        path: multi_view_adjudication/test-*.parquet
  - config_name: needle_retrieval
    data_files:
      - split: train
        path: needle_retrieval/train-*.parquet
      - split: validation
        path: needle_retrieval/validation-*.parquet
      - split: test
        path: needle_retrieval/test-*.parquet
  - config_name: partial_observation_calibration
    data_files:
      - split: train
        path: partial_observation_calibration/train-*.parquet
      - split: validation
        path: partial_observation_calibration/validation-*.parquet
      - split: test
        path: partial_observation_calibration/test-*.parquet
  - config_name: policy_applicability
    data_files:
      - split: train
        path: policy_applicability/train-*.parquet
      - split: validation
        path: policy_applicability/validation-*.parquet
      - split: test
        path: policy_applicability/test-*.parquet
  - config_name: policy_under_uncertainty
    data_files:
      - split: train
        path: policy_under_uncertainty/train-*.parquet
      - split: validation
        path: policy_under_uncertainty/validation-*.parquet
      - split: test
        path: policy_under_uncertainty/test-*.parquet
  - config_name: record_aggregation
    data_files:
      - split: train
        path: record_aggregation/train-*.parquet
      - split: validation
        path: record_aggregation/validation-*.parquet
      - split: test
        path: record_aggregation/test-*.parquet
  - config_name: state_perturbation
    data_files:
      - split: train
        path: state_perturbation/train-*.parquet
      - split: validation
        path: state_perturbation/validation-*.parquet
      - split: test
        path: state_perturbation/test-*.parquet
  - config_name: table_lookup
    data_files:
      - split: train
        path: table_lookup/train-*.parquet
      - split: validation
        path: table_lookup/validation-*.parquet
      - split: test
        path: table_lookup/test-*.parquet

procedural-typed-decisions

Procedurally generated decision problems. Each row is one structured state (JSON, or a table, CSV, key=value lines, or prose for the arithmetic, retrieval, and aggregation configs) with several typed questions over that same state, following the Jev / System One request shape: choice (pick one criterion), noul (a number in [0, 1]; a probability or a yes/no), and score (an ordered rubric). Every answer is computed exactly from the state by rules that the state itself spells out, so the labels are noise-free.

This is an independent dataset. It is not an official TypeSafe Jev dataset and is not produced by or affiliated with TypeSafe or OpenJev.

Configs

config	questions
arithmetic	An order with a discount/shipping rule, an account ledger, or a schedule; each state asks 2–5 of: amount_due / final_balance / finish_time (choice among the result and typical slips), within_budget, went_negative, done_by_deadline (noul), random_line_bulk, random_is_deposit, random_is_long (noul, exact probability k/n), budget_use, net_change (score, descriptive levels), lines_above, withdrawal_count, starts_before_noon (score), largest_line, lowest_day, longest_task (choice)
entity_belief_tracking	world_location (choice), agent_belief_location (choice), belief_matches_world (noul)
event_state_reconstruction	current_owner (choice), is_open (noul), current_severity (score)
evidence_sufficiency	claim_supported (noul), has_conflict (noul), strongest_support_origin (choice)
multi_view_adjudication	intent (choice), is_urgent (noul), workflow_impact (score)
needle_retrieval	value_of_id (choice), id_has_value (noul), id_listed (noul); up to ~300 records whose ids differ from the target by one or two digits
partial_observation_calibration	incident_real (noul, exact Bayesian posterior)
policy_applicability	access_allowed (noul), governing_policy (choice), review_risk (score)
policy_under_uncertainty	access_allowed (noul), governing_policy (choice), requester_role (choice); exact posteriors over a role known through history counts and reports of stated reliability
record_aggregation	count_in_category (score), largest_quantity (choice), any_out_of_stock (noul), total_above (noul)
state_perturbation	material_change (noul), changed_dimension (choice), risk_direction (score)
table_lookup	find_person (choice, two-condition filter), manager_of (choice, join), started_before (noul), count_matching (score)

Schema

field	meaning
id	task:split:index
level	Difficulty level (0–4); larger levels add events, records, sensors, or distractors.
state	The state: a JSON string, or rendered text for the retrieval and aggregation configs.
questions	JSON object of named System One questions (type, instructions, criteria).
answers	JSON object of reference answers, in the System One answers shape.
one column per question	Flat label, for browsing and filtering: a ClassLabel for choice, score, and yes/no noul questions; a float for graded noul (incident_real, random_*); the option text for open numeric choices (amount_due, final_balance, finish_time). Null when the state does not ask that question (arithmetic only).

States are unique within a split, and validation/test states never occur in train.

Use

As a multi-question Jev request, send {"state": row["state"], "questions": json.loads(row["questions"])} (parsing the state first when it is JSON) and compare with row["answers"]. The same rows are included, grouped by state, in tasksource/tasksource-jev-typed-decisions.

Reproduction

Generation is deterministic (row i of a split is seeded by task:split:i). From a tasksource checkout:

PYTHONPATH=.:src python scripts/build_procedural_jev.py --output build/procedural-typed-decisions --upload

Generators live in src/tasksource/jev/procedural/.