leo-bjpark/multi-principal-authority

Authority Data

Synthetic action-authorization datasets for testing whether a model can resolve priority-ordered user policies. Every row contains a query, a randomized priority declaration, randomized user-policy presentation order, and one of three answers: Permitted, Prohibited, or Undecidable.

Dataset contract

• Labels. Permitted and Prohibited split the rows with at least one matching user as evenly as possible. Undecidable is used exactly for rows with no matching user.
• Rows per user count. Each dataset contains 100 rows for every permitted user count. Train therefore has 300 rows for 1–3 users; test has 700 rows for 1–7 users.
• Decision rule. Inspect users in the declared priority order. The first user whose policy context completely matches the query decides the answer. Their action is Permitted or Prohibited; if no user matches, the answer is Undecidable.
• Controlled context difficulty. Every row records context_count, the number of query conditions. Within each user-count bucket, rows are spread as evenly as possible across every available count from 1 through the split's category capacity. Every policy uses that complete category set; a non-matching policy differs from the query in exactly one value. Context cardinality therefore measures conjunction difficulty without being confounded by omitted conditions.
• Matching-user-count balance. Each partial-matching bucket distributes its 100 rows as evenly as possible across 0 through its user count. When 100 does not divide exactly, a positive-count bucket receives any extra row before the zero-match bucket does. Undecidable is exactly the zero-match group. Thus a 4-user bucket has 20 rows each for 0, 1, 2, 3, and 4 matching users. Permitted and Prohibited are allocated jointly with matching count, rather than being independently shuffled into those buckets.
• All-user context matching. The matching-control config gives every user a complete query-context rule. It has no Undecidable rows: each user-count bucket is balanced as 50 Permitted / 50 Prohibited. Undecidable always means that no policy completely matched the query, and every row has exactly its full user count as matching users.
• Randomized order and names. Each row samples distinct person names (for example, Alice and Bob). Priority is a random permutation of those names, not an alphabetical list. User-policy blocks, rule blocks, condition keys, and value lists are independently shuffled before rendering.
• Semantic uniqueness. Rows are unique as reasoning instances. A changed JSON key order or policy presentation order does not count as a distinct example; query, named policies, priority, and final label must differ.
• No category leakage. A category appears in either train or test for a dataset family, never both. Tool names are likewise split by partition.

The requested Tool constraint lists contained day on both sides. To honor the no-overlap requirement, the duplicate test-side day is removed: Tool train uses day,time; Tool test uses month,year,information_type,purpose,recipient.

Configs

Config	Family	Context mode	Query / policy style	Train / test
MPA-tool-baseline	Tool	Partial matching	Structured bullets	300 / 700
MPA-common-baseline	Common action	Partial matching	Structured bullets	300 / 700
MPA-tool-all_user_context_matching	Tool	All users match; 50/50 labels, no Undecidable	Structured bullets	300 / 700
MPA-tool-natural_language	Tool	Partial matching	Natural-language rules and query	300 / 700

Split partitions

Family	Split	Categories	Tools
Tool	Train	day, time	send_email, send_text_message, submit_online_form
Tool	Test	month, year, information_type, purpose, recipient	make_phone_call, send_voice_message, start_live_chat
Common	Train	date, time	—
Common	Test	day, month, year	—

Data layout and schema

The default root seed is 42. It is stored in both the output path and every row, while a stable hash of root seed, config name, and split produces an independent split seed. Generating one config or all configs therefore gives the same files for a given root seed.

data/base/
└── seed-42/
    ├── MPA-tool-baseline/{train,test}.jsonl
    ├── MPA-common-baseline/{train,test}.jsonl
    ├── MPA-tool-all_user_context_matching/{train,test}.jsonl
    └── MPA-tool-natural_language/{train,test}.jsonl

Generate the canonical data or another reproducible seed with:

python make_data.py
python make_data.py --seed 123

Each JSONL row has these public fields:

Field	Description
id	Split-local row ID
prompt_multi_printical	Ordered JSON object mapping each person name to {context, authorization} policy data
prompt_priority	Priority-ordered JSON name list, such as ["Bob", "Alice"]
prompt_query	Rendered query block
prompt_example	Object with per-user user_constraints_text messages and a final query_priority_final_text message
label	Permitted, Prohibited, or Undecidable
AuthoritySetting	Structured user policies and optional tool
Query	Structured query attributes and optional tool
metadata	Config, split, user/context counts, priority, category provenance, and matching evidence

The canonical schema is intentionally direct:

{
  "prompt_multi_printical": {
    "Alice": {
      "context": {"day": "monday", "time": "09:00-12:59"},
      "authorization": "Permitted"
    }
  },
  "prompt_priority": ["Alice"],
  "prompt_query": "Query:\n- Day: Monday\n- Time: 09:00-12:59",
  "prompt_example": {
    "user_constraints_text": ["User Alice:\n- Context: Day = Monday; Time = 09:00-12:59\n- Authorization: Permitted"],
    "query_priority_final_text": "Task, query, priority, and answer-format message"
  },
  "label": "Permitted",
  "AuthoritySetting": {
    "users": [{"user": "Alice", "rules": [{"conditions": {"day": "monday", "time": "09:00-12:59"}, "authorization": "Permitted"}]}]
  },
  "Query": {"attributes": {"day": "monday", "time": "09:00-12:59"}},
  "metadata": {
    "priority": ["Alice"],
    "presentation_order": ["Alice"],
    "user_count": 1,
    "context_count": 2,
    "matching_mode": "partial",
    "matching_users": ["Alice"],
    "matching_user_count": 1,
    "deciding_user": "Alice",
    "generation": {"root_seed": 42, "split_seed": 123456789}
  }
}

authorization is a user-level result for an action under a context. label is the final answer after priority resolution. Undecidable occurs only when matching_users is empty; it is never a policy authorization.

prompt_multi_printical is an insertion-ordered object in presentation_order. prompt_priority is the sole authority order. The first name in matching_users is deciding_user; it need not be the first name in prompt_priority because higher-priority users may not match.

Repository layout

Only the generation, inspection, and publication files are kept:

File	Responsibility
src/domains.py	Person names, condition values, display labels, tools, and split partitions
src/sampling.py	Controlled query-context, one-value mismatch, and randomized-order sampling
src/prompts.py	prompt_multi_printical, priority, query, and prompt-example rendering
src/dataset.py	Configs, row construction, balance allocation, validation, JSONL writing, and generator CLI
make_data.py	Short generator entry point
shells/browse_samples.py	Keyboard-driven three-pane sample browser
shells/print_random_sample.sh	Browser launcher and non-interactive random sample printer
hf_push.py	Validation and Hugging Face publication for the four canonical configs

There are no separate docs, test, paraphrase, category, attribute, or polarity pipelines. README.md is the complete project documentation.

Validation

python make_data.py --output-dir /tmp/authority-data-check
python hf_push.py --base-dir /tmp/authority-data-check --dry-run

The generator validates per-user-count label, matching-user-count, and context-count balance, split row counts, user-count ranges, category/tool disjointness, random priority order, semantic-row uniqueness, and the gold label for every emitted row.

hf_push.py publishes only the four MPA configs to leo-bjpark/multi-principal-authority by default. Before a non-dry-run upload, it removes the known retired AuthorityBench config folders; pass --keep-obsolete-configs to retain them. The raw JSONL keeps metadata.generation seeds as integers; the Hub export represents those two seed values as strings so unsigned split seeds remain portable in Arrow.

Interactive sample browser

Run the sample script in a terminal to open a single-page keyboard browser:

bash shells/print_random_sample.sh

At the fixed top of the page, use Tab (or ↑/↓) to select MPA version, split, label, user count, matching-user count, context count, or sample number; use ←/→ to change that selection. Label, user-count, matching-user-count, and context-count options are laid out on their own rows (All, then each concrete value); the focused option is highlighted. They filter the displayed samples and the statistics below them. The three panes show priority/query/label/evidence on the left, prompt_multi_printical in the middle, and prompt_example on the right. j/k or PageUp/PageDown scroll the middle pane, n/m scroll the right pane, and [/] scroll the left pane. q exits. For the previous one-shot random text output, run:

INTERACTIVE=0 bash shells/print_random_sample.sh