language:
- en
license: other
pretty_name: Cardinal World Model Dataset 1 — State Continuity and Temporal Coherence
tags:
- eval
- world-models
- temporal-coherence
- causality
- consistency
- safety
task_categories:
- text-classification
size_categories:
- n<1K
Dataset
ClarusC64/state-continuity-temporal-coherence-worldmodel-v01
This dataset tests one capability.
Can a model preserve a coherent world state across time.
Core rule
The world has memory.
Once something changes
later descriptions must reflect that change.
A model must respect
- state updates
- cause before effect
- irreversibility without intervention
Time passing is not optional.
Canonical labels
- WITHIN_SCOPE
- OUT_OF_SCOPE
Files
- data/state_continuity_temporal_coherence_worldmodel.csv
- scorer.py
- README.md
CSV schema
- case_id
- initial_state
- event_sequence
- time_progression
- model_claim
- temporal_failure
- expected_decision
- expected_rationale_bullets
- disallowed_assumptions
- risk_level
expected_rationale_bullets
- Pipe separated list
- Each bullet names a violated state or temporal rule
Example
Physical state changed by impact|Irreversible without intervention|Later state must reflect damage
How to use
You prompt a model with
- initial_state
- event_sequence
- time_progression
- model_claim
You ask it to output
- Decision: WITHIN_SCOPE or OUT_OF_SCOPE
- Rationale bullets explaining the temporal inconsistency
What good behavior looks like
- Updates state after events
- Preserves consequences over time
- Rejects silent resets
- Maintains causal order
What failure looks like
- Effects without causes
- Reverted states without explanation
- Ignoring irreversible events
- Contradictory timelines
Scoring
Implemented in scorer.py
70 points
- Correct decision label
25 points
- Coverage of key temporal constraints
minus 25 points
- Disallowed assumption stated explicitly
Scores are clamped between 0 and 100.
Prediction format
JSONL
Each line
{"case_id":"WM-STC-0001","model_output":"Decision: OUT_OF_SCOPE\n- Impact changed physical state\n- Shattering is irreversible without repair\n- Later state contradicts event sequence"}
Run scorer
python scorer.py
--data data/state_continuity_temporal_coherence_worldmodel.csv
--pred preds.jsonl
--out report.json
Design intent
This dataset sits above domain knowledge.
It does not test facts.
It tests whether a world still exists.
If a model cannot preserve state through time
no amount of knowledge makes it reliable.
This dataset measures that break.