README.md

---
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.