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