datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
counterfactual-action-invariants-v0.1
What this dataset tests
Leaders demand causality.
Reality gives entanglement.
You must keep invariants.
Why it exists
Models often answer a forced question.
They pick one cause.
They fake proof.
This set checks whether you
resist false certainty
name confounders
propose a valid counterfactual method
turn pressure into a decision gate
Data format
Each row contains
scenario_context
user_message
counterfactual_pressure
constraints… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/counterfactual-action-invariants-v0.1.temporal-drift-invariants-v0.1
What this dataset tests
Time moves.
Assumptions decay.
You must notice.
Why it exists
Many replies treat yesterday as today.
That breaks decisions.
This set checks whether you detect drift and hold invariants.
Data format
Each row contains
timeline_context
user_message
drift_pressure
constraints
failure_modes_to_avoid
target_behaviors
gold_checklist
Feed the model
timeline_context
user_message
constraints
Score for
drift detection
time… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/temporal-drift-invariants-v0.1.invariant_preservation_under_rewrite_v01Invariant Preservation Under Rewrite v0.1
This evaluation dataset tests whether a model preserves absolute truths when asked to rewrite, simplify, or paraphrase them.
This is not training data.
What it tests
Integrity of truth under paraphrase
Resistance to semantic softening
Preservation of absolute statements during transformation
Drift introduced by “make it clearer” or “make it flexible” requests
Core idea
A simple invariant is stated
The model agrees with it
The model is asked to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/invariant_preservation_under_rewrite_v01.