datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
counterfactual-intervention-integrity-worldmodel-v01
Dataset
ClarusC64/counterfactual-intervention-integrity-worldmodel-v01
This dataset tests one capability.
Can a model reason cleanly about interventions without breaking the world.
Core rule
Interventions have local consequences.
Changing one thing
must change what depends on it
must not change what does not
No magic.
No silent propagation.
No ignored causes.
Canonical labels
WITHIN_SCOPE
OUT_OF_SCOPE
Files… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/counterfactual-intervention-integrity-worldmodel-v01.state-continuity-temporal-coherence-worldmodel-v01
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 changeslater 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… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/state-continuity-temporal-coherence-worldmodel-v01.robotics-world-model-coherence-drift-v0.1What this repo is for
You use it to detect when a robot’s internal world model no longer matches reality.
It captures failures like:
map outdated after environment change
localisation drift
object position mismatch
robot navigating into blocked space
Applies to:
warehouse robots
delivery robots
drones
inspection robots
industrial manipulators
Prompt format
Return exactly one token
coherent or incoherent
robotics-world-model-consistency-v0.1What this dataset tests
The internal world model matches perception and reality
No duplication, teleporting, or stale entities
Identity tracking stays stable
Why this exists
Embodied systems fail when state tracking breaks
duplicated objects
teleport assumptions
stale memory
wrong identity
impossible combined states
This set labels those breaks
Data format
perception_updates
memory_state_before
world_model_after
observed_world_after
Task
Emit one… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/robotics-world-model-consistency-v0.1.cross-domain-constraint-consistency-worldmodel-v01world_modelWorld Model Container Dataset
Dataset summary
This dataset tests whether models can locate themselves inside a container such as a room, pitch, corridor, or line before predicting events or motion.Focus is on spatial grounding, object persistence, and behavior inside clear boundaries.
Supported tasks
Spatial grounding for world models
Video and image based container detection
Object persistence and absence tracking
Occlusion handling and recovery
Drift analysis in spatial reasoning… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/world_model.