datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
clinical-cross-modal-memory-fidelity-v0.1Clinical Cross-Modal Memory Fidelity v0.1
Goal
Test whether prior image evidence is recalled accurately over time
Detect retroactive distortion driven by later narrative
Detect fabrication used to patch memory gaps
What it measures
memory_driftEarlier image facts are altered or inverted
fabricationNew findings are invented at recall
cross_modal_consistencyRecalled description matches original image evidence
How it works
Initial image facts are fixed and explicit
Intervening tasks… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-cross-modal-memory-fidelity-v0.1.modality-state-consistency-v0.1
What this dataset tests
Inputs arrive in many forms.
State must stay coherent.
Why it exists
Models drift when switching modality.
Facts change.
Promises vanish.
This set checks whether state stays consistent.
Data format
Each row contains
modality_context
user_message
modality_pressure
constraints
failure_modes_to_avoid
target_behaviors
gold_checklist
Feed the model
modality_context
user_message
constraints
Score for
cross-modal… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/modality-state-consistency-v0.1.