datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wave-propagation-1d
Wave1D-Propagation — StructBench canonical dataset
Download
One case, one file — fetch exactly what you need (pip install huggingface_hub):
from huggingface_hub import hf_hub_download, snapshot_download
# one case
path = hf_hub_download("StructBench/wave-propagation-1d",
filename="<case_id>.h5", repo_type="dataset")
# the full archive (resumable; cached under HF_HOME)
root = snapshot_download("StructBench/wave-propagation-1d"… See the full description on the dataset page: https://huggingface.co/datasets/StructBench/wave-propagation-1d.clinical-interventional-ripple-cross-system-propagation-mapping-v0.1What this dataset tests
Whether a model can map cross-system ripple propagationfrom a single intervention perturbation.
Required outputs
ripple_sequence_top6
lag_structure
propagation_signature
Systems tracked
autonomic
immune
metabolic
neuro_network
gut_microbiome
sleep
subjective_experience
Lag structure labels
immediate_0_6h
short_6_72h
medium_3_14d
long_2_12w
Propagation signature labels
damped_convergence
amplified_cascade
oscillatory_rebound… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-interventional-ripple-cross-system-propagation-mapping-v0.1.autonomous-driving-rss-incoherence-propagation-and-shockwave-detection-v0.1What this dataset tests
Whether a system can quantify
how incoherence propagates through traffic.
This is not collision detection.
It is shockwave and recovery measurement.
Required outputs
initial_disturbance_type
affected_agents_count
braking_wave_velocity
lane_stability_loss
recovery_time_s
propagation_severity_score
Scoring conventions
braking_wave_velocity is relative wave speed
lane_stability_loss ranges 0 to 1
propagation severity ranges 0 to 1
affected agents counts… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-rss-incoherence-propagation-and-shockwave-detection-v0.1.clinical-iatrogenic-cascade-propagation-mapping-v0.1What this dataset tests
Whether an intelligence system can tracehow a localized clinical failurepropagates into multi-system harm.
Required outputs
cascade pathway graph
amplification nodes
cross-system coupling failures
time to escalation
escalation acceleration points
preventable amplification flags
Use case
Second layer of the Iatrogenic Harm Cascade Library.
F1-cascade-propagation-and-failure-horizon-v0.1What this dataset tests
Whether a system can trace
how decoherence propagates across subsystems
and estimate time-to-failure.
Required outputs
initial_decoupling_pair
propagation_path
affected_components
cascade_velocity
predicted_failure_component
failure_horizon_laps
intervention_window_laps
containment_feasibility_score
Field meanings
cascade_velocity0 to 1higher means faster spread
failure_horizon_lapslaps until failure becomes likely
intervention_window_lapslaps… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/F1-cascade-propagation-and-failure-horizon-v0.1.hierarchy-constraint-propagation-across-levels-v0.1Constraint Propagation Across Levels v0.1
What this tests
Whether constraints set at the top level remain active and correctly applied in lower-level decisions.
Failure modes
constraint_droppedResponse approves a proposal without applying any top-level constraint
constraint_mutatedResponse references the constraint but allows an action that violates it
propagation_okResponse correctly carries the constraint into the subtask decision
How it works
top_level_constraints defines non-negotiables… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/hierarchy-constraint-propagation-across-levels-v0.1.ai-5node-infer-buf-lag-cpl-hallucination-propagation-v0.1
What this repo does
This dataset models hallucination propagation in agent ecosystems. It detects when inference pressure, weakened verification buffer, governance lag in review, and tight coupling through shared knowledge and automation cross the five-node cascade threshold into an unrecoverable hallucination propagation cascade.
This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state.The fifth node represents the nonlinear… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-5node-infer-buf-lag-cpl-hallucination-propagation-v0.1.