datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
autonomous-driving-decoherence-onset-detection-v0.1What this dataset tests
Whether a system can detect
the onset of system-wide decoherence.
Decoherence means:
camera, lidar, radar, and map
stop supporting a unified scene narrative.
Required outputs
decoherence_onset_timestamp
coherence_drop_delta
affected_modalities
narrative_conflict_flag
onset_confidence
early_warning_score
Scoring conventions
timestamp is seconds from window start
coherence drop delta is 0 to 1
conflict flag is 1 when the narratives diverge
early warning… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-decoherence-onset-detection-v0.1.2d-vdw-interface-decoherence-v0.1Goal
Predict failure in stacked 2D devices.
Core idea
Stacked device failure arrives when coupling collapses:
twist angle and alignmentinterlayer charge transferI-V behavior and leakagecontact resistance
stop moving together.
Inputs
twist angle
TEM alignment score
interlayer charge transfer efficiency
contact resistance
I-V nonlinearity index
leakage current
thermal cycles
humidity exposure
Required outputs
interface_coherence_score
decoherence_flag
decoherence_type… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/2d-vdw-interface-decoherence-v0.1.ai-constitutional-cross-axis-decoherence-mapping-v0.1
Goal
Detect cross-axis decoherence.
Meaning:
the model looks helpful
but violates honesty or harmlessness
or evades while staying “safe”
This catches failures that single-axis checks miss.
Inputs
constitution_excerptsuser_requestmodel_output
Required outputs
axis_status_mapFormat example: honesty=ok|harmlessness=violation|helpfulness=appears_ok
cross_axis_decoherence_flagyes | no
decoherence_patternExamples:
helpful_but_fabricated
unsafe_helpfulness… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-constitutional-cross-axis-decoherence-mapping-v0.1.aviation-pilot-vehicle-decoherence-source-attribution-v0.1What this dataset tests
Whether a system can correctly identifythe source of pilot–vehicle loop decoherence.
Sources may be:
pilotaircraftenvironmentmixednone
Key insightCorrect attribution determinesthe correct recovery action.
Required outputs
primary_decoherence_source
source_confidence
resonance_pattern_type
escalation_likelihood
contributing_factors
attribution_rationale
Use case
Layer two of Pilot–Vehicle Loop Coherence Under Stress.Feeds adaptive intervention and crew… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-pilot-vehicle-decoherence-source-attribution-v0.1.fusion-cross-field-decoherence-precursor-detection-v0.1What this dataset tests
Whether a system can detect early decoherenceacross three linked stability surfaces:
magnetic stability
temperature gradient structure
impurity behavior
The goal is not to detect quench.The goal is to detect the precursor window10 to 100 milliseconds before quench.
Required outputs
decoherence_precursor_flag
onset_ms_before_quench
precursor_pattern_label
disruption_risk_score
confidence_score
Use case
Layer 2 of COH-FUSION-001Plasma Stability Coherence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fusion-cross-field-decoherence-precursor-detection-v0.1.aviation-propulsion-aerodynamics-decoherence-precursor-detection-v0.1What this dataset tests
Whether a system can detect early decoherence
between propulsion behavior and aerodynamic response.
The signal is relationship drift:
lag expansion
correlation collapse
nonlinear divergence
oscillatory mismatch.
Required outputs
decoherence_onset_time
precursor_pattern_type
severity_gradient
failure_likelihood_index
estimated_time_to_critical_min
primary_decoupling_channels
Scoring conventions
onset time is minutes from window start
severity and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-propulsion-aerodynamics-decoherence-precursor-detection-v0.1.quantum-coupling-drift-decoherence-precursor-detection-v0.1What this dataset tests
Whether a system can detect early coupling driftbetween stabilizer syndromes (X/Z)and logical performance.
It targets the earliest phase of decoherence collapse:
correlations degrade before any single metric alarms.
Required outputs
decoherence_precursor_flag
drift_onset_day
precursor_pattern_label
risk_score_30d
confidence_score
Use case
Layer 2 of the Quantum T2 Collapse Prediction Trinity.
This dataset supports:
early warning systems
calibration… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/quantum-coupling-drift-decoherence-precursor-detection-v0.1.pharma-toxicity-decoherence-precursor-detection-v0.1What this dataset tests
Whether a system can detect early systemic decoherence that precedes overt toxicity.
It is not organ damage detection.
It is coupling loss detection.
Required outputs
decoherence_onset_time
decoherence_signature_set
affected_coupling_edges
precursor_pattern_label
time_to_overt_toxicity_estimate
confidence_score
Use case
Early safety screening.
Flag candidates that destabilize cross-tissue coherence
before late-stage attrition.
alphafold-interface-decoherence-under-stress-detection-v0.1
What this dataset tests
Whether a model can detectstress-induced interface decoherencebefore full dissociation.
Stress modes covered
pH shiftheat stressoxidative stress
The failure mode
Contact maps stop predicting bindingand cross-interface signal transmission weakens.
Inputs
stress_typestress_level
baseline_interface_coherencebaseline_kd_nMbaseline_contact_stabilitybaseline_allosteric_cross_interface_score… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alphafold-interface-decoherence-under-stress-detection-v0.1.F1-decoherence-trigger-localization-v0.1
What this dataset tests
Whether an intelligence system can identify the initiating disturbance that caused telemetry coherence to collapse across components.
The goal is not detecting failure.The goal is identifying the trigger.
Required outputs
trigger_event
trigger_lap
initiating_component
correlation_drop
propagation_direction
immediate_failure_risk
Use case
Layer two of the Failure Cascade Graph trinity.
Used for:
predictive failure analysis… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/F1-decoherence-trigger-localization-v0.1.