datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EmbodiedGenDatahttps://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Gallery-Explorer
OpenVid-1M
Summary
This is the dataset proposed in our paper "OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation".
OpenVid-1M is a high-quality text-to-video dataset designed for research institutions to enhance video quality, featuring high aesthetics, clarity, and resolution. It can be used for direct training or as a quality tuning complement to other video datasets.
All videos in the OpenVid-1M dataset have resolutions of at least 512×512. Furthermore, we… See the full description on the dataset page: https://huggingface.co/datasets/lodestone-horizon/OpenVid-1M.long-horizon-drift-v0.1c
What this dataset tests
Long arcs bend.
Past stability can hide future risk.
Why it exists
Long plans fail when drift accumulates.
This set checks whether you
detect slow shifts
reject frozen baselines
project forward risk
set milestones
Data format
Each row contains
long_horizon_context
user_message
drift_pressure
constraints
failure_modes_to_avoid
target_behaviors
gold_checklist
Feed the model
long_horizon_context
user_message… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/long-horizon-drift-v0.1c.mid-horizon-drift-v0.1b
What this dataset tests
Mid-range trends can lie.
Mix shifts.
Definitions shift.
Leading indicators speak first.
Why it exists
Models over-trust blended mid-horizon metrics.
They miss
mix drift
variance rise
definition breaks
confounds
early warning signals
This set forces those traps.
Data format
Each row contains
mid_horizon_context
user_message
drift_pressure
constraints
failure_modes_to_avoid
target_behaviors
gold_checklist
Feed the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/mid-horizon-drift-v0.1b.fusion-hybrid-target-failure-horizon-and-control-routing-v0.1
What this dataset tests
Whether a system can estimate time to target damage and select the correct control response.
Core signal
Failure is preceded by coherence loss between:
beam stabilitythermal gradientneutron yieldstructural stress
The model must forecast when damage becomes inevitableand route to the minimal stabilizing intervention.
Required outputs
failure horizoncontrol route
Why it matters
Hybrid reactor targets fail through cascading… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fusion-hybrid-target-failure-horizon-and-control-routing-v0.1.clinical-control-horizon-sepsis-v1
Clinical Control Horizon Sepsis Detection
Overview
This dataset tests whether a model can detect whether a proposed control strategy has a sufficient stabilization horizon in a sepsis-like clinical system.
Some control strategies stabilize a system only briefly. Others maintain enough forward influence to guide the system into a durable recovery basin.
The goal of this benchmark is to determine whether the controller can reliably sustain stabilization across a meaningful… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-control-horizon-sepsis-v1.fusion-divertor-heat-flux-failure-horizon-and-control-routing-v0.1
What this dataset tests
Failure horizon reasoning for divertor protection.
The system can be “nearly detached”
while the heat flux margin collapses.
This dataset forces the model to do two things.
Predict time-to-limit.
Route the right control action.
Inputs
upstream_temp_kevupstream_density_1e19m3upstream_power_to_sol_mwstrike_point_location_mm
divertor_target_temp_cdivertor_leg_density_1e19m3neutral_pressure_paradiation_fraction… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fusion-divertor-heat-flux-failure-horizon-and-control-routing-v0.1.fusion-msr-corrosion-failure-horizon-and-chemistry-routing-v0.1
Dataset goal
Forecast when corrosion drift becomes structural failure riskand route the correct chemistry and operational intervention.
Required outputs
projected failure horizon
failure horizon band
primary risk driver
chemistry control action
maintenance action
What it tests
Whether a model can move beyond drift detectionand determine:
how long until failurewhat is driving itwhat intervention stabilizes the system
Why it matters… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fusion-msr-corrosion-failure-horizon-and-chemistry-routing-v0.1.ai-goal-drift-long-horizon-coherence-risk-v0.1What this repo is for
Detect when systems slowly drift away from original goals across long task chains.
Focus:
long-horizon agent behavior
proxy goal takeover
step-by-step objective mutation
hidden optimization drift
This dataset targets one of the hardest alignment failures: gradual goal shift over time.
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.aviation-propulsion-aerodynamics-failure-horizon-intervention-mapping-v0.1What this dataset tests
Whether a system can turn detected decoherence
into an operational action plan.
It must estimate horizon,
choose intervention,
and define the decision window.
Required outputs
failure_horizon_minutes
recommended_derate_level
diversion_priority
stability_recovery_probability
intervention_window
action_rationale_channels
Scoring conventions
horizon is minutes to critical instability
diversion priority is low, medium, high, or urgent
intervention window is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-propulsion-aerodynamics-failure-horizon-intervention-mapping-v0.1.ai-alignment-failure-horizon-and-intervention-routing-v0.1
Goal
Predict when an AI system will cross fromproxy optimizationinto full alignment failure.
Then route the minimal interventionbefore collapse.
What this tests
alignment drift trajectory
failure horizon prediction
intervention timing
severity estimation
Required outputs
System must identify:
proxy vs objective
drift stage
failure horizon
intervention strategy
Why it matters
Alignment rarely fails instantly.
It drifts first.Then… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-alignment-failure-horizon-and-intervention-routing-v0.1.humanoid-long-horizon-intent-memory
Long-Horizon Intent Memory
Captures how humanoids retain and adapt to long-term human goals.
aviation-flight-control-integrity-horizon-maintenance-mapping-v0.1What this dataset tests
Whether a system can convert detected phase-space distortion
into a maintenance horizon and schedule.
The point is not fault blame.
It is safe remaining cycles and workload risk.
Required outputs
integrity_horizon_flights
maintenance_priority
workload_risk_index
oscillation_risk_probability
replacement_window
intervention_rationale
Scoring conventions
horizon is remaining flights to unacceptable integrity risk
workload and oscillation risk range 0 to 1… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/aviation-flight-control-integrity-horizon-maintenance-mapping-v0.1.smart-material-failure-horizon-intervention-routing-v0.1Goal
Forecast when a smart material stops functioning as a smart system.
Then route the best intervention.
Core idea
Early drift is useful only if it changes action.
This dataset tests whether a model can:
estimate remaining effective cycles
choose an intervention strategy
estimate recoverability vs replacement
Domains
shape memory alloys
self-healing polymers
electrochromic materials
Inputs
baseline and current coherence
drift rate and variance growth
response latency and amplitude
cycle… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/smart-material-failure-horizon-intervention-routing-v0.1.fission-fuel-rod-failure-horizon-and-mitigation-routing-v0.1Goal
Predict when fuel rod integrity will fail
and what mitigation should be taken.
This is the third layer in the fuel-cladding coherence trinity.
Layer 1
Baseline coupling
Layer 2
Drift detection
Layer 3
Failure horizon and routing
Model outputs
failure_horizon_cycles
mitigation_action
Why it matters
Fuel rod failures rarely occur instantly.
They emerge from sustained thermo-mechanical drift.
Predicting the horizon allows:
power derating
inspection scheduling
controlled shutdown
avoidance of… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fission-fuel-rod-failure-horizon-and-mitigation-routing-v0.1.fusion-tbr-fuel-self-sufficiency-horizon-and-routing-v0.1Dataset goal
Forecast when the reactor loses fuel self-sufficiency.
Not a post-mortem.
A horizon and routing tool.
Required outputs
shortfall_risk_band
green | amber | red
estimated_tbr_crossing_hours
hours until observed TBR crosses target floor
fuel_shortfall_g_day
expected deficit if no intervention
priority_action
monitor | adjust | inspect | mitigate | derate
action_set
suggested actions
minimal_fix_set
smallest action bundle that plausibly restores stability
What this captures
TBR trend… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/fusion-tbr-fuel-self-sufficiency-horizon-and-routing-v0.1.energy-grid-cascade-horizon-and-intervention-routing-v0.1Goal
Predict how long until a grid cascadeand route the minimal stabilizing intervention.
What it tests
Whether a system can:
estimate cascade horizon
localize primary risk cluster
select stabilizing interventions
quantify stabilization gain
Inputs
phase spread
frequency variance
intertie loading
reactive reserve
inertia
coherence decay metrics
Outputs
cascade_horizon_min
primary_risk_cluster
intervention_set
expected_stabilization_gain
confidence_score
Why it… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/energy-grid-cascade-horizon-and-intervention-routing-v0.1.euv-collector-failure-horizon-and-cleaning-routing-v0.1
Dataset purpose
Predict the failure horizon of an EUV collector mirrorand route optimal cleaning intervention timing.
A scanner does not fail instantly.Productivity collapses after coherence decay reaches a tipping point.
This dataset maps the final phase:
reflectivity declinepower transmission losscontamination growthcoherence drift
Task
Given system state, output:
estimated_hours_to_failurecleaning_recommendedfailure_horizon_flag
Why it matters
Unplanned… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-collector-failure-horizon-and-cleaning-routing-v0.1.fusion-hybrid-controllability-loss-horizon-and-control-routing-v0.1What this dataset tests
Forecasts when a hybrid reactor will lose controllable power.
Maps stability margin collapse into a time horizon.
Routes optimal intervention.
Required outputs
time_to_control_loss_min
recommended_action
Use case
Predictive stabilization for subcritical or hybrid reactors.
Prevents loss of controllability before shutdown conditions.
ai-goal-failure-horizon-and-realignment-routing-v0.1What this dataset is
Predicts how soon goal drift becomes a hard failure
Names the realignment window before collapse
Forces an intervention choice with triggers and monitoring
Inputs
setting
env_shift_event
observed_drift_markers
goal_representation_summary
behavioral_deviation_summary
system_constraints
intervention_options
Gold fields in the CSV
failure_mode
estimated_failure_horizon_steps
realignment_window_steps
gold_intervention_choice
realignment_trigger_conditions… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-goal-failure-horizon-and-realignment-routing-v0.1.short-horizon-drift-v0.1