drift-detection
reasoning-drift-onset-detection-v0.1
Important Evaluation Limitation
Version 0.1 uses a highly regular trajectory structure in which the first drift step is frequently located at Step 4 and visible failure commonly appears at Step 5.
This creates a positional shortcut: a model may achieve inflated onset-detection performance by learning the dataset construction pattern rather than analysing the reasoning trajectory.
Version 0.1 should therefore be treated as a task-definition and scorer-validation release, not as a… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/reasoning-drift-onset-detection-v0.1.reasoning-drift-onset-detection-v0.2A SIOS structured reasoning-state benchmark for detecting when a reasoning trajectory loses a governing constraint, identifying the structural form of that drift, and assessing whether the failure is repaired.
Repository:
ClarusC64/reasoning-drift-onset-detection-v0.2
Version:
0.2.0
Publisher:
Clarus Invariant
Framework:
SIOS
Benchmark identity
Reasoning Drift Onset Detection v0.2 is not a single-label classification benchmark.
It is a structured reasoning-state benchmark.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/reasoning-drift-onset-detection-v0.2.clinical_structural_drift_detection_v0.1Clinical Structural Drift Detection
PurposeDetect when a clinical plan drifts from the evolving patient reality.
You get a case with time change signals.You decide if drift exists.You label the drift type.You propose the corrective adjustment.
Input fields
patient_summary
time_series
current_plan
observed_change
drift_signal
Required outputReturn one JSON object
drift_detectedyes or no
drift_typeMust match the allowed list
adjustmentOne sentence
Allowed drift_type values… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_structural_drift_detection_v0.1.ai-capability-hiding-drift-detection-v0.1
What this dataset is
This dataset detects drift in capability-hiding patterns over time.
It compares:
baseline probe capability vs baseline expressed performance
current probe capability vs current expressed performance
whether a change in monitoring context explains a new gap
The goal is not blame.
The goal is early warning that oversight changes expression.
What it tests
You detect when the monitored/unmonitored gap:
newly appears
widens
changes shape
You also avoid… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-capability-hiding-drift-detection-v0.1.metamaterial-resonance-coherence-drift-detection-v0.1Goal
Detect when a metamaterial stops matching its designed resonance behavior.
Core idea
Emergent properties vanish when three things stop moving together:
unit cell geometrysimulated resonancemeasured scattering response
This dataset tests whether a model can detect that coherence loss.
Inputs
SEM-derived geometry features
simulated resonance and expected band behavior
measured scattering parameters and resonance shift
stress context (temperature, humidity, cycling)
Required outputs… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/metamaterial-resonance-coherence-drift-detection-v0.1.euv-laser-pulse-shape-drift-detection-v0.1
What this dataset tests
Early detection of coherence drift between CO2 laser pulse-shape parameters and EUV pulse energy stability.
The key signal is not a single threshold breach.
It is loss of correlation between pulse shaping and the stability of EUV energy output.
Task
Given pulse and energy statistics, output:
drift_flag (0 or 1)
failure_mode (one of the allowed modes)
Allowed failure_mode values… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/euv-laser-pulse-shape-drift-detection-v0.1.