Datasets:
id string | scenario string | api float64 | buf float64 | lag float64 | cpl float64 | notes string | label_cascade_state int64 |
|---|---|---|---|---|---|---|---|
AI5Q-0001 | Rate limits enforced per client. Circuit breakers active. On-call responds within minutes. | 0.42 | 0.82 | 0.12 | 0.38 | buffer strong | 0 |
AI5Q-0002 | Traffic increases and throttling kicks in. Triage occurs daily due to workload. | 0.56 | 0.62 | 0.54 | 0.5 | lag rising | 0 |
AI5Q-0003 | Agent loop drives request spikes. Alerts fire but response delayed. Retry policy amplifies load. | 0.66 | 0.48 | 0.72 | 0.64 | coupling tight | 1 |
AI5Q-0004 | Multiple services share the same key and quota pool. Spike propagates across products. | 0.74 | 0.4 | 0.78 | 0.76 | shared coupling | 1 |
AI5Q-0005 | Quota exhaustion triggers cascading failures in dependent tools. Rollback delayed. | 0.82 | 0.34 | 0.82 | 0.8 | late intervention | 1 |
AI5Q-0006 | Adaptive throttling and kill switch trigger automatically. Anomaly stops loops fast. | 0.58 | 0.72 | 0.16 | 0.52 | fast response | 0 |
AI5Q-0007 | Throughput pressure loosens rate limits. Weekly review misses rising loop risk. | 0.72 | 0.42 | 0.76 | 0.72 | buffer eroded | 1 |
AI5Q-0008 | New workflow tested in sandbox with strict quotas. Live monitor can pause. | 0.46 | 0.78 | 0.14 | 0.44 | recoverable | 0 |
AI5Q-0009 | Retry storms and shared quotas overwhelm monitoring. Triage fails for days. | 0.88 | 0.26 | 0.86 | 0.88 | cascade engaged | 1 |
What this repo does
This dataset models quota spike cascades caused by agent loops and retry storms. It detects when API load pressure rises, protective buffers weaken, governance lag delays containment, and tight coupling through shared quota pools and dependent tools crosses the five-node cascade threshold into an unrecoverable quota spike cascade.
This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state.
The fifth node represents the nonlinear transition from recoverable drift to systemic cascade.
Core quad
api
buf
lag
cpl
Prediction target
label_cascade_state
Row structure
One row represents an AI operations scenario with numeric signals for API load pressure, buffer strength from rate limits and circuit breakers, governance lag, and coupling tightness through shared quotas and dependencies, paired with a cascade state label.
Files
data/train.csv
data/tester.csv
scorer.py
Evaluation
Run predictions on data/tester.csv and score with scorer.py.
License
MIT
Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the geometry.
Production-scale data determines operational exposure.
What Production Deployment Enables
• 50K–1M row datasets calibrated to real operational patterns
• Pair, triadic, and quad coupling analysis
• Real-time coherence monitoring
• Early warning before cascade events
• Collapse surface and recovery window modeling
• Integration and implementation support
Small samples reveal structure.
Scale reveals consequence.
Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.
For dataset expansion, custom coherence scorers, or deployment architecture:
team@clarusinvariant.com
Instability is detectable.
Governance determines whether it propagates.
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