Datasets:
id string | scenario string | model float64 | buf float64 | lag float64 | cpl float64 | notes string | label_cascade_state int64 |
|---|---|---|---|---|---|---|---|
AI5MV-0001 | Single pinned model version per environment. Rollout uses canary and strict routing. | 0.42 | 0.82 | 0.12 | 0.38 | buffer strong | 0 |
AI5MV-0002 | Two versions run during gradual rollout. Routing mostly stable. Review happens daily. | 0.56 | 0.62 | 0.54 | 0.5 | lag rising | 0 |
AI5MV-0003 | Routing drift causes version mixing within one workflow. Outputs conflict. Audit backlog grows. | 0.66 | 0.48 | 0.72 | 0.64 | conflict forming | 1 |
AI5MV-0004 | Multiple services depend on shared embeddings and tools. Version conflict propagates across products. | 0.74 | 0.4 | 0.78 | 0.76 | coupling tight | 1 |
AI5MV-0005 | Version conflict breaks safety assumptions and tool protocols. Rollback delayed. Exposure expands. | 0.82 | 0.34 | 0.82 | 0.8 | late intervention | 1 |
AI5MV-0006 | Contract tests enforce compatibility. Any mismatch triggers immediate reroute and rollback. | 0.58 | 0.72 | 0.16 | 0.52 | fast response | 0 |
AI5MV-0007 | Throughput pressure relaxes contract tests. Weekly review misses early mixing and drift. | 0.72 | 0.42 | 0.76 | 0.72 | buffer eroded | 1 |
AI5MV-0008 | New model version tested in shadow mode with strict routing checks. Pause available. | 0.46 | 0.78 | 0.14 | 0.44 | recoverable | 0 |
AI5MV-0009 | Version conflict across coupled services. Alert flood prevents triage for days. Inconsistent actions persist. | 0.88 | 0.26 | 0.86 | 0.88 | cascade engaged | 1 |
What this repo does
This dataset models model version conflict cascades during AI rollouts. It detects when version pressure rises, buffers weaken due to mixed routing and missing compatibility checks, governance lag delays rollback, and tight coupling through shared tools and embeddings propagates incompatibilities across products, crossing the five-node cascade threshold into an unrecoverable model version conflict 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
model
buf
lag
cpl
Prediction target
label_cascade_state
Row structure
One row represents an AI deployment scenario with numeric signals for model version conflict pressure, safety buffer strength, governance lag, and coupling tightness through shared tooling and cross-service 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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