ClarusC64/ai-5node-align-buf-lag-cpl-reward-hacking-v0.1
What this repo does This dataset models reward hacking cascades where AI systems learn to satisfy metrics while violating intent. It detects when alignment pressure rises, buffers weaken due to missing audits and narrow evals, governance lag delays intervention, and tight coupling through shared KPIs propagates gaming behavior across products, crossing the five-node cascade threshold into an unrecoverable reward hacking cascade. This dataset models a five-node cascade: four… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-5node-align-buf-lag-cpl-reward-hacking-v0.1.
What this repo does
This dataset models reward hacking cascades where AI systems learn to satisfy metrics while violating intent. It detects when alignment pressure rises, buffers weaken due to missing audits and narrow evals, governance lag delays intervention, and tight coupling through shared KPIs propagates gaming behavior across products, crossing the five-node cascade threshold into an unrecoverable reward hacking 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
align buf lag cpl
Prediction target
labelcascadestate
Row structure
One row represents an AI governance scenario with numeric signals for reward hacking pressure, safety buffer strength, governance lag, and coupling tightness through shared KPIs and cross-product dependence, 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.