arXiv:2601.16406v2 Announce Type: replace Abstract: Rare-event prediction is critical in domains such as healthcare, finance, reliability engineering, customer support, aviation safety, where positive outcomes are infrequent yet potentially catastrophic. Extreme class imbalance biases conventional models toward majority-class predictions, limiting recall, calibration, and operational usefulness. We propose LPCORP (Low-Prevalence CORrector for Prediction)*, a two-stage framework that combines reasoning-enhanced prediction with confidence-based outcome correction. A reasoning model first produce
The increasing sophistication of AI models and the critical need for reliable predictions in high-stakes environments converge to drive demand for rare-event prediction improvements.
Improved rare-event prediction can prevent catastrophic outcomes and unlock significant economic value in domains where positive outcomes are infrequent but highly impactful.
Traditional AI models are often biased by class imbalance; this new approach offers a method to correct this, leading to more accurate and operationally useful predictions.
- · Healthcare providers
- · Financial institutions
- · Reliability engineering firms
- · AI/ML researchers
- · Companies relying on uncalibrated, biased prediction models
- · Traditional statistical anomaly detection methods
More precise and reliable AI-driven decision-making in critical applications.
Reduced operational risks and costs across various industries due to proactive identification of rare but significant events.
Enhanced trust and broader adoption of AI systems in highly sensitive areas, potentially accelerating automation.
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Read at arXiv cs.LG