LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems
arXiv:2512.01556v3 Announce Type: replace-cross Abstract: Foundation models often generate unreliable answers, while heuristic uncertainty estimators fail to fully distinguish correct from incorrect outputs, causing users to accept erroneous answers without any statistical guarantee. We address this problem through selection-conditioned risk control, aiming to ensure that an accepted prediction has an error probability no larger than a user-specified risk level. To this end, we propose LEC, a principled framework that reframes selective prediction as a decision problem governed by a linear exp
The proliferation of foundation models and increasing reliance on AI systems for critical tasks necessitates robust methods for controlling prediction reliability, driving research into practical risk management frameworks.
Ensuring statistical guarantees on prediction reliability is crucial for the safe and trustworthy deployment of AI across industries, impacting adoption, regulation, and public trust.
The introduction of principled frameworks like LEC provides a more statistically sound approach to managing uncertainty in AI outputs, moving beyond heuristic methods and enabling verifiable risk control in selective prediction and routing systems.
- · AI developers
- · High-stakes AI applications (e.g., medical, finance)
- · AI safety researchers
- · Enterprises adopting AI
- · AI systems relying solely on heuristic uncertainty estimates
- · Users unknowingly accepting erroneous AI outputs
- · Companies with poor AI risk management strategies
Increased trustworthiness and broader adoption of AI in risk-sensitive applications.
Development of industry standards and regulatory frameworks around verifiable AI reliability guarantees.
A competitive advantage for AI providers capable of offering statistically safe and controlled predictions, leading to consolidation or new market entrants.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG