PromptShift-CRC: Drift-Aware Conformal Risk Control for Foundation Models Under Prompt and Domain Shift
arXiv:2606.15964v1 Announce Type: cross Abstract: Foundation models are now used in settings where the prompts they receive can change quickly. Users change, topics change, policies change, and the model may suddenly face a kind of request that was rare in the calibration data. This makes fixed calibration risky. Conformal prediction and conformal risk control give model-agnostic ways to control error, but they work best when the calibration data still look like the future data. This paper develops PromptShift CRC, a drift-aware conformal risk control method for foundation-model outputs under
Rapid deployment of foundation models into diverse, dynamic real-world environments necessitates robust drift-aware control mechanisms to ensure reliability and safety.
This development allows for more reliable and adaptable deployment of AI, particularly foundation models, critical for maintaining performance and trust in rapidly changing operational contexts.
Foundation models can be deployed with greater confidence in environments where prompts and data distributions are expected to shift, reducing the need for constant, manual recalibration.
- · Foundation model developers
- · Enterprises deploying AI
- · AI safety researchers
- · Developers of AI agents
- · Static AI calibration methods
- · Companies with brittle AI deployments
Foundation models become more robust and reliable in dynamic real-world applications.
Increased adoption of AI agents and automated systems that rely on adaptable foundation models.
Accelerated integration of AI into critical infrastructure and decision-making processes due to improved trustworthiness.
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