arXiv:2510.04406v2 Announce Type: replace-cross Abstract: Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opportunities to exploit and understand modular structure. We introduce a conformal prediction framework for two-stage sequential models, where an upstream predictor generates intermediate representations for a downstream model. By decomposing the overall prediction residual into stage-specific components, our method enables practitioners to attribute uncertainty
This research addresses a critical limitation in current AI model interpretability and reliability by introducing a methodical approach to uncertainty quantification in modular systems, crucial as AI systems become more complex.
A strategic reader should care because improving the reliability and interpretability of multi-stage AI models directly impacts the trustworthiness and deployment potential of advanced AI systems in critical applications.
The ability to attribute uncertainty to specific stages within sequential AI models allows for more targeted improvements, better error diagnosis, and potentially more robust system design.
- · AI developers
- · High-stakes AI applications (e.g., medical, autonomous driving)
- · AI explainability platforms
- · MLOps platforms
- · Black-box AI models
- · Systems with undifferentiated error reporting
Individual components of complex AI systems can be evaluated and optimized for uncertainty more effectively.
This could lead to a proliferation of modular, verifiable AI architectures, increasing confidence in AI deployments for sensitive tasks.
Improved modular reliability facilitates the development of larger, more complex AI agents that can attribute and mitigate errors internally, accelerating the 'ai-agents' paradigm.
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Read at arXiv cs.LG