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This empirical study finds that joint upper-bound coverage of traffic speed forecasts can be substantially improved via calibration, yet improved coverage does not translate into better route-choice utility in frozen offline proxy tasks on Beijing and Chengdu road-speed data.
PASC proposes a conformal prediction method for multi-stage NLP and LLM pipelines that provides finite-sample, distribution-free joint coverage guarantees across all stages, achieving higher empirical coverage and efficiency than baselines like Bonferroni and independent CP.