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Status Submitted
Created by Guest
Created on Sep 19, 2026

RELATED IDEAS

Tight adaptive abstention sets for human tie-breaking

Problem: When agents abstain or hedge, they produce over-wide candidate lists (useless) or overconfident single answers (dangerous). Honest uncertainty should be tight and adaptive, not vague.

Idea: Add an Orchestrate abstention module fusing evidential deep learning with conformal prediction, producing small, adaptive candidate sets that keep coverage guarantees — honest uncertainty with less hedging. Grounded in Karimi et al. (COPA 2024), whose evidential uncertainty sets were smaller and more adaptive than three state-of-the-art conformal methods.

Value: Human tie-breakers get short, trustworthy shortlists instead of noise or false confidence — faster resolution, fewer errors.

Idea priority Low
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