ALIVE: Warnings Before Exclusion in Budgeted Multi-Source Learning

2026-07-31

AI Summary

This work addresses the challenge of designing an auditable, evidence-driven authorization mechanism for decisions with heterogeneous persistence—such as transient routing versus permanent exclusion—in budget-constrained multi-source learning. We propose ALIVE, the first framework to decouple persistent exclusion actions from non-persistent routing decisions, introducing an auditable exclusion protocol grounded in evidence thresholds and capacity feasibility. Under a shared audit-and-learning budget, source exclusion is permitted only when a strict majority inconsistency condition is met and dual-certificate verification succeeds. By integrating sampling without replacement via random prefix selection with Serfling/FPC finite-population corrections, ALIVE provides theoretical family-wise error rate guarantees at any time. Experiments show that ALIVE improves accuracy–AUBC by 0.1935 percentage points over pure routing on CIFAR, substantially reduces median evidence volume in the PPR engine (e40 drops from 304 to 96), and covers 88% of samples with shorter prefixes on a fixed natural panel.

Abstract

A routing decision can be revised at the next transaction, but a latched source exclusion persists across later decisions. We ask what evidence should authorize these unequal-persistence actions when finite-population auditing and learning share a budget. ALIVE (Action-Layered Intervention via Evidence) is an auditable control layer: one randomized without-replacement prefix supplies cached evidence, heuristic warnings drive non-latching floor-bounded routing, and only two fresh simultaneous certificate separations may latch an exclusion request subject to capacity-feasible activation. Conditional on fixed support and labels under an ideal uniform audit permutation, any predictable controller preserving this interface inherits an anytime familywise bound of δon acting against a source that fails the pre-fixed absolute or relative strict-majority-disagreement predicate. With a published known-size, all-strict-majority PPR engine, median evidence count fell from 304 to 96 identities in e40 and from 171 to 62 in e60, while both engines used 48 in e80. In the matched CIFAR controller, the persistent-action layer added +0.1935 accuracy-AUBC percentage points over routing-only in all ten paired seed clusters. The +0.1954-point full-system contrast against CBR was also positive but did not meet the predeclared multiplicity-adjusted criterion (conditional Holm-adjusted sign-flip reference value =.097656). On a fixed natural panel, exploratory PPR used a median closure prefix of 95 rather than 105 for exploratory Serfling/FPC, but still exposed 88.0% of the panel and had no downstream task. Together these results map a restraint--power--cost--utility boundary: the action contract controls a defined persistent decision, while net value depends on evidence margin, audit cost, and budget regime.

Problem

• budgeted learning
• multi-source learning
• source exclusion
• auditing
• persistent decisions

Innovation

• ALIVE
• budgeted multi-source learning
• auditable control layer
• persistent exclusion
• familywise error bound