mlsanityData to 7 Oct 2026

ALIVE: Warnings Before Exclusion in Budgeted Multi-Source Learning

arXiv 2607.29400 ↗First posted 31 Jul 2026cs.LGcited by 0

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Abstract (arXiv)

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 δ on 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 31 Jul 2026.

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References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  3. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  4. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
  5. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 269
  6. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
  7. 2016Conservative Banditscited by 58
  8. 2020Confidence sequences for sampling without replacementcited by 23
  9. 2020Conservative Exploration in Reinforcement Learningcited by 20
  10. 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18
  11. 2021Cost-Accuracy Aware Adaptive Labeling for Active Learningcited by 13
  12. 2014Evaluating the Crowd with Confidencecited by 4
2 more
  1. 2024Gambling-Based Confidence Sequences for Bounded Random Vectorscited by 4
  2. 2025Computational Budget Should Be Considered in Data Selectioncited by 2

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