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From Outcomes to Components: A Counterfactual Evaluation Framework for LLM-Proposed Repairs

Hengyu Ye, Songhua Hu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26650 · cited by 0 works (OpenAlex) · open-access PDF

Abstract

An LLM-generated repair action specifies which records to change and by how much. A failed action may still contain a useful target set or numerical update, depending on how the two are paired. We evaluate this problem in traffic networks and databases, where aggregate observations obscure the records that need correction. We examine the pairings by executing each combination from the same initial state, using a reference action that restores the affected records. In 76 of 114 traffic actions, the gain from correcting both parts exceeds the sum of the gains from correcting each part alone. Six of these actions become worse after either correction alone, but improve when both parts are corrected. In database repair, replacing only the proposed update with the reference update worsens 17 of 108 model actions, all of which select unaffected records. We also test whether parts of two proposals can be reused. On held-out observations, selecting among the proposals and their recombined target-update pairs recovers 61.6% of the reference action's loss reduction, compared with 58.0% from selecting between the proposals alone.

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Topics: Counterfactual thinking, Set (abstract data type), Aggregate (composite), Action (physics), Computer science, Aggregate data

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