Validating Repair Claims after Evidence Restoration in Compressed Prompts

An answer that recovers after deleted evidence is restored does not, by itself, establish a reliable repair. The recovery may depend on sampling variation or on other changes to the prompt. We evaluate repair claims by fixing the restoration candidate and testing it against three distinct length-matched sham texts in fresh paired calls. An entry screen restricts search to questions that pass with the full context and fail after compression. With controlled deletion at a retention ratio of 0.5, protocol pass rates rose from 20.0% to 45.0% on 60 development items. On 100 held-out test items, they rose from 17.0% to 36.0%. Of 36 search candidates, 34 passed confirmation; one failed and one lacked enough matched texts to complete testing. All confirmed repairs were on GSM8K, with none on HotpotQA. During confirmation, target evidence was prepended and shams were appended, limiting the inference to a contrast involving both content and position. Search and confirmation together cost an estimated US$3.927; confirmation contributed 37.2%. The resulting record distinguishes candidates found during search from repairs supported by fresh control comparisons and makes the cost of that support explicit.

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Publication Details

Journal
Mathematical Modeling and Algorithm Application
Published
2026-11-02
DOI
https://doi.org/10.54097/qpnkcr67
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Validating Repair Claims after Evidence Restoration in Compressed Prompts

Yuejin Zhang, Ziye Yue
Mathematical Modeling and Algorithm Application
Topic Modeling
article

Validating Repair Claims after Evidence Restoration in Compressed Prompts

Yuejin Zhang, Ziye Yue
article en

Abstract

An answer that recovers after deleted evidence is restored does not, by itself, establish a reliable repair. The recovery may depend on sampling variation or on other changes to the prompt. We evaluate repair claims by fixing the restoration candidate and testing it against three distinct length-matched sham texts in fresh paired calls. An entry screen restricts search to questions that pass with the full context and fail after compression. With controlled deletion at a retention ratio of 0.5, protocol pass rates rose from 20.0% to 45.0% on 60 development items. On 100 held-out test items, they rose from 17.0% to 36.0%. Of 36 search candidates, 34 passed confirmation; one failed and one lacked enough matched texts to complete testing. All confirmed repairs were on GSM8K, with none on HotpotQA. During confirmation, target evidence was prepended and shams were appended, limiting the inference to a contrast involving both content and position. Search and confirmation together cost an estimated US$3.927; confirmation contributed 37.2%. The resulting record distinguishes candidates found during search from repairs supported by fresh control comparisons and makes the cost of that support explicit.

Mathematical Modeling and Algorithm ApplicationVol. 10(1)
Openalex Percentile: Top 16%
Topic Modeling
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