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Evidence record 16935 · automatically gathered

RepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists

The integration of Large Language Models (LLMs) into software engineering has shifted the focus from function-level generation to repository-scale assistance. However, existing benchmarks largely rely on bug reports from GitHub Issues, which often allow models to bypass genuine understanding via pattern matching on error logs. This misalignment under-measures Edit Bias, which refers to premature generation, where models prematurely propose code modifications instead of understanding the existing

Record details

Published: 5 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 6 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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arXiv cs.CY · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Neuro-symbolic pedagogical alignment (NSPA) for long-horizon classroom discourse analysis: Mitigating dialect bias via counterfactual preference optimization

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How to cite this record

ethics.ai (5 August 2026), “RepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists,” evidence record 16935, https://ethics.ai/record/16935 (originally published by arXiv cs.AI).

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