ExplainBench: Evaluating Code Explanations from Agents
Large Language Model (LLM) agents have seen rapid adoption in software engineering. As agents take a greater role in the actual generation of code, they are making larger changes, spanning tens to hundreds of lines. This makes manual review of agent results increasingly infeasible, leading developers to turn to explanations to understand enacted changes. Despite this, there are no benchmarks that evaluate the trustworthiness of agent-generated explanations. To bridge this gap, we propose Explain
Record details
Published: 28 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy
Retrieved: 5 August 2026
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ethics.ai (28 July 2026), “ExplainBench: Evaluating Code Explanations from Agents,” evidence record 16220, https://ethics.ai/record/16220 (originally published by HuggingFace Daily Papers).
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