Daily Papers

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 ExplainBench, a benchmark to automatically evaluate explanations from coding agents. ExplainBench is based on the intuition that informative explanations should enable an LLM to correctly answer questions, allowing quantitative comparison of explanation quality between agents. With this observation, we construct a suite of questions that evaluates whether explanations accurately describe (1) the intended behavior of buggy code and (2) the effect of applying the agent patch itself. Experiments first reveal that explanation quality is a distinct axis of agent evaluation: ExplainBench ranks agents differently from the widely-used SWE-bench Verified benchmark. A deeper breakdown of explanation quality in agents shows frequent problems in explanations, such that explanations often claim that a patch is correct when it is not. Based on this insight, we implement and evaluate an explanation audit agent which runs additional tests to validate and refine explanations. This agent improved the explanations of all evaluated agents, demonstrating agent explanations can be automatically made more trustworthy.

As machine learning systems are increasingly deployed in high-stakes domains such as criminal justice, finance, and healthcare, the demand for interpretable and trustworthy models has intensified. Despite the proliferation of local explanation techniques, including SHAP, LIME, and counterfactual methods, there exists no standardized, reproducible framework for their comparative evaluation, particularly in fairness-sensitive settings. We introduce ExplainBench, an open-source benchmarking suite for systematic evaluation of local model explanations across ethically consequential datasets. ExplainBench provides unified wrappers for popular explanation algorithms, integrates end-to-end pipelines for model training and explanation generation, and supports evaluation via fidelity, sparsity, and robustness metrics. The framework includes a Streamlit-based graphical interface for interactive exploration and is packaged as a Python module for seamless integration into research workflows. We demonstrate ExplainBench on datasets commonly used in fairness research, such as COMPAS, UCI Adult Income, and LendingClub, and showcase how different explanation methods behave under a shared experimental protocol. By enabling reproducible, comparative analysis of local explanations, ExplainBench advances the methodological foundations of interpretable machine learning and facilitates accountability in real-world AI systems.