Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents

• URL: http://arxiv.org/abs/2607.24882v1
• Date: Mon, 27 Jul 2026 09:39:09 GMT
• Title: Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents
• Abstract summary: Agent Retrieval Bench is a file-level benchmark for this upstream retrieval problem.<n>The benchmark covers four positive-retrieval tasks: code2test, comment2context, trace2code, and edit2ripple.<n>The corpus includes 308 base-commit snapshots, 392,000 files, and 7.9 million chunks.
• Score: 8.81342573156581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern coding agents are usually evaluated by whether they eventually produce a correct patch, but patch generation depends on an earlier context-acquisition stage: finding the repository files needed for the task. We introduce Agent Retrieval Bench, a file-level benchmark for this upstream retrieval problem. Samples are built from real coding-workflow signals and evaluated against frozen base-commit repositories, with relevance defined by what an agent needs next rather than direct query-file semantic similarity. The benchmark covers four positive-retrieval tasks: code2test, comment2context, trace2code, and edit2ripple; a fifth subset evaluates selective retrieval using natural evidence-backed no-gold cases and counterfactual wrong-repository controls. Agent Retrieval Bench contains 427 samples across 25 repositories: 345 positive examples, 50 natural no-gold examples, and 32 counterfactual controls. The corpus includes 308 base-commit snapshots, 392,000 files, and 7.9 million chunks. We evaluate lexical retrieval, RepoMap, open-source embeddings, selective abstention, and logged agent context selection. No single retrieval family dominates: Qwen3-Embedding-4B has the best sample-weighted MRR on positive samples, Qwen3-Embedding-8B the best Recall@20, and RepoMap the best budgeted context yield at 8K tokens, with task-level winners differing substantially. Selective thresholds calibrated with counterfactual controls do not improve selective success on natural no-gold cases, revealing a calibration gap. Logged trajectories also miss every gold file on 27-35 percent of samples. A controlled seed-intervention pilot finds that retrieval-derived initial context yields higher file F1 with less post-seed exploration than random non-gold context, while oracle gold context shows substantial remaining headroom.

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