REAP curates production-derived tasks for coding-agent benchmarks

Paper focus

“REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage,” listed on arXiv as arXiv:2604.01527, addresses a central problem in coding-agent evaluation: how to build benchmarks that reflect real developer work while still producing reliable, executable signals. The arXiv abstract describes REAP as an automated curation pipeline for production-derived AI coding-agent benchmarks. A SciRate page for arXiv:2604.01527v3 summarizes the associated Harvest benchmark as a setting in which each task gives a coding agent a real developer prompt and verifies the resulting code change against production fail-to-pass tests.

The paper’s focus is benchmark construction rather than a new coding agent. Based on the supplied arXiv and SciRate excerpts, REAP starts from interactive production usage and applies automated filtering and validation before tasks are accepted as benchmark items. This is different from benchmark approaches based mainly on hand-written toy tasks or static problem sets: the sources frame REAP as a way to curate tasks derived from real coding-agent interactions and then make those tasks suitable for repeatable evaluation.

Method or system

The arXiv abstract and PDF excerpts identify three main components in REAP: task classification, agentic test-relevance validation, and multi-run stability checks. The PDF excerpt specifically describes LLM-based task classification, indicating that language models are used to categorize or filter candidate coding tasks during curation. The same excerpt describes agentic test-relevance validation, in which the system checks whether the tests attached to a candidate task are relevant to the code change an agent is expected to make. REAP also includes multi-run stability checks, which are used to assess whether benchmark outcomes remain consistent across repeated executions.

The SciRate excerpt adds context about Harvest, the benchmark associated with REAP. It describes Harvest tasks as pairing a real developer prompt with production fail-to-pass tests. In this setup, an agent receives a prompt based on developer usage, proposes a code change, and that change is evaluated using tests that should move from failing to passing if the task is solved. This makes the benchmark executable: success is tied to runnable software behavior rather than only to textual similarity or human preference.

The supplied sources do not include enough detail to report the full task count, repository coverage, programming-language distribution, acceptance rates, or model-by-model performance results. They do, however, consistently describe REAP as an automated curation pipeline and Harvest as a benchmark format based on real prompts and production tests.

Why it matters

Coding-agent benchmarks are most useful when they evaluate tasks similar to the work developers actually ask agents to do and when their results can be reproduced. The supplied arXiv excerpts indicate that REAP targets both goals: it draws from interactive production usage, and it applies validation and repeated-run stability checks before including tasks in a benchmark. Production-derived prompts can make tasks closer to real coding workflows, while fail-to-pass tests provide an executable criterion for whether a generated code change solved the issue.

The stability-check component is especially relevant for agentic systems. Coding agents may vary across runs because of sampling, tool use, environment behavior, or different intermediate actions. If a benchmark task produces inconsistent outcomes, it may be less useful for comparing systems. According to the arXiv PDF excerpt, REAP uses multi-run stability checks to improve the trustworthiness of executable benchmark signals.

For researchers and engineering teams evaluating coding agents, the broader implication is that benchmark construction may need to be an ongoing curation process rather than a one-time dataset assembly step. The sources portray REAP as one example of that process: classify candidate tasks, validate whether their tests are relevant, and check stability across runs before using the tasks for evaluation.

Limitations

The provided sources support the high-level description of REAP, but they do not provide enough information to assess several important questions. The excerpts name M. Paltenghi through the SciRate author search reference, but they do not provide a complete author list or institutional affiliations. They also do not specify the number of tasks curated, the diversity of repositories or programming languages, the exact thresholds used in stability checks, or the degree to which Harvest generalizes beyond the production setting from which its tasks were derived.

There are also limitations inherent to production-derived benchmark construction. Tasks drawn from one production environment may be realistic for that environment without being representative of all software-engineering work. The sources describe REAP’s classification, validation, and stability checks, but the supplied excerpts do not establish whether those checks eliminate benchmark bias, data leakage, or environment-specific brittleness. As with any benchmark based on executable tests, passing fail-to-pass tests may also be an incomplete proxy for code quality if the tests do not cover all relevant behavior.

Hero image prompt: Editorial illustration of an AI coding benchmark assembly line, with abstract code blocks moving through classification, validation, and stability-check stations, ending in a clean test-harness dashboard; modern research-magazine style, no logos, no readable text, no real user-interface screenshots.

Hero image alt text: Abstract illustration of coding tasks being filtered through automated benchmark validation stages before becoming executable tests.