analiza aiqbench
ViBench 2.0 addresses the critical reliability gap in agentic software development by evaluating models on long-horizon, full-stack product construction rather than isolated coding tasks. The benchmark reveals that while top models can pass individual test plans with high accuracy, they still struggle to deliver fully functional products consistently, highlighting a significant step toward practical autonomous engineering. This work provides essential metrics for assessing the maturity of AI agents in real-world software building scenarios.
ViBench 2.0: A Benchmark for Long-Horizon Software Building
arXiv:2611.05620v1 Announce Type: new Abstract: On Replit, our agentic engineering platform, users now build entire products without writing code, through successive feature requests to an agent. Yet existing coding benchmarks evaluate single, fully specified tasks, and many are near saturation. We introduce ViBench 2.0, a benchmark for long-horizon software building that we use to select our platform's models. Its 17 full-stack web products, modeled on widely used software, grow over 241 feature requests and are judged only when finished, as a user would judge them: a browser agent runs 544 test plans, and a build passes only if every plan does, under a grading protocol validated to keep this strict score reliable. Evaluating nine models, we find a reliability gap: the three strongest pass up to 98% of test plans yet deliver a fully working product in at most 65% of builds, and no open-weight model delivers one. The gap is closing fast: across three releases of one model family, pass@1 on the public products rises from 3% to 53% while the cost per build falls by more than half. We release eight products with the harness and grader, hold out nine, and report lessons from production use.
Title:ViBench 2.0: A Benchmark for Long-Horizon Software Building
Abstract:On Replit, our agentic engineering platform, users now build entire products without writing code, through successive feature requests to an agent. Yet existing coding benchmarks evaluate single, fully specified tasks, and many are near saturation. We introduce ViBench 2.0, a benchmark for long-horizon software building that we use to select our platform's models. Its 17 full-stack web products, modeled on widely used software, grow over 241 feature requests and are judged only when finished, as a user would judge them: a browser agent runs 544 test plans, and a build passes only if every plan does, under a grading protocol validated to keep this strict score reliable. Evaluating nine models, we find a reliability gap: the three strongest pass up to 98% of test plans yet deliver a fully working product in at most 65% of builds, and no open-weight model delivers one. The gap is closing fast: across three releases of one model family, pass@1 on the public products rises from 3% to 53% while the cost per build falls by more than half. We release eight products with the harness and grader, hold out nine, and report lessons from production use.
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
I.2.2; D.2.5; D.2.8
arXiv:2611.05620 [cs.AI]
(or arXiv:2611.05620v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.05620 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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