SWE-Serve Benchmark Reveals Production Inference Gaps, Asserts Limits and Variability
A preprint study assessing a new benchmark for production inference tasks shows a sizable gap between local task completion and production correctness, with mixed implications for users and builders.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the abstract
- Authors
- Jennifer Williams, Dave Farris, Jeff Farris, Jiantao Jiao
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What the paper reports
SWE-Serve introduces a benchmark with 53 repository-grounded tasks for production inference engineering, assessed across 11 models and 31 configurations. The evaluation includes hidden tests, end-to-end serving checks where applicable, and calibrated performance gates, revealing gaps between local task success and production correctness.
Why it matters
The study highlights a measurable production correctness gap, suggesting future agents may improve local task handling but still face constraints in real deployment.
SWE-Serve is designed to benchmark agents across production inference engineering tasks, not just isolated optimizations. It emphasizes coordination across model support, runtime execution, and public APIs, which can differ from lab benchmarks and affect real-world reliability.
Across examples and tests, the benchmark shows a substantial gap between patches that pass internal checks and patches that survive production-like end-to-end tests, underscoring the complexity of moving from local task completion to production correctness.
What this does not tell us
Abstract-only scope; this is a preprint and results reflect an abstract-only evaluation without peer-reviewed validation.
Original sources · 1
Check the original paper for its authors, methods, version and access terms.
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