Benchmark RadarDeployBench
DeployBench evaluates LLM agents on 51 research-artifact deployment tasks across AI/ML, systems, and scientific computing. Tasks require setting up environments with multi-language toolchains and system-level dependencies, verified by hidden pipelines that execute the paper's experiments and check outputs.
- Released
- 2026-06-03
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
Current benchmarks overlook the complexity of deploying research artifacts, which is a bottleneck for reproducibility. DeployBench provides a standardized testbed with hidden verification, enabling comparison of agents on a realistic deployment task and highlighting failures in completion-judgment.
Motivation
LLM agents have made rapid progress on software engineering and ML research tasks, but these advances often assume access to a working runnable environment.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.