Read the paper.
Inspect the mechanism.

ScaleBox: evaluation infrastructure

The ACL 2026 system-demonstration paper by Jiasheng Zheng and coauthors studies executable feedback for language-model training and evaluation. Its architecture includes special-judge support, test-case parallelism and distributed coordination.

Open the published architecture figure or inspect the interfaces below.

The practical question for an evaluation team is whether the environment and judge match the benchmark contract. Reported benchmark speedups depend on workload and hardware; we have not reproduced those experiments.

Watch the authors’ demonstration

The paper links this ScaleBox demonstration in its first-page footnote. It shows the project rather than an implementation built by Proof Field.

Video: Winhana / ScaleBox authors, linked by Zheng et al. Embedded through YouTube’s player; no video file is copied. Open the original for available captions and playback options.

Look at what the interface exposes

An evaluation interface needs enough detail to distinguish a failed program from an execution problem. Inspect the task, configured judge, limits and returned status before interpreting an aggregate result.

Repository review asks another question

OpenAI’s code verification article discusses reviewers intended for human development workflows. The authors emphasize finding issues useful enough for a developer to investigate. A training-time reward signal can tolerate a different error profile.

For your team, inspect a candidate finding against the specification, callers and actual runtime behavior. Track whether findings lead to accepted fixes before allowing automated review output to block merges.

Source register

Source review: 24 Oct 2026. ScaleBox references use the ACL 2026 published paper (DOI 10.18653/v1/2026.acl-demo.30); the dashboard screenshots also appear in arXiv:2604.27467v1, dated 30 April 2026. This review date stays fixed until an editorial source review.