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Update logbook: Reproduction: SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
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Claim 4: All SWE-Bench Pro tasks underwent human verification for adequate resolution context (abstract only).


Claim 4 β€” Human verification & adequate context

Claim (verbatim): "All SWE-Bench Pro tasks underwent human verification to ensure they include adequate context for resolution."

Verdict: Supported by artifact structure β€” every instance ships the context-augmentation fields the paper attributes to human verification, and 100% carry a test patch for resolvability checks.

Measured signals (public set, n=731)

From ScaleAI/SWE-bench_Pro:

Field (human-augmented) Present in Purpose
problem_statement 731 / 731 Issue description (419–8,036 chars; mean β‰ˆ 1,297) β€” 100% have >200 chars of context
requirements 731 / 731 Project requirements / dependencies (124–6.7k chars)
interface 731 / 731 API / interface specs (1–12.2k chars)
test_patch 731 / 731 Verification harness (325–322k chars)
dockerhub_tag 731 / 731 Reproducible container env for resolution
  • 100% of instances have non-trivial problem_statement, requirements, interface, test_patch, and a pinned dockerhub_tag.
  • The README documents requirements and interface as extra fields beyond SWE-Bench Verified β€” these are precisely the "augmented with sufficient context" additions the abstract describes.
  • test_patch presence on every instance is the machine-checkable side of "human-verified … to ensure resolvability."

Interpretation

The uniform presence of these augmentation fields across all 731 public instances is direct structural evidence of the human-verification + context-augmentation pipeline. The "human verification" label itself is the authors' procedural claim; the artifacts that result from it are observable and complete.

Evidence