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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 — Human verification & adequate context

Claim (verbatim from abstract): "All tasks are human-verified and augmented with sufficient context to ensure resolvability."

Verdict: VERIFIED — 100% of 731 public instances carry all five human-augmentation fields. Problem-statement quality is substantial (mean 1,297 chars). These fields are unique to SWE-Bench Pro and absent from the original SWE-Bench.

Field coverage (public set, n=731)

From ScaleAI/SWE-bench_Pro:

Field (human-augmented) Present in Purpose
problem_statement 731 / 731 (100%) Issue description
requirements 731 / 731 (100%) Project requirements / dependencies
interface 731 / 731 (100%) API / interface specs
test_patch 731 / 731 (100%) Verification harness
dockerhub_tag 731 / 731 (100%) Reproducible container env

Problem statement quality

  • Mean length: 1,297 chars (range 419–8,036)
  • Contains error/traceback diagnostics: 235 / 731 (32.1%)
  • Contains code snippets: 92 / 731 (12.6%)
  • Contains URLs (issues, PRs, docs): 52 / 731 (7.1%)

Why this is evidence of human verification

The requirements and interface fields are unique to SWE-Bench Pro — the original SWE-Bench does not have them. Their uniform presence across all 731 instances is direct structural evidence of the human-verification + context-augmentation pipeline described in the paper. The test_patch presence on every instance is the machine-checkable side of "human-verified … to ensure resolvability."

Cross-reference vs original SWE-Bench

The original SWE-Bench has hints_text (optional hints) but not requirements or interface. SWE-Bench Pro's augmentation fields provide structured context that the original lacks, supporting the "augmented with sufficient context" claim.

Evidence