Content Quality: Clear, well-structured News-category piece (Overview / What We Know / What We Don't Know / Analysis). Technical concepts (SFT, trajectory-level vs segment-level selection, the two-stage pipeline) are explained accessibly without oversimplifying. Word count (758) is within the 400-1200 News range.
Source Verification: Both cited sources were read from the committed local snapshots (sources/2026-08/new-swe-prime-method-trains-ai-coding-agents-on-just-10-of-data-lifting-bug-fix-accuracy-up-to-242/): source-0.html.gz (arxiv.org/abs/2608.27449, the abstract page, sha256 verified 57d9d2f1...) and source-1.html.gz (arxiv.org/html/2608.27449v1, the full HTML paper, sha256 verified f4577a19...). Both fetches returned HTTP 200 with no archive_fallback and no suspicious_patterns entries. Title, author list, submission date (27 Aug 2026), and subject classification (cs.SE) on source-0 all match the article. Author affiliations (Sun Yat-sen University, Huawei Cloud Computing Technologies Co., Chongqing University) confirmed verbatim in source-1's author block.
Factual Accuracy: Verified against source-1 (full paper text, extracted from the gzipped HTML and cross-checked table-by-table): the 'task success alone does not guarantee high-quality supervision' quote appears verbatim in the paper's introduction (not the abstract, which omits 'alone' -- the article correctly attributes this quote to the '[full paper]' link, so no misattribution). The Stage 1 and Stage 2 pipeline-description quotes are verbatim. The 67,074-trajectory and 32,161-resolved-trajectory figures, and the Nebius/SWE-rebench OpenHands Trajectories dataset attribution, are verbatim. The GLM-4.7-Flash SWE-Bench Verified figures (raw 40.4, full-data SFT 41.4, SWE-Prime 10% 51.4) match the paper's results table exactly, and the derived 24.2% relative gain ((51.4-41.4)/41.4) and the 12.2% figure (Qwen3-30B-A3B-Instruct-2507 on SWE-Bench Pro, full-data 16.83 to SWE-Prime 18.88) both reconcile with the abstract's 'up to 12.2% and 24.2%, respectively' claim for Pro and Verified in that order. The 'What We Don't Know' claims (no code/artifact release mentioned, no dedicated limitations section) were confirmed by keyword search across the full extracted paper text -- no 'github', 'repository', 'release', or 'limitation' hits of that kind. One factual error was found and is documented in `findings`: the claim that Random-10% scored below the raw model 'in two of the six comparisons' is wrong -- reconstructing the full results table shows it happened in four of six comparisons (both benchmarks for both GLM-4.7-Flash and Qwen3-Coder-30B-A3B-Instruct). This is a subordinate claim in the body (not the headline, summary, or lead), and the surrounding sentence's main point -- that Random-10% underperformed the full-data baseline across all six comparisons -- is independently verified correct. This is being handled via a public corrections record rather than REJECT because it is a single, isolable numerical miscount that a reader can be honestly informed of without gutting the article's thesis.
Overall Assessment: Substantively accurate, well-sourced, and appropriately scoped preprint coverage with one recoverable numerical miscount in a subordinate claim. APPROVE_WITH_CORRECTIONS: publish with a public corrections record.