# Actual focused source check, 2026-10-15

This continuation revisits the existing coding-agent procurement question. It does not claim the upstream sources changed since yesterday.

- **S6, official SWE-Lancer README**, https://raw.githubusercontent.com/openai/preparedness/main/project/swelancer/README.md : opened with web.run, 193 returned lines. Docker Setup L20–53 and manager configuration L163–165 require the monolith image for manager tasks. L37 describes per-task image build time and size as publisher estimates, not our measurements. The report previously distinguished IC/Manager scoring but omitted the concrete setup difference. Preserve sources/S6.md and add the requirement to the existing Lancer paragraph. Do not infer that all images or costs are interchangeable. The GitHub connector could not obtain this file's SHA; the successful web read and stored source are the evidence, not a claimed pinned commit.
- **S10, official SWE-bench Pro V2 README**, https://raw.githubusercontent.com/scaleapi/SWE-bench_Pro-os/main/v2/README.md : opened with web.run, 108 returned lines. Locked protocol L64–67 specifies pristine-image replay and the in-sandbox timeout, with partial patches still captured and graded. The GitHub commits endpoint reports latest file commit 31a20c751b0bd56db6510ce7d4d3c269c5b7c72b (2026-09-22). Existing sources/S10.md is retained. Expand the report's 50-minute statement to explain partial-patch scoring; keeping timed-out attempts in the trial denominator is our procurement recommendation based on that protocol and the existing all-attempt cost principle.

Reuse: S1–S11 source text and IDs are preserved. Only S6/S10 reading notes and affected claims/requirements need updating. Other source text was not fetched again for these two additions. The existing report's historical date remains 2026-10-14; these are additional checks of already available source details, not a claim to have refreshed all news to 2026-10-15.

Reasoning: environment preparation, downloads/cache and clean-image replay are operational work as well as model inference. Recommend itemizing shared setup and per-task costs; no observed cost saving or runtime benchmark is claimed.