{
 "schema_version": 1,
 "generated": "2026-10-19",
 "id": "google-research-harness2--resume-interrupted-multi-task-refinement-run",
 "repo": "google-research/harness2",
 "repo_url": "https://github.com/google-research/harness2",
 "repo_slug": "google-research-harness2",
 "title": "Resume a long refinement run after it stops midway",
 "page_title": "How to resume a long refinement run after it stops midway with harness2",
 "summary": "Re-issue the same command to continue a multi-task run without redoing finished tasks.",
 "who": "Researcher running a larger JobBench or LAB sweep that was interrupted by a crash or quota limit",
 "problem": "Long model-backed runs cost money, and restarting from scratch after an interruption wastes both time and API spend.",
 "outcome": "A completed run that picked up where it stopped, with results kept under the same runs directory.",
 "steps": [
  "Note the exact command you used, including --bench, --k, --mode, any subset flags and seed.",
  "Re-run the identical harness2 command to resume.",
  "Use a new HARNESS2_RUNS_DIR only if you changed models or task selection.",
  "Run harness2-judge and harness2-report with the same flags once refinement finishes.",
  "Check each command's --help for extra options."
 ],
 "agent_prompt": "My google-research/harness2 run on <benchmark> stopped partway. Reconstruct the exact original command, re-run it to resume, and verify that previously finished tasks were not repeated by checking the runs/ folder.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "Resume only works when you repeat the same benchmark, executor, subset, --k, --mode and seed, and changing models or task selection requires a new HARNESS2_RUNS_DIR.",
 "category": "Research & Knowledge",
 "tags": [
  "Research & Knowledge"
 ],
 "lang": "python",
 "requirements": [
  "Linux, Python 3.12 and uv for installation",
  "No credentials needed for the local demo (harness2-demo)",
  "Model-backed runs need Node.js 20+, the Google Cloud CLI, a Vertex AI project and model IDs in .env, and gcloud application-default login",
  "LAB needs Pandoc and Poppler; WorkBuddy-Bench needs Docker",
  "Model-backed benchmark runs incur API costs"
 ],
 "risk": "dual-use",
 "seen_in_the_wild": false,
 "best": false,
 "score": 0.462,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/google-research-harness2/resume-interrupted-multi-task-refinement-run/",
 "md": "https://dedupedai.com/use-cases/google-research-harness2/resume-interrupted-multi-task-refinement-run/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly."
}