{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "plab-lead-clsr-mdia--reproduce-mdia-benchmark-tables",
 "repo": "Plab-Lead/CLSR_MDia",
 "repo_url": "https://github.com/Plab-Lead/CLSR_MDia",
 "repo_slug": "plab-lead-clsr-mdia",
 "title": "Reproduce the released MDia benchmark tables offline",
 "page_title": "How to reproduce the released MDia benchmark tables offline with CLSR MDia",
 "summary": "Verify the release package and regenerate the tables and figures from saved scalar records.",
 "who": "A reviewer, auditor, or researcher who needs to check the reported MDia accuracy and generated-token numbers without rerunning model inference.",
 "problem": "Published accuracy and token savings are hard to trust unless the numbers can be recomputed from the source records.",
 "outcome": "A fresh output directory with recomputed aggregates, paired intervals, frontiers, a VALIDATION.json file, and exported PNG and SVG figures.",
 "steps": [
  "Clone the repository and open a terminal in its folder.",
  "`python3 -m venv .venv-evidence`",
  "`source .venv-evidence/bin/activate`",
  "`python -m pip install -r requirements-evidence.lock.txt`",
  "`python scripts/verify_mdia_release.py`",
  "`python scripts/reproduce_mdia_release.py --output runs/mdia-results-20261005`"
 ],
 "agent_prompt": "In the CLSR_MDia repository, set up the .venv-evidence environment from requirements-evidence.lock.txt, run scripts/verify_mdia_release.py, then run scripts/reproduce_mdia_release.py with a fresh output directory. Report whether all checks pass by reading VALIDATION.json, and list the exported table and figure files.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "This command recomputes analyses from released scalar records and runs offline; it does not run new model inference, which needs the original benchmark inputs and providers.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python 3.10+ for the offline toy pipeline, or Python 3.11+ for the release reproduction",
  "A terminal and git for cloning the repository",
  "No API keys are needed for the toy pipeline or the offline release reproduction",
  "For new model inference, benchmark inputs from their original sources and provider access (see docs/extending.md)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/plab-lead-clsr-mdia/reproduce-mdia-benchmark-tables/",
 "md": "https://dedupedai.com/use-cases/plab-lead-clsr-mdia/reproduce-mdia-benchmark-tables/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.",
 "api": "https://dedupedai.com/api/v1/usecases/plab-lead-clsr-mdia--reproduce-mdia-benchmark-tables.json",
 "api_version": "v1"
}