{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.28558",
  "title": "CFD Correction of Open Tip Clearance Flow in a Compressor Cascade Using VAE Latent Space Adaptation",
  "summary": "The method replaces modifying the RANS solver or constructing artificial high-resolution experimental labels by freezing a VAE trained on 166 CFD loss fields and fitting only a low-rank latent adapter from 12 paired CFD-experiment conditions via an observation operator. Cost is moderate: one VAE training pass plus a small adapter fit, all feasible on a single GPU, but it requires an existing parametric CFD dataset and paired sparse measurements. It can fail outside the sampled parameter space, the 12-fold cross-validation on 12 conditions is weak evidence of generalization, and correctness is only enforced at sparse measurement windows, so unobserved field regions may remain physically wrong.",
  "rationale": "This is a scientific-computing surrogate correction method for compressor aerodynamics, not an LLM inference, training, or agent technique, so it falls outside the reader's production adoption scope despite being computationally inexpensive.",
  "technique": "VAE latent-space adaptation for CFD field correction",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28558",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
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      "claim_id": "claim-01",
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      "statement": "In 12-fold cross-validation, mean absolute error against experimental observations drops from 0.1335 to 0.0473 (about 2.8x), RMSE from 0.1717 to 0.0621, and relative L2 error from 0.5108 to 0.1871.",
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    },
    {
      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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