{
  "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": "2610.11004",
  "title": "Reconstruction of Multiscale Plasma Dynamics Across Operating Regimes",
  "summary": "ReMAIN replaces SHRED's fully connected decoder with a U-Net whose feature hierarchy is modulated by the recurrent state via feature-wise linear modulation, adding parameter conditioning on operating regime. The cost is a more complex, spatially structured architecture requiring dense field training data and sensor placement engineering, with no latency, memory, or cost figures reported. It can fail outside its training distribution, at untested sensor configurations, and the abstract provides no quantitative error reductions to support sizing decisions.",
  "rationale": "This is a scientific machine learning method for sparse-sensor reconstruction of plasma and PDE dynamics, a domain unrelated to the reader's LLM production infrastructure decisions, and it contains no quantified efficiency or quality factor to evaluate.",
  "technique": "FiLM-conditioned U-Net decoder for SHRED reconstruction",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11004",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ReMAIN replaces SHRED's fully connected decoder with a U-Net whose feature hierarchy is modulated by the recurrent state via feature-wise linear modulation, adding parameter conditioning on operating regime. The cost is a more complex, spatially structured architecture requiring dense field training data and sensor placement engineering, with no latency, memory, or cost figures reported. It can fail outside its training distribution, at untested sensor configurations, and the abstract provides no quantitative error reductions to support sizing decisions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.11004",
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      "source_excerpt": ""
    }
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      "dimension": "compatibility",
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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}