{
  "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": "2609.20997",
  "title": "MOSAIC-SR: Transformer-Guided Symbolic Regression for Scientific Equation Recovery",
  "summary": "MOSAIC-SR replaces randomly initialized combinatorial search for equation recovery with a pretrained Transformer that proposes candidate sketches, which are then refined by scale-aware constant optimization and local symbolic repair. The cost is running the pretrained proposer plus several parallel local searches, so it is more expensive per query than pure neural generation and requires maintaining the search and optimization stack. It can fail when the true equation lies outside the Transformer's learned prior, when constants are numerically ill-conditioned, or when the pretrained model was trained on a different distribution of expressions than the target domain.",
  "rationale": "This is a scientific equation-recovery method, not an LLM efficiency or agent technique, so it is only relevant if the team specifically ships symbolic regression for scientific modeling.",
  "technique": "Transformer-guided multi-start symbolic regression",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.20997",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "Highest symbolic solution rate on SRSD-Feynman (with and without dummy variables) and six additional benchmarks; top-two predictive accuracy; advantage persists with irrelevant dummy inputs. No multiplicative factor is reported in the abstract.",
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      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}