{
  "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": "2609.28563",
  "title": "SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference",
  "summary": "SpaFactor replaces per-gene independent regression and heavier graph-neural-network approaches for predicting spatial gene expression from H&E images with a residual MLP mapping multiscale morphology features into low-dimensional latent gene programs decoded through shared gene loadings. Its cost is modest: a lightweight MLP with multiscale context fusion that fits constrained GPU budgets and avoids graph computation or auxiliary supervision pipelines. It can fail when target cohorts differ from the five public benchmarks, when histology image encoders do not transfer across scanners or staining protocols, and when the low-rank program assumption cannot capture genes whose expression is not coordinated with the learned programs.",
  "rationale": "This is a domain-specific spatial-omics method with unquantified improvements, so it merits adoption only by teams working on histology-to-transcriptomics inference and passive observation by general AI/ML practitioners.",
  "technique": "Low-rank factorization for histology-to-transcriptomics prediction",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28563",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    },
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      "statement": "Best aggregate performance across five public spatial transcriptomics cohorts, with clearer gains for spatially variable genes; no multiplicative factor is reported in the abstract.",
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      "dimension": "operations",
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      "dimension": "compatibility",
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    },
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      "dimension": "security",
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    {
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  ],
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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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}