{
  "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": "2610.12336",
  "title": "asdex: Automatic Sparse Differentiation in JAX",
  "summary": "asdex replaces dense jax.jacobian and jax.hessian calls, which require one AD pass per column or row, with sparse drop-in equivalents that detect the sparsity pattern, color a graph, and run one compressed AD pass per color before decompressing. The cost is added preprocessing (sparsity detection and coloring), dependency on a new library, and per-call overhead that may exceed the savings for small or dense problems. It fails when the true Jacobian is dense or nearly dense, when sparsity varies with inputs in ways the input-agnostic detection misses, or when the function is not JAX-compatible, and incorrect pattern detection would silently drop nonzero derivative entries.",
  "rationale": "For JAX workloads that compute large sparse Jacobians or Hessians, this is a low-integration-cost drop-in that can reduce AD passes from dimension-scale to color-scale, but the gain must be validated against the actual sparsity structure of the reader's functions.",
  "technique": "Automatic sparse differentiation via graph coloring",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12336",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 53,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "asdex replaces dense jax.jacobian and jax.hessian calls, which require one AD pass per column or row, with sparse drop-in equivalents that detect the sparsity pattern, color a graph, and run one compressed AD pass per color before decompressing. The cost is added preprocessing (sparsity detection and coloring), dependency on a new library, and per-call overhead that may exceed the savings for small or dense problems. It fails when the true Jacobian is dense or nearly dense, when sparsity varies with inputs in ways the input-agnostic detection misses, or when the function is not JAX-compatible, and incorrect pattern detection would silently drop nonzero derivative entries.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.12336",
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    },
    {
      "claim_id": "claim-02",
      "statement": "AD passes scale with the number of graph colors rather than problem dimension; a banded Jacobian with b bands requires only b passes regardless of matrix size (abstract states this structurally, without a benchmark factor).",
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      "source_url": "https://arxiv.org/abs/2610.12336",
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    }
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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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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}