{
  "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": "2609.26602",
  "title": "A Configurable Heuristic for the MLCS Problem",
  "summary": "ARP replaces exact dynamic-programming or DAG-pruning MLCS solvers and tuned hyper-heuristics with a configurable add-replace-prioritize heuristic for sequence analysis. It costs nothing in GPU or API terms, but requires implementing a domain-specific combinatorial algorithm and tuning its quality-runtime tradeoff parameter. It can fail to deliver exact solutions, since as a heuristic it offers no optimality guarantee and its advantage is demonstrated only on synthetic and biological sequence benchmarks.",
  "rationale": "This is a classical algorithms paper on NP-hard sequence analysis with no connection to LLM inference, training, agents, or retrieval workloads that the reader operates.",
  "technique": "Configurable heuristic for Multiple Longest Common Subsequence",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26602",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ARP replaces exact dynamic-programming or DAG-pruning MLCS solvers and tuned hyper-heuristics with a configurable add-replace-prioritize heuristic for sequence analysis. It costs nothing in GPU or API terms, but requires implementing a domain-specific combinatorial algorithm and tuning its quality-runtime tradeoff parameter. It can fail to deliver exact solutions, since as a heuristic it offers no optimality guarantee and its advantage is demonstrated only on synthetic and biological sequence benchmarks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26602",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Aggressive configuration matches UB-HH solution quality while running 1.1x-1.7x faster; fastest configuration finds significantly longer common subsequences than the BNMAS heuristic.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26602",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}