{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "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": "Correct completed actions, invalid calls, retries, latency, and cost per completed 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": "2610.18522",
  "title": "Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale",
  "summary": "Explicit preselection of relevant skills before agent execution replaces implicit routing, in which the agent scans full skill metadata and decides what to load during the task. It costs an additional selection step with its own latency and coverage trade-offs, and requires a mechanism (retriever or selector model) to pick candidate skills from the library. It can fail when the preselector misses the golden skill, since the agent then has no opportunity to recover it, and gains shrink when libraries are small enough that implicit routing has not yet collapsed.",
  "rationale": "Skill routing collapse appears well below 1,000 skills and preselection is a cheap, infrastructure-light mitigation that any small team can implement with a retriever or a prompted selection pass.",
  "technique": "Explicit skill preselection over implicit routing",
  "family": "uso_de_ferramenta",
  "recommendation": "adotar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18522",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Explicit preselection of relevant skills before agent execution replaces implicit routing, in which the agent scans full skill metadata and decides what to load during the task. It costs an additional selection step with its own latency and coverage trade-offs, and requires a mechanism (retriever or selector model) to pick candidate skills from the library. It can fail when the preselector misses the golden skill, since the agent then has no opportunity to recover it, and gains shrink when libraries are small enough that implicit routing has not yet collapsed.",
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      "source_url": "https://arxiv.org/abs/2610.18522",
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      "statement": "With 1,000 irrelevant skills in the library, implicit routing loaded-skill recall falls to 27.4% while explicit preselection from the same library retains 73.9% recall (about 2.7x higher recall).",
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    }
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
  "core_kind": "",
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}