{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time 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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2610.21113",
  "title": "SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems",
  "summary": "SSR replaces passing the entire agent registry into the LLM orchestrator's prompt with deterministic, training-free lexical retrieval over Agent Card capability fields, using IDF weighting and an inverted index to score only a small candidate subset. It costs little in infrastructure since it requires no model training or embeddings infrastructure, but it adds an indexing component to maintain and its lexical matching can miss semantic relevance that embedding-based retrieval would capture. After adoption it can fail when agent capabilities are described with vocabulary disjoint from query terms, when the registry is small enough that full-catalog prompting was already tractable, or when synthetic-benchmark gains do not transfer to real heterogeneous agent descriptions.",
  "rationale": "It is deterministic, training-free, requires no GPU or cluster, and directly cuts prompt size, cost, and latency in multi-agent orchestration, making it immediately deployable under constrained infrastructure.",
  "technique": "IDF-weighted inverted index for agent retrieval",
  "family": "orquestracao_multiagente",
  "recommendation": "adotar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21113",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 14,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "SSR replaces passing the entire agent registry into the LLM orchestrator's prompt with deterministic, training-free lexical retrieval over Agent Card capability fields, using IDF weighting and an inverted index to score only a small candidate subset. It costs little in infrastructure since it requires no model training or embeddings infrastructure, but it adds an indexing component to maintain and its lexical matching can miss semantic relevance that embedding-based retrieval would capture. After adoption it can fail when agent capabilities are described with vocabulary disjoint from query terms, when the registry is small enough that full-catalog prompting was already tractable, or when synthetic-benchmark gains do not transfer to real heterogeneous agent descriptions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21113",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "At 1,000 agents, SSR reduces LLM input by 98.8% and orchestration latency by 70.9% relative to full-catalog orchestration; on MS MARCO the inverted index reduces explicitly scored passages by 96.8%.",
      "basis": "inferred",
      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.21113",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}