{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per 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": "2609.19244",
  "title": "Characterizing Web Search by Conversational LLM Agents: From Search Decisions and Strategies to Results and Responses",
  "summary": "This is a measurement study, not a deployable method; it replaces no existing technique but characterizes how four commercial platforms decide to invoke web search, formulate queries, select domains, and cite sources, whereas the reader's equivalent practice is ad hoc prompt-level tuning of retrieval-augmented pipelines. Adopting its findings costs only engineering attention, but translating them into practice means building citation-verification and search-invocation auditing into a RAG pipeline, which adds latency and evaluation complexity. The findings can fail to transfer because they are platform- and model-version-specific: search behavior varies substantially across ChatGPT, Claude, Grok, and DeepSeek, so conclusions may not hold for the reader's chosen API or self-hosted stack.",
  "rationale": "The paper offers diagnostic insights rather than an adoptable technique, and its platform-specific findings are useful context for designing grounding and attribution checks but require validation against the reader's own models before changing production behavior.",
  "technique": "Empirical characterization of agentic web search",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19244",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is a measurement study, not a deployable method; it replaces no existing technique but characterizes how four commercial platforms decide to invoke web search, formulate queries, select domains, and cite sources, whereas the reader's equivalent practice is ad hoc prompt-level tuning of retrieval-augmented pipelines. Adopting its findings costs only engineering attention, but translating them into practice means building citation-verification and search-invocation auditing into a RAG pipeline, which adds latency and evaluation complexity. The findings can fail to transfer because they are platform- and model-version-specific: search behavior varies substantially across ChatGPT, Claude, Grok, and DeepSeek, so conclusions may not hold for the reader's chosen API or self-hosted stack.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19244",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
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      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "",
      "limitation": "",
      "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": ""
}