{
  "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": "2610.03174",
  "title": "FinNextAssist: Towards Professional Financial Deep Research Assistant",
  "summary": "FinNextAssist replaces monolithic deep research agents with a four-stage pipeline (Task Planner, Evidence Compiler, Reasoning Engine, Report Assembler) plus specialized sub-agents for financial tables and heterogeneous data. The cost is orchestration complexity, dependence on authoritative financial data subscriptions, and added latency from multi-stage iterative retrieval, though it runs on third-party LLM APIs and requires no local GPU. It can fail where sub-task decomposition misroutes work, where upstream data sources are incomplete or license-restricted, and where benchmark gains on FinDeepResearch and related suites do not transfer to the reader's specific markets, languages, or report formats.",
  "rationale": "The architecture is implementable with API-only infrastructure and a small team, but the abstract reports only qualitative superiority without quantified margins, so adoption should follow workload-specific validation against a simpler single-agent baseline.",
  "technique": "Staged multi-agent deep research framework with domain sub-agents",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03174",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.03174",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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    },
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      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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  ],
  "risks": [],
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  "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": "",
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}