{
  "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": "2609.25254",
  "title": "The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis",
  "summary": "The system replaces hand-written neuroimaging analysis scripts (quality control, modeling, visualization for fNIRS data) with a natural-language agent that calls a domain-specific toolset through an LLM. It costs API inference or a locally served LLM plus the engineering of a curated tool wrapper and a custom benchmarking suite, and the paper provides no quantified accuracy, latency, or cost figures in the abstract. It can fail through incorrect tool selection or parameter specification by the agent, hallucinated quality-control judgments on safety-relevant scientific data, and poor generalization beyond the demonstrated fNIRS tasks, since fMRI support is only a stated future extension.",
  "rationale": "This is a domain-specific application of standard LLM tool-calling to neuroimaging pipelines, so unless the reader works on neuroimaging analysis it offers no production technique to adopt, and its unquantified evaluation gives nothing transferable to validate.",
  "technique": "LLM tool-use agent for neuroimaging analysis",
  "family": "uso_de_ferramenta",
  "recommendation": "nao_aplica",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.25254",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations": [],
  "equations_status": "not_fetched",
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}