{
  "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": "2609.17984",
  "title": "TuiML: Machine Learning for AI Agents",
  "summary": "TuiML replaces agents recalling scikit-learn/Weka APIs from memory with a library whose components expose machine-readable metadata and parameter schemas, so agents can search, inspect, and compose validated workflows, with one specification layer driving MCP, a Python API, a CLI, and local serving. The cost is adopting a new native algorithm stack in place of battle-tested scikit-learn implementations, plus the integration and maintenance burden of an early-stage open-source project whose long-term support is unproven. Failures after adoption can include algorithm coverage or correctness gaps relative to scikit-learn, community and maintenance risk, and the fact that the claimed benefits for agent reliability (fewer runtime errors, preserved state) are asserted rather than measured.",
  "rationale": "The approach directly addresses a real agent-engineering failure mode (unvalidated API recall and lost session state) and runs locally on constrained infrastructure, but its value over simply wrapping scikit-learn with an MCP server needs validation on the reader's own agent workloads.",
  "technique": "Agent-first ML library with self-describing components",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17984",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TuiML replaces agents recalling scikit-learn/Weka APIs from memory with a library whose components expose machine-readable metadata and parameter schemas, so agents can search, inspect, and compose validated workflows, with one specification layer driving MCP, a Python API, a CLI, and local serving. The cost is adopting a new native algorithm stack in place of battle-tested scikit-learn implementations, plus the integration and maintenance burden of an early-stage open-source project whose long-term support is unproven. Failures after adoption can include algorithm coverage or correctness gaps relative to scikit-learn, community and maintenance risk, and the fact that the claimed benefits for agent reliability (fewer runtime errors, preserved state) are asserted rather than measured.",
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      "dimension": "operations",
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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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      "source_page": null,
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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}