{
  "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": "2609.17632",
  "title": "EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents",
  "summary": "The method replaces static hand-written system prompts governing tool use with a prompt-as-policy that a separate Policy Agent revises after each interval using decision traces and realized feedback, while the backbone LLM stays frozen. Cost is modest and infrastructure-compatible: extra LLM calls for trace review and policy rewriting, trace storage, and a more complex deployment loop with versioned prompts. Failure modes include overfitting the policy to recent market regimes or noisy backtests, reward hacking where the policy inflates simulated metrics without real edge, compounding prompt drift that degrades stability, and the risk that reported gains do not survive transaction costs, slippage, or out-of-sample live trading.",
  "rationale": "The technique runs on fixed API-scale models with no training, but its gains are validated only on trading benchmarks, so the reader should pilot the prompt-evolution loop on their own agent workload rather than adopt it outright.",
  "technique": "Self-evolving system-prompt policies for tool-use agents",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17632",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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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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  ],
  "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": "",
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}