{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "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": "2610.29443",
  "title": "DynaWeave: Learning to Adapt through Dynamic Scenario Synthesis",
  "summary": "Replaces static task environments with dynamically synthesized scenarios where exogenous events (requirement changes, state shifts, external information updates) activate during agent interaction via DAG-based dependency graphs and an execution engine. Costs include building the synthesis framework, generating and verifying 2,095 trajectories across 36 environments, and supervised fine-tuning of Qwen3.5 models, requiring non-trivial engineering and compute. Can fail if synthesized events do not match production event distributions, if the dynamic verifier introduces evaluation errors, or if fine-tuned models overfit to synthetic dynamics rather than genuine adaptation.",
  "rationale": "The method requires implementing a full scenario synthesis framework, DAG execution engine, and dynamic verifier plus fine-tuning infrastructure, which exceeds typical constrained budgets without evidence the approach transfers to the reader's specific domain and models.",
  "technique": "Dynamic scenario synthesis for agent training",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.29443",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces static task environments with dynamically synthesized scenarios where exogenous events (requirement changes, state shifts, external information updates) activate during agent interaction via DAG-based dependency graphs and an execution engine. Costs include building the synthesis framework, generating and verifying 2,095 trajectories across 36 environments, and supervised fine-tuning of Qwen3.5 models, requiring non-trivial engineering and compute. Can fail if synthesized events do not match production event distributions, if the dynamic verifier introduces evaluation errors, or if fine-tuned models overfit to synthetic dynamics rather than genuine adaptation.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.29443",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Increases task completion rate on CostBench by 67.34 percentage points; consistent improvements on Gaia2 and ToolMaze across Qwen3.5 model sizes, exceeding Gemini-3.1-Pro-Preview on ToolMaze.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.29443",
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    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "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": ""
}