{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2610.09335",
  "title": "SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models",
  "summary": "Replaces reactive explicit-map search policies with a recurrent state-space world model that decodes a task-aware spatial value layer from BEV exploration and obstacle memory, enabling planning via imagined rollouts without a separate scalar critic. Costs include a staged training pipeline (world-model learning, expert imitation, imagination refinement), a simulation environment, and added inference compute for rollouts, though it avoids image-generating world-model latency. It can fail because absolute success remains low (23.8%), the approach is validated only on one UAV benchmark, and transfer to real flight demands sim-to-real adaptation beyond an ML engineer's typical scope.",
  "rationale": "The method requires simulator-based world-model training and embodied RL infrastructure that exceed a constrained production setup, and its modest absolute success makes it research to monitor rather than deploy. ",
  "technique": "Value-grounded spatial world model for UAV search",
  "family": "planejamento_e_decomposicao",
  "recommendation": "observar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.09335",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 216,
  "citations": 0,
  "claims": [
    {
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