{
  "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": "2610.29139",
  "title": "Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model",
  "summary": "PAWM replaces per-step next-observation world models with a selectively invoked model that predicts impending multi-step failures and steers a black-box LLM agent away from them. It costs a two-stage training pipeline: supervised fine-tuning on synthetic pitfall-labeled data plus GRPO co-training against the target environment, plus added inference latency at intervention points. It can fail when the synthetic pitfall distribution mismatches the deployment environment, when pitfalls are mispredicted (false interventions can derail correct trajectories), and the method requires retraining per environment.",
  "rationale": "The quality gains are substantial but the approach requires environment-specific SFT and RL co-training infrastructure that exceeds a constrained 24 GB GPU setup and limited budget, so it is worth tracking rather than adopting now.",
  "technique": "Pitfall anticipation world model for agent steering",
  "family": "planejamento_e_decomposicao",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.29139",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 218,
  "citations": 0,
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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