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    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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 quality, actual GPU memory, serving throughput, and preparation 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": "2610.10274",
  "title": "Sparse Planning in Visual World Models via Cost Gradients",
  "summary": "COSTGRAD replaces processing the full spatial token grid during latent planning in token-based world models: it ranks tokens by the gradient norm of the planning cost with respect to each input token and keeps only the top subset, with no additional training. The cost is one gradient computation per planning step plus selector-architecture coupling; quality is preserved only on three of four benchmarks and only on AdaLN-conditioned predictors. After adoption the method can silently degrade to random-selection-level performance when paired with concat-style action conditioning, because gradient-selected removal induces more action-pathway drift on that architecture.",
  "rationale": "The speedup is real and training-free, but the demonstrated architecture-dependent failure mode means the gain must be validated on the reader's specific world-model architecture before production use.",
  "technique": "Cost-gradient token selection for world-model planning",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10274",
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  "total_implementations": 1,
  "stars": 215,
  "citations": null,
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