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    "change": "Check for available weights and a compatible runtime before planning a product trial.",
    "baseline": "Your current model on identical held-out tasks and the same resource 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": "Task success, latency, memory, context limits, and migration 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."
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  "builder_review": null,
  "arxiv_id": "2610.03127",
  "title": "Coverage You Can Steer: Online Conformal Calibration for RL-Driven Hardware-Aware NAS",
  "summary": "Replaces static one-shot conformal quantile calibration, which loses coverage control under the non-exchangeable candidate distribution produced by an improving RL search policy, with online feedback control (Adaptive Conformal Inference variants) that steers empirical coverage to the requested level. Costs little in memory or latency (quantile updates per step), but adds an online control loop and hyperparameter choice among ACI variants on top of the existing RL-NAS pipeline. Can fail when reward predictors are poorly calibrated in absolute terms (bounds become vacuous or over-aggressive), when the search distribution shifts faster than the controller adapts, and the validation is limited to three NAS testbeds and one acquisition experiment, so gains may not transfer to other search spaces.",
  "rationale": "The method is inexpensive and code is available, but it only matters if the reader actually runs RL-driven hardware-aware NAS, so its cost savings require validation on the reader's own search workload before adoption.",
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  "family": "arquitetura_eficiente",
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  "published": "2026-10-02",
  "as_of": "2026-10-18",
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  "source_url": "https://arxiv.org/abs/2610.03127",
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