{
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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": "2609.39034",
  "title": "Switching Linear Attention",
  "summary": "SwiLA replaces the softmax-attention key-value cache with a fixed-size recurrent state updated as online expectation-maximization over a mixture of linear regressions, so each output dimension dynamically selects among multiple linear attention components. The cost is increased per-step computation and state size relative to plain linear attention, plus the requirement to train or fine-tune the architecture from scratch, which is not feasible on a single 24 GB GPU or through third-party inference APIs. What can fail is adoption itself: the reported gains are measured on the authors' own training runs, no pretrained checkpoints or serving support are established, and the constant-memory advantage may not materialize in quality for workloads dominated by short contexts.",
  "rationale": "The technique requires pretraining a novel sequence layer, which the reader's constrained infrastructure cannot support, so it is relevant to track but not adoptable until pretrained models and API or serving support exist.",
  "technique": "Mixture-of-regressions linear attention recurrence (SwiLA)",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
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  "source_url": "https://arxiv.org/abs/2609.39034",
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