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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."
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    "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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  "arxiv_id": "2610.26747",
  "title": "recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Models",
  "summary": "recAMPLIFY replaces parameter scaling in masked protein language models with a weight-tied recursive Transformer core that refines hidden states over a variable number of cycles, optionally with a Gated Mixture Recurrence decoder for adaptive depth. The recipe requires large-scale pretraining (2T tokens), which exceeds the reader's infrastructure and budget; only released checkpoints, if published, would make GMR's adaptive-depth inference usable, and GMR's accuracy trade-off is task-dependent and can be negative. Recursive weight-tying can become unstable beyond the trained depth (the paper's own gating analysis addresses this), and the method is specific to masked protein encoders, not general LLM workloads.",
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  "family": "arquitetura_eficiente",
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  "published": "2026-10-19",
  "as_of": "2026-10-20",
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