{
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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": "2610.18462",
  "title": "Readout Scaling Shapes Sparse Feature Recovery",
  "summary": "RC-DS replaces standard centered-logit SAE training coordinates with coordinates canonicalized to remove readout singular-value gains while preserving decision information. It costs an additional coordinate transformation (available as an exact wrapper) and, per the paper's own controls, worse output-logit reconstruction than centered-logit SAEs in most tested settings. It can fail when the generating distribution differs from the assumed pre-readout space, since the benefit reverses with the data distribution, and reconstruction gains do not imply task-useful features, as the label-detection and steering controls indicate.",
  "rationale": "This is an interpretability research method whose benefit is distribution-dependent and whose production relevance for a constrained-infrastructure engineer is not established, so tracking it is warranted but adoption is not.",
  "technique": "Readout-canonicalized decision-space SAE training",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18462",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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