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    "scenario": "Your workload might fit a smaller or differently structured model.",
    "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."
  },
  "builder_review": null,
  "arxiv_id": "2609.40127",
  "title": "Learning Functional Subspaces for Neural Network Compression",
  "summary": "LSP replaces closed-form low-rank truncation criteria (activation energy, layer-wise reconstruction, quadratic loss approximations) with orthogonal projectors learned jointly against a global KL or training-loss objective, then merged into standard low-rank factors that also permit a shared narrow KV latent. The cost is an additional optimization pass over the projectors (weights stay frozen, so it fits one 24 GB GPU for models up to roughly 7B with careful handling), plus rank-allocation overhead and the engineering of tied-layer factor sharing, which is not yet an off-the-shelf library feature. It can fail when the KL-to-dense objective does not preserve task-specific behavior, when downstream kernels do not exploit the low-rank factors or shared latent (eliminating the speedup), and at extreme compression where even learned subspaces degrade.",
  "rationale": "The method fits constrained infrastructure since it only trains small projectors on frozen weights and yields genuine memory and latency gains, but the claimed advantages require reimplementing the training and factor-merging pipeline and validating speedups on the reader's actual serving stack.",
  "technique": "End-to-end learned low-rank subspace compression",
  "family": "arquitetura_eficiente",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.40127",
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  "total_implementations": 2,
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