{
  "schema_version": 1,
  "builder_plan": {
    "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": "2609.35579",
  "title": "Output-aware Residual Stream Pruning for Large Language Models",
  "summary": "Replaces activation-reconstruction-error-based residual stream pruning with a sensitivity-weighted covariance criterion, where subspace selection is solved via a tractable eigendecomposition rather than direct optimization. The cost is the pruning pipeline itself: computing activation covariance and output sensitivity via a second-order KL approximation requires calibration data and an offline procedure, though the final spectral step retains the efficiency of rotation-based methods. It can fail if the second-order approximation misestimates sensitivity on calibration data unrepresentative of production traffic, if the pruned hidden dimension degrades instruction-following behavior not captured by perplexity or KL, or if downstream kernels do not exploit the narrower hidden size, yielding no real inference savings.",
  "rationale": "Structured hidden-dimension pruning is feasible on a single 24 GB GPU and the method is a drop-in improvement over an existing pruning criterion, but the abstract offers no quantified speedup or compression figures, so workload-specific validation on the target model and tasks is required before adoption.",
  "technique": "Output-aware residual stream pruning",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35579",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "dimension": "security",
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    {
      "dimension": "data_and_training",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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  "equations_source_sha256": "",
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}