{
  "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": "2609.24401",
  "title": "Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning",
  "summary": "ASF-S replaces heuristic or weight-magnitude structured pruning criteria with Principle Gradient Importance, a gradient-based candidate selection method that preserves output-layer topographical organization (Artificial Functional Connectivity). It costs a pruning search pass plus validation runs, and avoids the retraining phase that conventional structured pruning requires, though the search framework itself adds implementation complexity beyond off-the-shelf pruners. It can fail if the reported no-retraining accuracy recovery does not transfer to the reader's architectures or tasks, since the abstract provides no detail on which benchmarks, model sizes, or degradation margins were measured.",
  "rationale": "Structured pruning is directly relevant to a 24 GB GPU budget, and a criterion that avoids retraining reduces cost, but the single-source claim and unspecified benchmarks require validation on the reader's own models before production adoption.",
  "technique": "Structured pruning via gradient importance and functional connectivity",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24401",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ASF-S replaces heuristic or weight-magnitude structured pruning criteria with Principle Gradient Importance, a gradient-based candidate selection method that preserves output-layer topographical organization (Artificial Functional Connectivity). It costs a pruning search pass plus validation runs, and avoids the retraining phase that conventional structured pruning requires, though the search framework itself adds implementation complexity beyond off-the-shelf pruners. It can fail if the reported no-retraining accuracy recovery does not transfer to the reader's architectures or tasks, since the abstract provides no detail on which benchmarks, model sizes, or degradation margins were measured.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.24401",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "70% parameter reduction with baseline accuracy recovered without retraining the pruned layers, per the paper's benchmarks.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.24401",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
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    },
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      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}