{
  "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": "2609.17515",
  "title": "What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity",
  "summary": "This is an evaluation study, not a new compression method: it assesses existing depth, width, hybrid, and expert pruning (followed by SFT) as replacements for full-size models in smart-home tool calling. The cost of adopting pruning is a post-pruning fine-tuning stage plus evaluation effort, with dense models showing narrow safe pruning regions before sharp degradation, while MoE models tolerate substantially more pruning. After adoption, failures are non-obvious: grounded specificity (device, argument, value correctness) degrades before schema-level intent, so aggregate accuracy can mask broken tool calls, and aggressive dense pruning can cause systematic over-refusal.",
  "rationale": "The paper's actionable value for a constrained team is its finding that pruning decisions for tool-calling deployments must be validated per-component and per-complexity rather than by aggregate accuracy, favoring MoE architectures if aggressive pruning is required.",
  "technique": "Pruning degradation analysis for tool-calling LLMs",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17515",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is an evaluation study, not a new compression method: it assesses existing depth, width, hybrid, and expert pruning (followed by SFT) as replacements for full-size models in smart-home tool calling. The cost of adopting pruning is a post-pruning fine-tuning stage plus evaluation effort, with dense models showing narrow safe pruning regions before sharp degradation, while MoE models tolerate substantially more pruning. After adoption, failures are non-obvious: grounded specificity (device, argument, value correctness) degrades before schema-level intent, so aggregate accuracy can mask broken tool calls, and aggressive dense pruning can cause systematic over-refusal.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.17515",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
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    },
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "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": ""
}