{
  "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": "2609.38106",
  "title": "Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs",
  "summary": "The paper is an evaluation study, not a new compression method: it replaces the practice of selecting pruned speech-LLM checkpoints using aggregate WER alone with selection that also tracks per-demographic-group WER and the worst-performing group's error rate. The cost is additional evaluation compute (one inference pass per demographic slice per checkpoint) and extra analysis and dataset labeling, which is small relative to deployment risk. What can fail: the fairness effects are dataset-dependent (gaps widened on Fair-Speech but not clearly on Common Voice), so per-group metrics must be measured on the target population, and LoRA recovery after pruning can widen disparities even while improving every group's absolute WER.",
  "rationale": "For a team deploying pruned or compressed speech models, adding per-group WER and worst-group error as selection criteria is a low-cost evaluation change that prevents hidden regressions the paper shows aggregate WER can mask.",
  "technique": "Per-group WER evaluation for pruned speech models",
  "family": "esparsidade_e_poda",
  "recommendation": "adotar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.38106",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 1,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper is an evaluation study, not a new compression method: it replaces the practice of selecting pruned speech-LLM checkpoints using aggregate WER alone with selection that also tracks per-demographic-group WER and the worst-performing group's error rate. The cost is additional evaluation compute (one inference pass per demographic slice per checkpoint) and extra analysis and dataset labeling, which is small relative to deployment risk. What can fail: the fairness effects are dataset-dependent (gaps widened on Fair-Speech but not clearly on Common Voice), so per-group metrics must be measured on the target population, and LoRA recovery after pruning can widen disparities even while improving every group's absolute WER.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.38106",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
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    },
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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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": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "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": ""
}