{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "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": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "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": "2610.27913",
  "title": "ACE-Compression: An Ability-Centric Evaluation of Compressed Large Language Models",
  "summary": "Replaces aggregate task-score evaluation of quantized or pruned checkpoints with a diagnostic framework probing four ability dimensions: procedural reasoning, knowledge grounding, social cognition, and persona fidelity. Cost is additional evaluation compute per checkpoint and integration of a new benchmark suite, with no change to model memory or serving latency. It can fail if its ability probes do not match the reader's actual workload distribution, leading to a compression choice optimized for the framework's dimensions rather than the application's real failure modes.",
  "rationale": "The reader already deploys or will deploy quantized or pruned models on a single 24 GB GPU, and this framework provides a low-cost, directly applicable method for selecting the compression approach that preserves the abilities their application depends on.",
  "technique": "Ability-centric evaluation of compressed LLMs",
  "family": "quantizacao",
  "recommendation": "adotar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.27913",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.27913",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
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      "source_url": "https://arxiv.org/abs/2610.27913",
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    }
  ],
  "exposure_map": [
    {
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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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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "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": ""
}