{
  "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": "2610.13806",
  "title": "FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems",
  "summary": "FORTE replaces ad hoc or importance-score-based feature removal with a model-agnostic pipeline of efficient perturbation scoring, grouped ablation over candidate feature sets to capture interactions, lineage tracing of downstream consumer models, and offline-to-online validation before deprecation. Its cost is the engineering of daily perturbation evaluation, grouped retraining runs, lineage instrumentation, and A/B test capacity, which is substantial for a small team without existing experimentation infrastructure. It can fail when offline ablation results do not predict online behavior (the paper's own largest candidate set was rejected by online guardrails), when feature importance drifts faster than the evaluation cadence, or when coordinated deprecation across multiple consumer models stalls.",
  "rationale": "The core idea, permutation-style feature evaluation plus grouped ablation before removal, is cheap to run on a single GPU for any feature-heavy production model, but the full framework assumes lineage tracking and A/B test space that require workload-specific validation at small scale.",
  "technique": "Feature deprecation via grouped ablation (FORTE)",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13806",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FORTE replaces ad hoc or importance-score-based feature removal with a model-agnostic pipeline of efficient perturbation scoring, grouped ablation over candidate feature sets to capture interactions, lineage tracing of downstream consumer models, and offline-to-online validation before deprecation. Its cost is the engineering of daily perturbation evaluation, grouped retraining runs, lineage instrumentation, and A/B test capacity, which is substantial for a small team without existing experimentation infrastructure. It can fail when offline ablation results do not predict online behavior (the paper's own largest candidate set was rejected by online guardrails), when feature importance drifts faster than the evaluation cadence, or when coordinated deprecation across multiple consumer models stalls.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.13806",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On a large ranking model, deprecating 8 features reduced trainable parameters by 29.9%, improved serving throughput by 19.8-25.5%, and cut annual training costs by hundreds of thousands of dollars; on a search-relevance model, 43.3% of features were removed without online regression.",
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      "source_url": "https://arxiv.org/abs/2610.13806",
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    }
  ],
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      "dimension": "latency",
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    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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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": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}