{
  "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": "2609.35352",
  "title": "Fiona: Accelerating FHE Inference with Packing-Aware Ternary Weights",
  "summary": "FIONA replaces plaintext-ciphertext multiplications in fully homomorphic encrypted inference with additions and subtractions by selectively ternarizing weight groups that align with the FHE packing layout, while keeping sensitive groups in full precision and lowering polynomial degrees to reduce multiplicative depth. The cost is an offline optimization and compilation step per model and packing layout, plus up to roughly 1% accuracy loss and a hybrid operator implementation that does not exist in standard serving stacks. The approach fails outside FHE deployments: it presupposes an encrypted-inference serving architecture, FHE scheme expertise, and encrypted client workloads, none of which apply to a standard GPU- or API-based production pipeline.",
  "rationale": "The technique only yields value inside an FHE encrypted-inference deployment, which the reader's constrained GPU/API production setup does not involve.",
  "technique": "Packing-aware selective ternarization for FHE inference",
  "family": "quantizacao",
  "recommendation": "nao_aplica",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35352",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FIONA replaces plaintext-ciphertext multiplications in fully homomorphic encrypted inference with additions and subtractions by selectively ternarizing weight groups that align with the FHE packing layout, while keeping sensitive groups in full precision and lowering polynomial degrees to reduce multiplicative depth. The cost is an offline optimization and compilation step per model and packing layout, plus up to roughly 1% accuracy loss and a hybrid operator implementation that does not exist in standard serving stacks. The approach fails outside FHE deployments: it presupposes an encrypted-inference serving architecture, FHE scheme expertise, and encrypted client workloads, none of which apply to a standard GPU- or API-based production pipeline.",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Reduces PMult operations by 53.4-79.5% and accelerates end-to-end encrypted inference by 2.38x, 1.68x, and 1.84x on VGG11, ViT, and BERT respectively, with under 1% accuracy loss.",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "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": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}