{
  "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": "2610.11315",
  "title": "Deflating the Hessian: Rank-4 W4A4 Quantization for Multimodal Diffusion Transformers",
  "summary": "It replaces separately optimized SVD-style low-rank compensation for W4A4 PTQ with a joint calibration objective using a deflated Hessian plus an activation-noise surrogate. The cost is a more involved calibration/solver implementation, retained rank-4 high-precision factors, and dependence on W4A4 kernels that preserve the claimed benefit. It can fail if calibration data are unrepresentative, activation quantization noise dominates on new prompts, or deployment kernels do not support the assumed 4-bit weight-activation path.",
  "rationale": "For a single-GPU production team this is worth benchmarking only where W4A4 diffusion or LLM inference is already planned, because the evidence supports better quantized quality but the payoff depends on model, calibration set, and kernel availability.",
  "technique": "Joint low-rank W4A4 PTQ calibration",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11315",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces separately optimized SVD-style low-rank compensation for W4A4 PTQ with a joint calibration objective using a deflated Hessian plus an activation-noise surrogate. The cost is a more involved calibration/solver implementation, retained rank-4 high-precision factors, and dependence on W4A4 kernels that preserve the claimed benefit. It can fail if calibration data are unrepresentative, activation quantization noise dominates on new prompts, or deployment kernels do not support the assumed 4-bit weight-activation path.",
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      "source_url": "https://arxiv.org/abs/2610.11315",
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    },
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      "claim_id": "claim-02",
      "statement": "Rank-4 reportedly beats rank-4 SVDQuant in PSNR/LPIPS across five diffusion backbones, surpasses rank-32 SVDQuant on SANA-1.6B, FLUX.1-schnell, and FLUX.1-dev with 8x smaller rank and up to 6.25x faster quantization, and raises Qwen3-8B MMLU from 61.50% to 68.17%.",
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      "source_url": "https://arxiv.org/abs/2610.11315",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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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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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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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": "",
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  "equations_source_sha256": "",
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}