{
  "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": "2609.19683",
  "title": "MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration",
  "summary": "MiX replaces shared-exponent microscaling block formats (NVFP4/MX-style) with a format grouping private per-element exponents under one shared mantissa, so multipliers in the datapath become shifters, and pairs it with a dual-format inference framework for VLM outlier topologies. The cost is that the claimed speed, energy, and area gains exist only on a custom accelerator that does not exist as a product; on the reader's GPU there is no hardware support for this format, so only the quantization accuracy result is indirectly relevant. It can fail in practice because accuracy parity was shown on selected VLMs and benchmarks, the dual-format path adds format-selection complexity, and simulated accelerator results often do not survive contact with real silicon or vendor toolchains.",
  "rationale": "The reader has a single commodity GPU and cannot fabricate or purchase the custom accelerator the performance claims depend on, so this is a hardware-design contribution to track rather than an adoptable or testable technique.",
  "technique": "Inverted-microscaling low-bit format with custom accelerator",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19683",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "MiX replaces shared-exponent microscaling block formats (NVFP4/MX-style) with a format grouping private per-element exponents under one shared mantissa, so multipliers in the datapath become shifters, and pairs it with a dual-format inference framework for VLM outlier topologies. The cost is that the claimed speed, energy, and area gains exist only on a custom accelerator that does not exist as a product; on the reader's GPU there is no hardware support for this format, so only the quantization accuracy result is indirectly relevant. It can fail in practice because accuracy parity was shown on selected VLMs and benchmarks, the dual-format path adds format-selection complexity, and simulated accelerator results often do not survive contact with real silicon or vendor toolchains.",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2609.19683",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "4.5-bit MiX matches or exceeds NVFP4 accuracy on multimodal benchmarks; the custom accelerator delivers 2.3-4.5x speedup, 1.4-2.9x energy reduction over the Focus accelerator, and 25% better area efficiency than NVFP4 baseline.",
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      "source_url": "https://arxiv.org/abs/2609.19683",
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    }
  ],
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
      "source_url": "",
      "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": ""
}