{
  "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": "2609.30950",
  "title": "Low-Bit Recurrent States in Hybrid Language Models",
  "summary": "The method replaces uniform eight-bit-or-higher quantization of fixed-size recurrent states in hybrid language models with mixed-precision bit allocation derived from observability-Gramian distortion weights, normalized state ranges, and logarithmically quantized decay rates, requiring no calibration data, rotation, or training. Its costs are per-token quantization overhead and variable metadata for storing per-channel bit widths, and gains diminish when state write-backs are less frequent. It can fail or underperform when the deployed model is not a hybrid recurrent architecture, when the inference stack cannot efficiently execute mixed-precision state updates, or when the write-back cadence and bit budget fall outside the validated regime.",
  "rationale": "The technique is training-free and targets memory-constrained serving of hybrid models, but its benefit is workload- and model-dependent and requires validation on the reader's specific inference stack and write-back frequency. Recommendation: testar.",
  "technique": "Mixed-precision quantization of hybrid recurrent states",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.30950",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces uniform eight-bit-or-higher quantization of fixed-size recurrent states in hybrid language models with mixed-precision bit allocation derived from observability-Gramian distortion weights, normalized state ranges, and logarithmically quantized decay rates, requiring no calibration data, rotation, or training. Its costs are per-token quantization overhead and variable metadata for storing per-channel bit widths, and gains diminish when state write-backs are less frequent. It can fail or underperform when the deployed model is not a hybrid recurrent architecture, when the inference stack cannot efficiently execute mixed-precision state updates, or when the write-back cadence and bit budget fall outside the validated regime.",
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    },
    {
      "claim_id": "claim-02",
      "statement": "A four-bit mean payload reduces excess negative log-likelihood by factors of 3.3--27.9 relative to the best of seven baselines across three hybrid models; at six bits, NLL differs from the FP32-state baseline by less than 0.005 nats.",
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    }
  ],
  "exposure_map": [
    {
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      "next_check": "",
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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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      "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": "",
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}