{
  "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": "2609.24042",
  "title": "Q-DEQ: Discrete Solving and Quantization for Deep Equilibrium Models in Time Series Forecasting under Edge Deployment Coding Constraints",
  "summary": "Q-DEQ replaces an explicitly stacked multi-layer forecaster and its continuous Anderson fixed-point solver with a shared-layer DEQ whose updates are solved as QUBO problems (via simulated annealing or a coherent Ising machine), followed by a W8A8 fake-quantized re-forward pass. The cost is up to +2.90% worse MSE on some datasets, plus substantial engineering complexity: per-iteration QUBO formulation, an SA or CIM backend, and quantization calibration all sit on top of standard training. Failure modes include SA failing to converge to a good fixed point within iteration budgets, degradation on datasets where the discrete search is less accurate than Anderson, and dependence on specialized CIM hardware whose claimed advantage is irrelevant to a single-GPU deployment.",
  "rationale": "The storage gains are real but the QUBO/CIM solving machinery adds exotic dependencies and accuracy risk that a constrained team cannot justify when conventional W8A8 quantization of a standard forecaster already captures most of the memory benefit.",
  "technique": "Discrete-solver deep equilibrium model with W8A8 quantization",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24042",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Q-DEQ replaces an explicitly stacked multi-layer forecaster and its continuous Anderson fixed-point solver with a shared-layer DEQ whose updates are solved as QUBO problems (via simulated annealing or a coherent Ising machine), followed by a W8A8 fake-quantized re-forward pass. The cost is up to +2.90% worse MSE on some datasets, plus substantial engineering complexity: per-iteration QUBO formulation, an SA or CIM backend, and quantization calibration all sit on top of standard training. Failure modes include SA failing to converge to a good fixed point within iteration budgets, degradation on datasets where the discrete search is less accurate than Anderson, and dependence on specialized CIM hardware whose claimed advantage is irrelevant to a single-GPU deployment.",
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      "source_url": "https://arxiv.org/abs/2609.24042",
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      "statement": "Parameter sharing cuts parameter counts by 1.80x-3.82x; combined with W8A8, static weight storage is reduced by 4.3x-12.8x, while relative MSE versus the explicit baseline ranges from -1.16% to +2.90% across five datasets.",
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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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    {
      "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": "",
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}