{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2610.19778",
  "title": "Computational compressive imaging reconstruction with a simulated-annealing-based Ising solver",
  "summary": "The method replaces gradient-based compressed sensing algorithms and neural network reconstruction for single-pixel and lensless imaging with an Ising-model formulation whose Hamiltonian is minimized by a simulated-annealing solver. It costs the engineering effort of encoding the inverse problem as an Ising Hamiltonian plus auxiliary sparsity and smoothness constraints, with reconstruction quality reported as comparable to or better than mainstream algorithms but no quantified speedup or energy figure in the abstract. It can fail because simulated annealing offers no convergence guarantees to the ground state, scales poorly on general-purpose hardware, and the claimed compute and power benefits depend on dedicated Ising-machine hardware that is not commercially accessible.",
  "rationale": "The work targets physics-based computational imaging rather than the reader's ML production stack, and its prospective efficiency gains rely on non-von Neumann Ising hardware the reader does not have.",
  "technique": "Ising-model formulation of compressive imaging reconstruction",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19778",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2610.19778",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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