{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "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": "2609.24328",
  "title": "A Distributional Optimisation Perspective on Combining Models in Deep Learning",
  "summary": "The method replaces ad hoc ensembling and weight-averaging of fine-tuned adapters with a principled joint-training formulation, casting model combination as entropy-regularised optimisation over a discrete distribution of models. It costs additional training computation to jointly optimise the component models rather than training them independently, and it adds algorithmic complexity (e.g., mean field Langevin dynamics, functional variational gradient descent) on top of standard fine-tuning pipelines. It can fail because the objective is non-convex in the adapter-averaging case, so convergence guarantees do not transfer, and the abstract reports no quantified production-level gain over conventional ensembling or merging.",
  "rationale": "The paper is exploratory, establishes that key theoretical guarantees hold only for ensembles, and provides no quantified improvement in the abstract, so it warrants monitoring rather than adoption or small-scale testing in a constrained production setting.",
  "technique": "Distributional optimisation for ensemble and LoRA merging",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24328",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2609.24328",
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      "dimension": "compatibility",
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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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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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}