{
  "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": "2609.24517",
  "title": "Not All Task Vectors Need Equal Rank: Energy-Proportional Allocation for Model Merging",
  "summary": "SERA replaces uniform rank assignment in SVD-based model merging with per-task rank allocation proportional to each task vector's singular-value energy, keeping the total rank budget unchanged. It costs one SVD per task vector plus an allocation step, adding modest computation and implementation complexity over fixed-rank merging with no extra inference cost. It can fail when the assumption that spectral energy predicts useful capacity does not hold, and evidence is limited to vision protocols, so gains on language-model merging are unverified.",
  "rationale": "The method is cheap to implement on constrained infrastructure and preserves inference cost, but its benefit is demonstrated only on vision benchmarks and depends on task spectral structure, so workload-specific validation is warranted before adoption.",
  "technique": "Energy-proportional rank allocation for SVD model merging",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24517",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2609.24517",
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    }
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      "dimension": "operations",
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    },
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "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": "",
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}