{
  "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": "2610.00929",
  "title": "Platonic Task Arithmetic",
  "summary": "The method replaces per-model fine-tuning and single-architecture task arithmetic by extracting a model-agnostic task descriptor from one model and injecting it into a heterogeneous target via a single least-squares weight edit or a low-rank adapter, needing only unlabeled probe images and class-name prompts. The closed-form variant costs one least-squares solve with no optimization loop and composes edits by signed summation; the adapter variant requires one optimization run per edit, and both require access to target weights, excluding API-only models. It can fail because heterogeneous models share the task object only partially (the model-specific residual is comparable in norm to the shared component), so 20-26% of the in-model gain is lost in transfer, with likely degradation on tasks far from the classification settings evaluated.",
  "rationale": "For a team with one 24 GB GPU, a closed-form weight edit that transfers and composes task knowledge without fine-tuning is inexpensive to validate, but the demonstrated partial sharing between models and the restriction to encoder-paired classification tasks make workload-specific testing necessary before production adoption.",
  "technique": "Cross-architecture task vector transfer (Universal Task Descriptors)",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00929",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces per-model fine-tuning and single-architecture task arithmetic by extracting a model-agnostic task descriptor from one model and injecting it into a heterogeneous target via a single least-squares weight edit or a low-rank adapter, needing only unlabeled probe images and class-name prompts. The closed-form variant costs one least-squares solve with no optimization loop and composes edits by signed summation; the adapter variant requires one optimization run per edit, and both require access to target weights, excluding API-only models. It can fail because heterogeneous models share the task object only partially (the model-specific residual is comparable in norm to the shared component), so 20-26% of the in-model gain is lost in transfer, with likely degradation on tasks far from the classification settings evaluated.",
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      "source_url": "https://arxiv.org/abs/2610.00929",
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      "claim_id": "claim-02",
      "statement": "Cross-model transfer of task descriptors retains 74-80% of the gain achieved by the target model's own descriptors, across six model families, eight classification tasks, and an audio-text setting.",
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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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      "finding": "",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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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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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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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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}