{
  "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": "2609.31168",
  "title": "Audio emotion recognition for atypical hearing",
  "summary": "The method replaces full fine-tuning of the CLAP audio foundation model with low-rank adaptation, training only small adapter matrices on a valence-arousal dataset from neurotypical listeners. The cost is modest: LoRA fine-tuning of a CLAP-scale model fits within a single 24 GB GPU, with the trade-off of slightly lower adaptation capacity than full fine-tuning and dependence on the pretrained checkpoint's quality. The stated open risk is generalization: affective responses learned from neurotypical annotations may not transfer to atypical listeners with auditory hypersensitivity, and the abstract reports no evaluation results confirming that small annotated datasets suffice.",
  "rationale": "LoRA on an audio foundation model is a mature, low-cost technique that runs on a single 24 GB GPU and is directly applicable to constrained production fine-tuning workloads.",
  "technique": "LoRA fine-tuning of CLAP for audio emotion recognition",
  "family": "treino_eficiente",
  "recommendation": "adotar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31168",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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  "equations_status": "not_fetched",
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