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    "change": "Check for available weights and a compatible runtime before planning a product trial.",
    "baseline": "Your current model on identical held-out tasks and the same resource budget.",
    "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": "Task success, latency, memory, context limits, and migration cost.",
    "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."
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  "builder_review": null,
  "arxiv_id": "2609.24391",
  "title": "NAVIR: Neuromorphic Audio-Visual Speech Recognition for Robust Human-Robot Interaction on Edge Hardware",
  "summary": "NAVIR replaces conventional AVSR pipelines built on 3D convolutions, recurrent units, and attention with factorized 2D-convolutional AkidaNet modules plus CTC decoding, targeting the BrainChip Akida neuromorphic chip. The cost is a hard dependency on Akida-class spiking hardware, quantization-aware training of a custom multimodal pipeline, and restricted operator support (sequential 2D convolutions only), which constrains model capacity and generality. It can fail through accuracy loss from quantization and firing-rate sensitivity, poor transfer beyond narrow command corpora like the 98.6%-accuracy industrial dataset, and the need for a camera with reliable lip visibility in the deployment environment.",
  "rationale": "The energy gains are real but require purchasing and integrating specialized neuromorphic hardware, which is outside the reader's existing GPU-and-API infrastructure and only pays off for dedicated noisy-edge deployments such as industrial human-robot interaction.",
  "technique": "Neuromorphic audio-visual speech recognition on edge hardware",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
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  "source_url": "https://arxiv.org/abs/2609.24391",
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  "total_implementations": 2,
  "stars": 214,
  "citations": 0,
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