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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": "2610.09710",
  "title": "SpikingVLA: Asynchronous Spiking Vision-Language-Action Models",
  "summary": "Replaces direct ANN VLA inference and prior high-timestep spiking VLA methods with an ANN-to-SNN conversion using Dendritic Integrate-and-Fire neurons plus asynchronous cross-component execution. The cost is a conversion pipeline, custom neuron and execution machinery tied to SNN simulation or neuromorphic runtimes, and possible residual accuracy loss from few-timestep conversion. It can fail if no suitable SNN runtime exists for the target hardware, if activation distributions in the reader's pretrained VLA differ from the navigation benchmark, or if asynchronous execution introduces timing nondeterminism in control loops.",
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  "technique": "ANN-to-SNN conversion with dendritic neurons and asynchronous execution",
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  "published": "2026-10-07",
  "as_of": "2026-10-18",
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  "source_url": "https://arxiv.org/abs/2610.09710",
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