{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your inference service slows down when several customers send requests together.",
    "change": "Replay a recorded request trace with only the proposed serving policy changed.",
    "baseline": "Your current scheduler on the same model and hardware.",
    "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": "Requests meeting your latency target, throughput, failures, and cost per successful request.",
    "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": "2610.20136",
  "title": "Seeing at the Edge: Energy Characterization of Vision-Language Model Inference on Edge Platforms",
  "summary": "The paper does not propose a method; it replaces intuition-based edge platform selection with measured per-request fixed energy and per-token marginal energy for Qwen2-VL-2B across CPU, NPU, and GPU deployment stacks. Adopting its guidance costs nothing beyond reading, but acting on it requires purchasing specific edge hardware (Pi 5 plus Hailo-10H, or Jetson Orin Nano) and accepting stack-specific quantization. The findings can fail to transfer because the comparison conflates silicon with quantization and serving software, covers a single 2B model, and the crossover near 40 output tokens shifts with response-length distribution and model choice.",
  "rationale": "The reader operates a 24 GB GPU or APIs rather than edge single-board computers, so the results are useful context for deployment decisions but not directly actionable on their current infrastructure.",
  "technique": "Energy characterization of VLM inference on edge accelerators",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20136",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    },
    {
      "dimension": "compatibility",
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      "dimension": "security",
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    {
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      "dimension": "reproducibility",
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  ],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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