{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your workload might fit a smaller or differently structured model.",
    "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."
  },
  "builder_review": null,
  "arxiv_id": "2610.19855",
  "title": "Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models",
  "summary": "This is a measurement study, not a deployable method: it compares diffusion language models against standard autoregressive inference with KV-cache on energy per token. Adopting diffusion LLMs would replace the AR serving stack and its mature KV-cache, batching, and quantization tooling with an immature inference pipeline whose parallelism benefits depend on sequence length and batch conditions. What can fail: the energy advantage may not survive real serving workloads, available diffusion models (7-8B, limited post-training) lag AR counterparts in output quality and instruction following, and there is no production-grade serving infrastructure for them on a single 24 GB GPU.",
  "rationale": "The reader should keep AR models with KV-cache in production and merely track diffusion LLMs, since the paper demonstrates an energy trade-off but the ecosystem, tooling, and model quality are not yet viable for a constrained production deployment.",
  "technique": "Energy benchmarking of diffusion vs autoregressive LLMs",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19855",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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    {
      "claim_id": "claim-02",
      "statement": "At 200-600 token sequences, diffusion models (LLaDA-8B, Dream-7B) consume 2.5-2.7 J/token, roughly 2x more energy-efficient than AR models with KV-cache (5.3-5.7 J/token); KV-cache itself reduces AR energy by 50-70% at longer sequences.",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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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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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
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  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}