{
  "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": "2606.01774",
  "title": "FLARE: Diffusion for Hybrid Language Model",
  "summary": "Replaces purely autoregressive decoding with one checkpoint that switches between verified autoregressive decoding and parallel diffusion denoising. It requires conversion post-training on a hybrid autoregressive checkpoint, dedicated kernels, and high-quality transfer data, which the paper identifies as the dominant factor. Adoption depends on a strong starting checkpoint and suitable transfer data; the code and kernels are not yet mature infrastructure for direct use.",
  "rationale": "Conversion training, custom kernels, and transfer data exceed immediate adoption on a small setup, but the approach is worth following.",
  "technique": "Hybrid autoregressive-to-diffusion checkpoint conversion",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-06-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2606.01774",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 20,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces purely autoregressive decoding with one checkpoint that switches between verified autoregressive decoding and parallel diffusion denoising. It requires conversion post-training on a hybrid autoregressive checkpoint, dedicated kernels, and high-quality transfer data, which the paper identifies as the dominant factor. Adoption depends on a strong starting checkpoint and suitable transfer data; the code and kernels are not yet mature infrastructure for direct use.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2606.01774",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Reports consistent throughput gains over open-source dLLM baselines in concurrent single-GPU serving, without a numerical factor in the abstract.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2606.01774",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
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      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [
    {
      "anchor": "S3.E4",
      "label": "(4)",
      "section": "3 Method",
      "role": "loss",
      "latex": "\\mathcal{L}_{{\\color[rgb]{0,0,0}\\textbf{FLARE}}}(\\theta)=\\mathcal{L}_{\\mathrm{AR}}(\\theta)+\\mathcal{L}_{\\mathrm{diff}}(\\theta).",
      "mathml": "<math display=\"block\"><mrow><mrow><mrow><msub><mi>ℒ</mi><mtext>FLARE</mtext></msub><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo stretchy=\"false\">(</mo><mi>θ</mi><mo stretchy=\"false\">)</mo></mrow></mrow><mo>=</mo><mrow><mrow><msub><mi>ℒ</mi><mi>AR</mi></msub><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo stretchy=\"false\">(</mo><mi>θ</mi><mo stretchy=\"false\">)</mo></mrow></mrow><mo>+</mo><mrow><msub><mi>ℒ</mi><mi>diff</mi></msub><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo stretchy=\"false\">(</mo><mi>θ</mi><mo stretchy=\"false\">)</mo></mrow></mrow></mrow></mrow><mo lspace=\"0em\">.</mo></mrow></math>",
      "context": "Let <math display=\"inline\"><mrow><mi>𝐱</mi><mo>=</mo><mrow><mo stretchy=\"false\">(</mo><msup><mi>x</mi><mn>1</mn></msup><mo>,</mo><mi mathvariant=\"normal\">…</mi><mo>,</mo><msup><mi>x</mi><mi>L</mi></msup><mo stretchy=\"false\">)</mo></mrow><mo>∈</mo><msup><mi>𝒱</mi><mi>L</mi></msup></mrow></math> be a training sample partitioned into <math display=\"inline\"><mi>K</mi></math> contiguous blocks <math display=\"inline\"><mrow><msub><mi>𝐱</mi><mi>b</mi></msub><mo>=</mo><mrow><mo stretchy=\"false\">(</mo><msup><mi>x</mi><mrow><mrow><mrow><mo stretchy=\"false\">(</mo><mrow><mi>b</mi><mo>−</mo><mn>1</mn></mrow><mo stretchy=\"false\">)</mo></mrow><mo lspace=\"0em\" rspace=\"0em\">​</mo><mi>B</mi></mrow><mo>+</mo><mn>1</mn></mrow></msup><mo>,</mo><mi mathvariant=\"normal\">…</mi><mo>,</mo><msup><mi>x</mi><mrow><mi>b</mi><mo lspace=\"0em\" rspace=\"0em\">​</mo><mi>B</mi></mrow></msup><mo stretchy=\"false\">)</mo></mrow></mrow></math> of size <math display=\"inline\"><mi>B</mi></math> (<math display=\"inline\"><mrow><mi>L</mi><mo>=</mo><mrow><mi>K</mi><mo lspace=\"0em\" rspace=\"0em\">​</mo><mi>B</mi></mrow></mrow></math>); we write <math display=\"inline\"><msup><mi>𝐱</mi><mrow><mphantom></mphantom><mo>&lt;</mo><mi>b</mi></mrow></msup></math> for all tokens strictly before block <math display=\"inline\"><mi>b</mi></math>. For each block, we sample a masked subset <math display=\"inline\"><mrow><msub><mi>ℳ</mi><mi>b</mi></msub><mo>⊆</mo><mrow><mo stretchy=\"false\">{</mo><mrow><mrow><mrow><mrow><mo stretchy=\"false\">(</mo><mrow><mi>b</mi><mo>−</mo><mn>1</mn></mrow><mo stretchy=\"false\">)</mo></mrow><mo lspace=\"0em\" rspace=\"0em\">​</mo><mi>B</mi></mrow><mo>+</mo><mn>1</mn></mrow><mo>,</mo><mi mathvariant=\"normal\">…</mi><mo>,</mo><mrow><mi>b</mi><mo lspace=\"0em\" rspace=\"0em\">​</mo><mi>B</mi></mrow></mrow><mo stretchy=\"false\">}</mo></mrow></mrow></math> and form the noisy block <math display=\"inline\"><msub><mover accent=\"true\"><mi>𝐱</mi><mo>~</mo></mover><mi>b</mi></msub></math> by replacing <math display=\"inline\"><msup><mi>x</mi><mi mathvariant=\"normal\">ℓ</mi></msup></math> with [MASK] for <math display=\"inline\"><mrow><mi mathvariant=\"normal\">ℓ</mi><mo>∈</mo><msub><mi>ℳ</mi><mi>b</mi></msub></mrow></math>. The complement <math display=\"inline\"><msubsup><mi>ℳ</mi><mi>b</mi><mi>c</mi></msubsup></math> defines a second, disjoint noisy view <math display=\"inline\"><msubsup><mover accent=\"true\"><mi>𝐱</mi><mo>~</mo></mover><mi>b</mi><mi>c</mi></msubsup></math>. Based on this block-level corruption process, FLARE maximizes a token-balanced clean/noisy objective that preserves the AR path while adding diffusion-style supervision. The clean stream follows the original causal order and provides the next-token prediction term <math display=\"inline\"><msub><mi>ℒ</mi><mi>AR</mi></msub></math>. The noisy stream provides the diffusion term <math display=\"inline\"><msub><mi>ℒ</mi><mi>diff</mi></msub></math>: the two complementary noisy views predict disjoint token subsets, both conditioned on the same preceding clean context. The overall objective is",
      "headline": "",
      "explanation": "",
      "objective": "",
      "symbols": {},
      "evidence": "",
      "evidence_url": "",
      "source_url": ""
    },
    {
      "anchor": "S3.EGx1-2",
      "label": "(5)",
      "section": "3 Method",
      "role": "proposed_method",
      "latex": "\\mathcal{L}_{\\mathrm{diff}}(\\theta) =\\sum_{b=1}^{K}\\left[\\underbrace{\\sum_{\\ell\\in\\mathcal{M}_{b}}\\log p_{\\theta}\\!\\left(x^{\\ell}\\mid\\tilde{\\mathbf{x}}_{b},\\mathbf{x}^{<b}\\right)}_{\\text{primary noisy view}}+\\underbrace{\\sum_{\\ell\\in\\mathcal{M}_{b}^{c}}\\log p_{\\theta}\\!\\left(x^{\\ell}\\mid\\tilde{\\mathbf{x}}_{b}^{c},\\mathbf{x}^{<b}\\right)}_{\\text{complementary noisy view}}\\right].",
      "mathml": "<math display=\"block\"><mrow><mrow><msub><mi>ℒ</mi><mi>diff</mi></msub><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo stretchy=\"false\">(</mo><mi>θ</mi><mo stretchy=\"false\">)</mo></mrow></mrow><mrow><mrow><mphantom></mphantom><mo>=</mo><mrow><mstyle displaystyle=\"true\"><munderover><mo movablelimits=\"false\">∑</mo><mrow><mi>b</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover></mstyle><mrow><mo>[</mo><mrow><munder><munder accentunder=\"true\"><mrow><mstyle displaystyle=\"true\"><munder><mo movablelimits=\"false\">∑</mo><mrow><mi mathvariant=\"normal\">ℓ</mi><mo>∈</mo><msub><mi>ℳ</mi><mi>b</mi></msub></mrow></munder></mstyle><mrow><mrow><mi>log</mi><mo lspace=\"0.167em\">⁡</mo><msub><mi>p</mi><mi>θ</mi></msub></mrow><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo>(</mo><msup><mi>x</mi><mi mathvariant=\"normal\">ℓ</mi></msup><mo fence=\"true\" lspace=\"0em\" rspace=\"0em\">∣</mo><mrow><msub><mover accent=\"true\"><mi>𝐱</mi><mo>~</mo></mover><mi>b</mi></msub><mo>,</mo><msup><mi>𝐱</mi><mrow><mphantom></mphantom><mo>&lt;</mo><mi>b</mi></mrow></msup></mrow><mo>)</mo></mrow></mrow></mrow><mo stretchy=\"true\">⏟</mo></munder><mtext>primary noisy view</mtext></munder><mo>+</mo><munder><munder accentunder=\"true\"><mrow><mstyle displaystyle=\"true\"><munder><mo movablelimits=\"false\">∑</mo><mrow><mi mathvariant=\"normal\">ℓ</mi><mo>∈</mo><msubsup><mi>ℳ</mi><mi>b</mi><mi>c</mi></msubsup></mrow></munder></mstyle><mrow><mrow><mi>log</mi><mo lspace=\"0.167em\">⁡</mo><msub><mi>p</mi><mi>θ</mi></msub></mrow><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo>(</mo><msup><mi>x</mi><mi mathvariant=\"normal\">ℓ</mi></msup><mo fence=\"true\" lspace=\"0em\" rspace=\"0em\">∣</mo><mrow><msubsup><mover accent=\"true\"><mi>𝐱</mi><mo>~</mo></mover><mi>b</mi><mi>c</mi></msubsup><mo>,</mo><msup><mi>𝐱</mi><mrow><mphantom></mphantom><mo>&lt;</mo><mi>b</mi></mrow></msup></mrow><mo>)</mo></mrow></mrow></mrow><mo stretchy=\"true\">⏟</mo></munder><mtext>complementary noisy view</mtext></munder></mrow><mo>]</mo></mrow></mrow></mrow><mo lspace=\"0em\">.</mo></mrow></mrow></math>",
      "context": "Concretely, the clean-stream term is the usual next-token log-likelihood over the full sequence, and the noisy-stream term sums the denoising log-likelihood over the two complementary masked views:",
      "headline": "",
      "explanation": "",
      "objective": "",
      "symbols": {},
      "evidence": "",
      "evidence_url": "",
      "source_url": ""
    },
    {
      "anchor": "S3.EGx1",
      "label": "",
      "section": "3 Method",
      "role": "baseline",
      "latex": "\\mathcal{L}_{\\mathrm{AR}}(\\theta) =\\sum_{\\ell=1}^{L}\\log p_{\\theta}\\!\\left(x^{\\ell}\\mid\\mathbf{x}^{<\\ell}\\right),",
      "mathml": "<math display=\"block\"><mrow><mrow><msub><mi>ℒ</mi><mi>AR</mi></msub><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo stretchy=\"false\">(</mo><mi>θ</mi><mo stretchy=\"false\">)</mo></mrow></mrow><mrow><mrow><mphantom></mphantom><mo>=</mo><mrow><mstyle displaystyle=\"true\"><munderover><mo movablelimits=\"false\">∑</mo><mrow><mi mathvariant=\"normal\">ℓ</mi><mo>=</mo><mn>1</mn></mrow><mi>L</mi></munderover></mstyle><mrow><mrow><mi>log</mi><mo lspace=\"0.167em\">⁡</mo><msub><mi>p</mi><mi>θ</mi></msub></mrow><mo lspace=\"0em\" rspace=\"0em\">​</mo><mrow><mo>(</mo><msup><mi>x</mi><mi mathvariant=\"normal\">ℓ</mi></msup><mo fence=\"true\" lspace=\"0em\" rspace=\"0em\">∣</mo><msup><mi>𝐱</mi><mrow><mphantom></mphantom><mo>&lt;</mo><mi mathvariant=\"normal\">ℓ</mi></mrow></msup><mo>)</mo></mrow></mrow></mrow></mrow><mo>,</mo></mrow></mrow></math>",
      "context": "Concretely, the clean-stream term is the usual next-token log-likelihood over the full sequence, and the noisy-stream term sums the denoising log-likelihood over the two complementary masked views:",
      "headline": "",
      "explanation": "",
      "objective": "",
      "symbols": {},
      "evidence": "",
      "evidence_url": "",
      "source_url": ""
    }
  ],
  "equations_status": "selected",
  "core_kind": "formula",
  "equations_fetched_at": "2026-09-13",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}