{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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 quality, actual GPU memory, serving throughput, and preparation 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": "2610.22985",
  "title": "Prism Attention: Proposal-Refined Index Sharing Mechanism for Efficient LLMs Inference",
  "summary": "Prism Attention replaces rigid cross-layer sharing of sparse attention indices (computed once at anchor layers and statically reused) with a coarse-to-fine proposal-and-refinement scheme that exploits the localized shift of salient tokens between layers. The cost is additional per-layer refinement logic and index management on top of a sparse-attention implementation, plus the integration complexity of a non-standard attention path rather than stock FlashAttention. It can fail on workloads whose attention patterns do not exhibit the assumed cross-layer locality, and realized speedup depends heavily on whether an efficient implementation of the proposal-refinement step exists for the reader's serving stack.",
  "rationale": "The claim is strong for long-context decoding, but gains are conditional on a specific sparsity assumption and require non-trivial integration, so a 24 GB/API-constrained team should benchmark it on their own workloads before committing to production.",
  "technique": "Proposal-refined cross-layer sparse attention (Prism Attention)",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.22985",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 127,
  "citations": null,
  "claims": [
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      "claim_id": "claim-01",
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    },
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    }
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}