{
  "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": "2610.25250",
  "title": "Improving the Expressive Power of Hybrid Sequence Models via Multi-Hop Retrieval",
  "summary": "MuRA replaces conventional hybrid blocks (full-attention layers plus fixed-state efficient layers) by letting efficient layers perform m sequential retrieval hops from a shared KV cache, which provably enables m-function composition where existing blocks require capacity scaling as Omega(sqrt(n)) even for two hops. The cost is architectural: it is a new block design that requires training from scratch or fine-tuning a custom implementation, with added implementation complexity in the retrieval mechanism, though it preserves asymptotic compute and memory efficiency. It can fail in practice because the evidence is a theoretical expressivity hierarchy plus synthetic composition tasks; there is no demonstration on pretrained models, real downstream benchmarks, or pretrained-checkpoint compatibility, so gains may not transfer to production workloads.",
  "rationale": "This is an expressivity theory paper with synthetic validation only, offering no deployable weights or production benchmark results, so an engineer with constrained infrastructure cannot adopt it now but should track whether multi-hop KV-cache retrieval is incorporated into trainable hybrid models.",
  "technique": "Multi-hop Retrieval Attention (MuRA) hybrid block",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-10-18",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.25250",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "dimension": "security",
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    {
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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}