{
  "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": "2609.38090",
  "title": "Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging",
  "summary": "Mira replaces reactive expert offloading, where the system waits for router outputs before transferring experts, with proactive prefetching driven by lightweight per-layer predictors that anticipate expert usage two layers ahead, combined with a two-tier HOT+STAGE cache and a custom quantized expert format. The costs are additional predictor training and maintenance, cache-management complexity, integration effort into a custom runtime, and a small accuracy degradation from the custom compression. It can fail when routing distributions shift and predictions miss, when workloads lack expert-reuse skew, or when quantization degrades quality on accuracy-sensitive tasks.",
  "rationale": "The approach directly targets the reader's single-24GB-GPU constraint, but the gains depend on routing predictability and a custom runtime, so it warrants a small-scale pilot against existing offloading tools before production adoption.",
  "technique": "Predictive expert prefetching with two-tier MoE cache",
  "family": "serving_e_batching",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.38090",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": null,
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  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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