{
  "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": "2610.27444",
  "title": "LoRASpace: A Pool-Wide Shared Substrate for Static and Dynamic Multi-LoRA Composition",
  "summary": "It replaces either lossy multi-LoRA weight merging or one-forward-pass-per-LoRA composite inference with a shared low-rank subspace computed once per adapter pool. The cost is an offline SVD over the pool, storage and bookkeeping for the shared basis, plus extra routing logic for dynamic mode; it is training-free at inference time. It can fail when the pool has little rank overlap, as in the diversity-curated LoRA-Retriever benchmark near 96% of summed rank, and the ablation indicates the static quality gain may come from rank allocation rather than the pool-wide basis itself.",
  "rationale": "For a 24 GB GPU or API-constrained team already serving multiple LoRAs, the method is worth piloting on its own adapter pool because overlap must be measured before any latency claim transfers.",
  "technique": "Pool-wide shared LoRA subspace composition",
  "family": "serving_e_batching",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.27444",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
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