{ "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": "2609.12550", "title": "Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances", "summary": "The method replaces static single-bitwidth serving or request-agnostic mixing across pre-materialized quantized MoE instances with per-request routing driven by a fragility-weighted perplexity risk score and a window-level linear program. It costs an additional reference-instance prefill per request, an online LP solve per window, and the operational burden of maintaining calibration between the reference and candidate quantized instances. It can fail if FWP calibration drifts from actual per-instance degradation, if traffic shifts away from the evaluated prompt population, or if the 2.5% incremental gain over request-agnostic mixing does not justify the added routing complexity, since the result is demonstrated on only 88 prompts of one model family.", "rationale": "The approach presupposes a fixed pool of multiple materialized quantized replicas of a large MoE model, which exceeds a single 24 GB GPU, and its measured advantage over the simpler request-agnostic baseline is only 2.5% relative, so small-scale adopters should track it rather than deploy it.", "technique": "Quality-constrained routing across quantized MoE instances", "family": "serving_e_batching", "recommendation": "observar", "published": "2026-09-11", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.12550", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces static single-bitwidth serving or request-agnostic mixing across pre-materialized quantized MoE instances with per-request routing driven by a fragility-weighted perplexity risk score and a window-level linear program. It costs an additional reference-instance prefill per request, an online LP solve per window, and the operational burden of maintaining calibration between the reference and candidate quantized instances. It can fail if FWP calibration drifts from actual per-instance degradation, if traffic shifts away from the evaluated prompt population, or if the 2.5% incremental gain over request-agnostic mixing does not justify the added routing complexity, since the result is demonstrated on only 88 prompts of one model family.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.12550", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "FWP allocation reaches a 1.284x offline model-based throughput multiplier versus 1.253x for request-agnostic mixing and 1.000x for static W4, an incremental 2.5% relative gain over the agnostic baseline, on 88 extended Qwen prompts.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.12550", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [ { "anchor": "S3.E6", "label": "(6)", "section": "3 Theoretical Framework", "role": "proposed_method", "latex": "\\Delta Q(\\mathbf{b})=\\alpha\\,\\Delta_{r}(b_{r})+(1-\\alpha)\\,\\Delta_{c}(b_{c}),", "mathml": "Δ​Q​(𝐛)=α​Δr​(br)+(1−α)​Δc​(bc),", "context": "Conditioning on the request type gives the exact decomposition", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S3.E10", "label": "(10)", "section": "3 Theoretical Framework", "role": "proposed_method", "latex": "w_{i}(x)=\\frac{(\\bar{\\Lambda}_{i}(x)+\\epsilon_{w})^{-1}}{\\frac{1}{|I_{p}(x)|}\\sum_{j\\in I_{p}(x)}(\\bar{\\Lambda}_{j}(x)+\\epsilon_{w})^{-1}},", "mathml": "wi​(x)=(Λ¯i​(x)+ϵw)−11|Ip​(x)|​∑j∈Ip​(x)(Λ¯j​(x)+ϵw)−1,", "context": "We design FWP from the router-norm fragility signal in prior work [7]. For the non-padding prompt-token index set Ip​(x), let Λ¯i​(x) be the mean router norm of experts activated by token i. Define weights normalized within the request,", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S3.E11", "label": "(11)", "section": "3 Theoretical Framework", "role": "proposed_method", "latex": "\\ell_{\\mathrm{FWP}}(x;\\mathbf{b})=\\frac{1}{|I_{p}(x)|}\\sum_{i\\in I_{p}(x)}w_{i}(x)\\bigl(-\\log p_{\\mathbf{b}}(x_{i}\\mid x_{ℓFWP​(x,𝐛)=1|Ip​(x)|​∑i∈Ip​(x)wi​(x)​(−log⁡p𝐛​(xi∣x<i)).", "context": "with ϵw>0, and the log-FWP observable", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" } ], "equations_status": "selected", "core_kind": "formula", "equations_fetched_at": "2026-09-29", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }