{
  "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": "2610.23239",
  "title": "Response-Informed Value Estimation for Cost-Aware Multi-Turn LLM Routing",
  "summary": "RIVET replaces static or outcome-only-trained multi-turn model routing with turn-level routing trained via two value estimators, one of which conditions on the realized response during training only. It costs an offline training pipeline over logged trajectories, two value networks, and labeled task outcomes, plus per-turn scoring overhead at deployment; routing itself stays pre-call and cheap. It can fail when logged trajectories are scarce or off-distribution relative to production traffic, when candidate model prices or behaviors change after training, and on benchmarks unlike MINT where response-informed credit assignment may transfer poorly.",
  "rationale": "The cost reduction is large and the method runs on modest infrastructure, but it requires logged multi-turn trajectories with outcome labels and workload-specific validation before production adoption.",
  "technique": "Response-informed value estimation for multi-turn LLM routing",
  "family": "serving_e_batching",
  "recommendation": "testar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23239",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "source_url": "https://arxiv.org/abs/2610.23239",
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      "source_url": "https://arxiv.org/abs/2610.23239",
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    }
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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",
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  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}