{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2609.40265",
  "title": "OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning",
  "summary": "OpenTSLM TeeMoE replaces the current fragmented setup of separate numerical forecasting models and language-based reasoning models with a single shared backbone serving three frozen LoRA experts (forecast aggregation, native forecasting, temporal analysis) weighted per request by a learned mixture-of-experts controller. The cost is training and serving a full language-model backbone plus the MoE controller, which exceeds a single 24 GB GPU budget for training and adds routing complexity and dependency on external specialist forecasters whose outputs it refines. It can fail if the frozen LoRA experts interact poorly through the controller on out-of-distribution series, if benchmark rankings do not transfer to the reader's domain-specific data, or if the released artifacts require infrastructure the reader does not have.",
  "rationale": "The method is a research-stage foundation model whose training and routing infrastructure exceed constrained infrastructure, and its benchmark rankings require domain-specific validation before any production adoption.",
  "technique": "LoRA mixture-of-experts time-series language model",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.40265",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "OpenTSLM TeeMoE replaces the current fragmented setup of separate numerical forecasting models and language-based reasoning models with a single shared backbone serving three frozen LoRA experts (forecast aggregation, native forecasting, temporal analysis) weighted per request by a learned mixture-of-experts controller. The cost is training and serving a full language-model backbone plus the MoE controller, which exceeds a single 24 GB GPU budget for training and adds routing complexity and dependency on external specialist forecasters whose outputs it refines. It can fail if the frozen LoRA experts interact poorly through the controller on out-of-distribution series, if benchmark rankings do not transfer to the reader's domain-specific data, or if the released artifacts require infrastructure the reader does not have.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.40265",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Ranks among the top three on GIFT-Eval by mean MASE rank, on Context is Key by RCRPS, and on TimeSeriesExam by accuracy; no multiplicative improvement factor is reported.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.40265",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "next_check": "",
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      "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,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "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": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}