{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your workload might fit a smaller or differently structured model.",
    "change": "Check for available weights and a compatible runtime before planning a product trial.",
    "baseline": "Your current model on identical held-out tasks and the same resource budget.",
    "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": "Task success, latency, memory, context limits, and migration cost.",
    "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": "2610.12582",
  "title": "One Model to Loop Them All: Elastic Latent Recurrence for Language Models",
  "summary": "The method replaces the standard practice of serving a fixed-depth non-recurrent transformer (or maintaining separate checkpoints per accuracy/latency tier) with a single recurrently applied block whose loop count is chosen at inference, while removing the usual one-loop accuracy penalty through gated state updates, mixed loop counts in training, and loop-index conditioning. The cost is that the gains require modifying pretraining: the recipe must be trained in, and all reported results come from models the authors trained themselves, including a late-stage mixed-depth schedule that still adds training compute. It can fail for a constrained-infrastructure adopter because no pretrained foundation model can be retrofitted with ELR via fine-tuning alone with validated results, the accuracy-per-loop gains on downstream production workloads are unverified outside the authors' benchmarks, and multi-loop serving multiplies per-request latency and compute on a single 24 GB GPU where a 4B-class model is already near the practical ceiling.",
  "rationale": "The results are compelling but only reproducible by training from pretraining, which the reader cannot do; adoption is only rational if the authors release the Koi-4B checkpoint, so the correct stance is to monitor for a release rather than invest now.",
  "technique": "Elastic latent recurrence: one checkpoint, variable loop depth",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12582",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 126,
  "citations": 0,
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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