{
  "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": "2610.06677",
  "title": "How Sparse Probability Maps Shape Mixture-of-Experts Routing",
  "summary": "The technique replaces the standard softmax top-K MoE router with sparsity-inducing probability maps (sparsemax, entmax, normmax) that can assign exact zeros and adapt expert participation per token. It costs nothing at inference but requires pretraining the MoE from scratch, and the paper's central finding is that routers co-adapt their score distributions to the map, so adaptive sparsity often does not survive training and no map beats softmax validation loss. What can fail is the premise itself: entmax learns score spreads that keep gaps below its zeroing threshold, so the expected token-dependent participation may never materialize, making map choice alone an unreliable design lever.",
  "rationale": "Adopting this requires training MoE models from scratch, which exceeds the reader's budget, and since there is no quality gain over softmax the only actionable lesson is conceptual: sparse routing behavior depends on learned score distributions, not the map alone.",
  "technique": "Sparse probability maps for MoE routing",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06677",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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