{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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 quality, actual GPU memory, serving throughput, and preparation 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": "2610.12520",
  "title": "CircuitTree: Composing Circuits into A Syllable Transcriber",
  "summary": "CircuitTree replaces a monolithic task-specific model with a hierarchy composed from many small subnetworks pruned out of a pretrained backbone (HuBERT), each circuit handling an identifiable subtask such as single-phoneme classification. The cost is a dedicated circuit-discovery and pruning pipeline, a greedy density-scaled composition procedure with Theta(N log N) scheduling overhead, and a small residual quality gap (1.2 accuracy points) against a non-composed ceiling. Adoption can fail when source circuits do not decompose cleanly for the target domain, when greedy composition propagates interference between circuits, or when the interpretability benefit does not justify the engineering effort over simply fine-tuning a small model.",
  "rationale": "The method targets mechanistic interpretability and modular reuse in speech models rather than inference efficiency, and its circuit-discovery pipeline demands research-grade effort with no demonstrated latency, memory, or cost advantage on constrained production infrastructure.",
  "technique": "Greedy tree-based composition of pruned circuits",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12520",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "CircuitTree replaces a monolithic task-specific model with a hierarchy composed from many small subnetworks pruned out of a pretrained backbone (HuBERT), each circuit handling an identifiable subtask such as single-phoneme classification. The cost is a dedicated circuit-discovery and pruning pipeline, a greedy density-scaled composition procedure with Theta(N log N) scheduling overhead, and a small residual quality gap (1.2 accuracy points) against a non-composed ceiling. Adoption can fail when source circuits do not decompose cleanly for the target domain, when greedy composition propagates interference between circuits, or when the interpretability benefit does not justify the engineering effort over simply fine-tuning a small model.",
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      "claim_id": "claim-02",
      "statement": "The composed circuit hierarchy transcribes open syllables at 64.8% accuracy, within 1.2 points of a 66.0% ceiling model sharing no circuitry, while reusing 99.5% of circuit components across tasks.",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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}