{
  "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": "2609.28248",
  "title": "hyperbolix: Hyperbolic Deep Learning in JAX",
  "summary": "The library replaces ad hoc or PyTorch-only hyperbolic neural network implementations with a JAX-native library of six manifolds, layers (linear, convolution, attention, normalization, quantization), Riemannian optimizers as optax transforms, and cancellation-free distance formulas that avoid float32 NaNs where prior hyperboloid implementations fail. It costs adoption of a geometrically specialized stack: curvature must be managed at call time, operations act on single points and require vmap batching, and hyperbolic layers replace standard Euclidean layers without a demonstrated general accuracy gain in this paper. It can fail if the workload's data has no hierarchical or tree-like structure, in which case hyperbolic embeddings add numerical and engineering complexity without benefit, and if the team's codebase is PyTorch-based, requiring a JAX migration.",
  "rationale": "For a constrained-infrastructure engineer, hyperbolic deep learning is a niche representation choice rather than an efficiency technique, so it is only worth testing on workloads with explicitly hierarchical data where the paper's numerically stable float32 operations reduce implementation risk.",
  "technique": "Hyperbolic deep learning library in JAX (hyperbolix)",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28248",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2609.28248",
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      "source_excerpt": ""
    }
  ],
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    },
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      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "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": ""
}