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    "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."
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  "builder_review": null,
  "arxiv_id": "2609.20510",
  "title": "Truncated automatic sparse differentiation for machine learning interatomic potentials",
  "summary": "The method replaces dense automatic differentiation of MLIP energies, which makes full Hessians computationally inaccessible for large systems, with sparse differentiation that exploits the finite receptive field of message-passing potentials, optionally truncating small long-range Hessian entries. Exact ASD requires computing and exploiting the sparsity pattern and delivers only modest speedups, while the practical gains come from truncated ASD, which introduces approximation by discarding nonzero entries between distant atoms and adds implementation complexity. The approach can fail when truncated couplings are not negligible for a target observable, when the MLIP lacks strict locality so the assumed sparsity does not hold, or when graph construction overhead erodes the speedup at the system sizes of interest.",
  "rationale": "The technique is implementable on modest hardware and the claimed speedup is substantial, but it applies only if the reader's production workload involves Hessians of interatomic potentials, and the truncation threshold must be validated against the specific observables and model architecture in use.",
  "technique": "Truncated automatic sparse differentiation for MLIP Hessians",
  "family": "esparsidade_e_poda",
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  "published": "2026-09-17",
  "as_of": "2026-10-18",
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  "source_url": "https://arxiv.org/abs/2609.20510",
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