{
  "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": "2609.37717",
  "title": "Predictive Geometry of Hidden Trajectories in Transformers",
  "summary": "The method replaces attention-magnitude heuristics for token pruning and uniform rank allocation with a loss-aware, Fisher-weighted tokenwise curvature score and layerwise observable-subspace ranks computed from the terminal-loss geometry. It costs matrix-free Jacobian-vector and vector-Jacobian products through the remaining transformer blocks per analyzed token, adding implementation complexity and extra backward-pass compute during model analysis and distillation setup, though inference itself is unaffected. It can fail off successful low-loss trajectories, since the Fisher characterization is local and the pruning or rank-allocation signal may misestimate sensitivity on out-of-distribution inputs or when the low-loss residual terms are not small.",
  "rationale": "The signals are computable on a single 24 GB GPU with matrix-free autodiff products, but the abstract provides no quantified speedup or quality delta, so production adoption requires workload-specific validation against simpler pruning and distillation baselines.",
  "technique": "Fisher-geometry tokenwise curvature for pruning and distillation",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37717",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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