{
  "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": "2609.21523",
  "title": "What Must Survive? Exact Task-Information--State Frontiers for Resource-Sufficient Learning",
  "summary": "The paper characterizes, rather than implements, the exact minimal state dimension for linear task families when a task message is revealed before compression, replacing heuristic dimensionality reduction with a provable partition-based frontier. It costs solving a strongly NP-hard partition problem (inapproximable at any fixed tolerance), so practical use requires approximate singular-value heuristics whose gap to the exact frontier is instance-dependent. Adoption can fail because the results hold only for finite families of linear tasks, the demonstrated gains come from constructed examples rather than production models, and real neural workloads rarely expose the required joint task operators in advance.",
  "rationale": "This is a theoretical optimality result for linear-task compression with NP-hard computation, offering design insight but no validated, deployable method for constrained-infrastructure production use.",
  "technique": "Task-advised state compression via low-rank partition frontiers",
  "family": "esparsidade_e_poda",
  "recommendation": "observar",
  "published": "2026-09-18",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.21523",
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  "total_implementations": 0,
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  "citations": 0,
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