{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "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": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "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": "2610.08884",
  "title": "Task-Sufficient Contraction: Source Selection for Machine Information Interfaces",
  "summary": "The method replaces ad hoc task-agnostic compression or task-specific retraining of representations with a canonical reduced source, fixed before the encoder or rate is chosen, that merges states only when every action has identical regret, provably preserving the full rate-regret curve (and, for quadratic loss, reducing to projection onto directions in which feasible actions differ). It costs an information-theoretic analysis of the action set, loss, and feasible-action geometry per consumer task, plus a separate contraction per downstream task, and the paper offers theorems rather than implementations or benchmarks. It fails when the task's loss or action set is mis-specified, when merging is done too coarsely (the paper shows retaining only the optimal water-filled action loses information), or when approximate contractions are treated as exact.",
  "rationale": "This is a theoretical rate-distortion and Information-Bottleneck characterization with no algorithm, code, or empirical results, so it is relevant background for representation compression but not deployable on a single-GPU production stack today.",
  "technique": "Task-sufficient source contraction via regret-equivalence merging",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.08884",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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    {
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      "source_url": "https://arxiv.org/abs/2610.08884",
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    }
  ],
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    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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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": "",
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}