{
  "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": "2609.28520",
  "title": "Certified Task-Conditioned Active Observability",
  "summary": "This formalizes and replaces classical binary observability with a task-conditioned active probing framework that identifies only task-relevant latent states under certified error and safe-abstention guarantees, using adaptive distinguishing trees and relative-entropy lower bounds. It costs implementation of a staged verifier, hypothesis-pruning score shell, and adaptive probing logic, plus interaction budget for physical interventions on the target system. It applies to autonomous agents acting on unobservable physical systems and can fail if the task-predictive equivalence relation is misspecified, if noise models underlying the certificates are wrong, or if the domain is software/LLM inference rather than embodied sensing.",
  "rationale": "The method targets certified state recovery in physical autonomous systems, not LLM training, inference, or agent tooling, so it falls outside the production concerns of a constrained-infrastructure AI/ML engineer.",
  "technique": "Task-conditioned active observability with certified abstention",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28520",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This formalizes and replaces classical binary observability with a task-conditioned active probing framework that identifies only task-relevant latent states under certified error and safe-abstention guarantees, using adaptive distinguishing trees and relative-entropy lower bounds. It costs implementation of a staged verifier, hypothesis-pruning score shell, and adaptive probing logic, plus interaction budget for physical interventions on the target system. It applies to autonomous agents acting on unobservable physical systems and can fail if the task-predictive equivalence relation is misspecified, if noise models underlying the certificates are wrong, or if the domain is software/LLM inference rather than embodied sensing.",
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      "source_url": "https://arxiv.org/abs/2609.28520",
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    }
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      "dimension": "security",
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    },
    {
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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": "",
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}