{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per 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": "2609.37267",
  "title": "Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym",
  "summary": "This work replaces ad-hoc design and single-dimension evaluation of proactive LLM agents with a joint Task Capability–Temporal Allocation–Trust (3T) framework, a five-dimension design space, and Proactivity-Gym, a simulation testbed with multi-day stateful scenarios and persona-conditioned simulated users. It costs no GPU training but requires building persona-based user simulators and stateful multi-day environments, plus human validation, since the paper shows LLM judges conflate task capability with trust. What can fail after adoption: simulated users may not predict real user trust dynamics, evaluation results across harnesses may not transfer to the reader's domain, and optimizing one 3T axis alone (e.g., correct output) can still produce sharp trust declines from misaligned interventions.",
  "rationale": "The paper contributes a design framework and evaluation testbed rather than a deployable technique, and its main actionable finding (evaluate trust and timing jointly, prefer sleep-time assistance) can inform design choices without adopting the full simulation infrastructure.",
  "technique": "3T framework and simulation testbed for proactive agents",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37267",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This work replaces ad-hoc design and single-dimension evaluation of proactive LLM agents with a joint Task Capability–Temporal Allocation–Trust (3T) framework, a five-dimension design space, and Proactivity-Gym, a simulation testbed with multi-day stateful scenarios and persona-conditioned simulated users. It costs no GPU training but requires building persona-based user simulators and stateful multi-day environments, plus human validation, since the paper shows LLM judges conflate task capability with trust. What can fail after adoption: simulated users may not predict real user trust dynamics, evaluation results across harnesses may not transfer to the reader's domain, and optimizing one 3T axis alone (e.g., correct output) can still produce sharp trust declines from misaligned interventions.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.37267",
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      "source_excerpt": ""
    }
  ],
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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": "",
  "exposures_source_sha256": ""
}