{
  "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": "2610.30108",
  "title": "Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use",
  "summary": "This survey introduces PAPAV, a taxonomy that replaces architecture-specific comparisons of computer-use and robot-use agents with five functional capabilities (Perceive, Anticipate, Plan, Act, Verify), so it is an analytical lens rather than a deployable method. It costs nothing to run but offers no model, code, or benchmark improvement; its value is limited to framing evaluation and design. Its survey finding that only 5 of 64 benchmarks assess Anticipate and 3 assess Verify indicates current agent evaluations over-weight task success, but adopting the framework cannot itself close those gaps and may not map cleanly onto an engineer's existing evaluation harness.",
  "rationale": "The paper is a conceptual framework and benchmark survey with no deployable artifact, so it is useful for thinking about evaluation coverage but not something to adopt or test on a 24 GB GPU.",
  "technique": "PAPAV capability-centric framework for embodied agents",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-22",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30108",
  "independent_implementations": 4,
  "total_implementations": 5,
  "stars": 371,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This survey introduces PAPAV, a taxonomy that replaces architecture-specific comparisons of computer-use and robot-use agents with five functional capabilities (Perceive, Anticipate, Plan, Act, Verify), so it is an analytical lens rather than a deployable method. It costs nothing to run but offers no model, code, or benchmark improvement; its value is limited to framing evaluation and design. Its survey finding that only 5 of 64 benchmarks assess Anticipate and 3 assess Verify indicates current agent evaluations over-weight task success, but adopting the framework cannot itself close those gaps and may not map cleanly onto an engineer's existing evaluation harness.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30108",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
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      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}