{
  "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": "2610.13021",
  "title": "EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents",
  "summary": "EmbodiedCLBench replaces ad hoc or non-embodied continual-learning evaluation with a compositional benchmark in embodied simulation, testing whether skills from basic tasks recombine to solve unseen advanced tasks. Adopting it costs embodied-simulator infrastructure, agent harness integration, and evaluation compute rather than a single API call, and it yields a measurement tool rather than a capability improvement. It can fail to transfer: results are confined to the benchmark's simulated tasks, and its central finding that agents cannot extract value from additional experience may not hold for methods or domains outside those tested.",
  "rationale": "The paper contributes an evaluation benchmark, not a deployable method, and its embodied-simulation setting is relevant only if the reader builds self-evolving embodied agents; otherwise it is worth monitoring for its finding that current agents do not benefit from additional experience.",
  "technique": "Benchmark for continual learning in embodied agents",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13021",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 365,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.13021",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The paper reports qualitative findings only: experience from basic tasks yields consistent improvement on unseen advanced tasks over zero-shot, the benefit remains stable as task complexity increases, but it does not scale with the number of learning samples; no multiplicative factor is stated.",
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      "source_url": "https://arxiv.org/abs/2610.13021",
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      "source_excerpt": ""
    }
  ],
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      "next_check": "",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "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": ""
}