{
  "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": "2609.24971",
  "title": "DolphinBench: Mapping the Pareto Frontier of Agent Memory",
  "summary": "DolphinBench replaces conversational QA-style memory benchmarks with task-completion evaluation over roughly 500k tokens of persona history per knowledge-work persona, requiring agents to succeed with relevant history and fail without it, and mandating cost and latency reporting alongside accuracy. Adoption costs the engineering effort of integrating the benchmark's harness and history into the reader's evaluation pipeline, plus the compute and API spend of running 200 tasks per persona, which is nontrivial on a limited cloud budget. It can fail to transfer if the reader's production tasks differ from the three included personas, and a memory system that scores well on the benchmark's Pareto frontier may still underperform on domain-specific recall patterns.",
  "rationale": "The reader building production agents with memory needs a cost-aware evaluation tool, and this benchmark is directly usable at small scale, but its three fixed personas require validation against the reader's actual workload before driving adoption decisions.",
  "technique": "Task-completion benchmark for agent memory with cost and latency reporting",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24971",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 3,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DolphinBench replaces conversational QA-style memory benchmarks with task-completion evaluation over roughly 500k tokens of persona history per knowledge-work persona, requiring agents to succeed with relevant history and fail without it, and mandating cost and latency reporting alongside accuracy. Adoption costs the engineering effort of integrating the benchmark's harness and history into the reader's evaluation pipeline, plus the compute and API spend of running 200 tasks per persona, which is nontrivial on a limited cloud budget. It can fail to transfer if the reader's production tasks differ from the three included personas, and a memory system that scores well on the benchmark's Pareto frontier may still underperform on domain-specific recall patterns.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.24971",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "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",
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      "finding": "",
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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": ""
}