{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2610.01415",
  "title": "Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States",
  "summary": "PoS replaces passive history retention and context compression with an explicitly maintained belief state combining current world-state estimates and unresolved task requirements, plus consistency validation and trapping detection with tailored recovery. The cost is additional inference-time LLM calls for belief construction, validation, and progress monitoring, which increases token spend and latency per step and adds orchestration complexity. Failure modes include belief-state corruption from validation errors propagating through subsequent decisions, misclassified trapping patterns triggering inappropriate recovery, and reliance on backbone LLM quality for belief consistency that may not transfer to domains outside the tested benchmarks.",
  "rationale": "The approach is inference-time and API-compatible, fitting constrained infrastructure, but gains are reported as benchmark rankings without quantified factors, so workload-specific validation on the reader's own agent tasks is required before production adoption.",
  "technique": "Explicit belief states for long-horizon agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01415",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 148,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PoS replaces passive history retention and context compression with an explicitly maintained belief state combining current world-state estimates and unresolved task requirements, plus consistency validation and trapping detection with tailored recovery. The cost is additional inference-time LLM calls for belief construction, validation, and progress monitoring, which increases token spend and latency per step and adds orchestration complexity. Failure modes include belief-state corruption from validation errors propagating through subsequent decisions, misclassified trapping patterns triggering inappropriate recovery, and reliance on backbone LLM quality for belief consistency that may not transfer to domains outside the tested benchmarks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01415",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Highest overall performance on all four benchmarks with all three LLM backbones; no multiplicative factor reported in the abstract.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01415",
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    }
  ],
  "exposure_map": [
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      "finding": "",
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "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,
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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": ""
}