{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful 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": "2609.26783",
  "title": "A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing",
  "summary": "The method replaces centralized-training centralized-coordinator multi-agent reinforcement learning for cooperative Dec-POMDPs: each team member independently learns an approximate low-rank MDP from local and delayed common information and runs least-squares value iteration, with theory showing member policies approximate the centralized team-optimal solution. The compute cost is modest and single-GPU compatible, but adoption requires problems with genuinely low-rank latent dynamics, tolerance of delayed information sharing, and the engineering effort to implement team-theoretic equivalence and LSVI from a theoretical paper rather than a maintained library. It can fail when latent dynamics are not low-rank, when delays exceed what the analysis assumes, or in non-cooperative settings, since all guarantees are asymptotic/finite-sample bounds for fully cooperative teams rather than demonstrated production benchmarks.",
  "rationale": "This is a control-theoretic decentralized MARL contribution with sample-complexity guarantees, not an LLM inference, training, or agent-orchestration technique, so it is only relevant if the reader's production work involves cooperative multi-agent control under partial observability.",
  "technique": "Decentralized low-rank Dec-POMDP learning with delayed information sharing",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26783",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces centralized-training centralized-coordinator multi-agent reinforcement learning for cooperative Dec-POMDPs: each team member independently learns an approximate low-rank MDP from local and delayed common information and runs least-squares value iteration, with theory showing member policies approximate the centralized team-optimal solution. The compute cost is modest and single-GPU compatible, but adoption requires problems with genuinely low-rank latent dynamics, tolerance of delayed information sharing, and the engineering effort to implement team-theoretic equivalence and LSVI from a theoretical paper rather than a maintained library. It can fail when latent dynamics are not low-rank, when delays exceed what the analysis assumes, or in non-cooperative settings, since all guarantees are asymptotic/finite-sample bounds for fully cooperative teams rather than demonstrated production benchmarks.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26783",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "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": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "next_check": "",
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      "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": ""
}