{
  "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": "2609.35427",
  "title": "LLMs are General Asynchronous Agents",
  "summary": "The framework replaces sequential read-think-act agent loops and task-specific async architectures (voice pipelines, VLAs, async tool-calling) with user- or agent-defined inference coroutines that maintain overlapping memory states on a single general-purpose LLM. It costs added orchestration complexity around inference, requires careful management of shared and diverging context states across coroutines, and adds memory overhead proportional to the number of concurrent coroutines, with no training cost since it works on off-the-shelf Qwen 3.x models. It can fail through context interference between overlapping memory states, race conditions when new inputs arrive mid-reasoning, and degraded output quality on models not shown to tolerate this inference pattern.",
  "rationale": "The approach runs on existing open-weight models without training and suits constrained infrastructure, but the abstract offers no quantified latency, cost, or quality results, so production value depends on validating concurrency behavior and state isolation in the reader's specific workload.",
  "technique": "Asynchronous LLM inference coroutines with overlapping memory states",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35427",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 2,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.35427",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
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    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "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": "",
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}