{
  "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": "2609.24289",
  "title": "TTSE: A Two-Track Online Self-Evolution Framework",
  "summary": "TTSE replaces static, externally supplied environment knowledge with two separately evolving stores: FACT, environment facts continuously verified against interaction evidence, and TIP, task-conditioned execution procedures, both injected at inference via retrieval. The cost is an additional online memory pipeline (fact verification, procedure extraction, retrieval injection) on top of the agent loop, plus the engineering to maintain and validate these stores; no training of the base model is required, so it is feasible on a single GPU or API-only setup. What can fail: incorrect or stale facts being verified into the FACT store, cross-condition mismatch when TIP procedures transfer poorly to new tasks, and unbounded memory growth or retrieval noise degrading rather than improving task success.",
  "rationale": "The abstract reports qualitative superiority on ALFWorld, ScienceWorld, SOPBench, and PinchBench without auditable numbers, so the approach is plausible for small-scale agent deployments but requires validation on the reader's own workload before production adoption.",
  "technique": "Dual-track online agent self-evolution (FACT/TIP)",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24289",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TTSE replaces static, externally supplied environment knowledge with two separately evolving stores: FACT, environment facts continuously verified against interaction evidence, and TIP, task-conditioned execution procedures, both injected at inference via retrieval. The cost is an additional online memory pipeline (fact verification, procedure extraction, retrieval injection) on top of the agent loop, plus the engineering to maintain and validate these stores; no training of the base model is required, so it is feasible on a single GPU or API-only setup. What can fail: incorrect or stale facts being verified into the FACT store, cross-condition mismatch when TIP procedures transfer poorly to new tasks, and unbounded memory growth or retrieval noise degrading rather than improving task success.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.24289",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "finding": "",
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "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": ""
}