{
  "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": "2609.16635",
  "title": "EchoPath: Execution-Level Replayable Memory for GUI Agents",
  "summary": "EchoPath replaces repeated fresh observe-plan-ground-act loops for recurrent GUI tasks with validated, parameterized, callable memories that are deterministically replayed via image-based target re-aiming. The cost is building and maintaining an artifact-validated trajectory store, plus the engineering complexity of precondition checks, input rebinding, and fallback to bounded grounding repair or fresh planning when replay is rejected. Replay can fail when the GUI changes beyond what coordinate re-aiming can correct, when stored state preconditions no longer match the runtime, or when tasks are not actually recurrent enough to amortize memory creation.",
  "rationale": "The mechanism is model-agnostic, requires no GPU or training, and directly cuts API token spend for repetitive enterprise GUI workflows, but its benefit depends entirely on having a stable, recurrent workload, so it must be validated on the reader's own tasks before production adoption.",
  "technique": "Replayable validated GUI trajectories with visual re-aiming",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.16635",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "EchoPath replaces repeated fresh observe-plan-ground-act loops for recurrent GUI tasks with validated, parameterized, callable memories that are deterministically replayed via image-based target re-aiming. The cost is building and maintaining an artifact-validated trajectory store, plus the engineering complexity of precondition checks, input rebinding, and fallback to bounded grounding repair or fresh planning when replay is rejected. Replay can fail when the GUI changes beyond what coordinate re-aiming can correct, when stored state preconditions no longer match the runtime, or when tasks are not actually recurrent enough to amortize memory creation.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.16635",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Reduced median token cost by more than 90% and median execution time by about 60% on real computer-use tasks.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.16635",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "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,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "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,
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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": "selector_failed",
  "core_kind": "",
  "equations_fetched_at": "2026-10-15",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}