{
  "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": "2609.37743",
  "title": "ContextRender: From Execution Dependencies to Agent Context",
  "summary": "It replaces passing the full tool-result history (or recency/relevance-only truncation) with a renderer that selects results using observed reuse from tool-flow analysis, recency, and semantic relevance under a fixed token budget. The cost is engineering complexity: building and maintaining a persistent execution-dependency graph and a tool-flow analysis pass around the agent loop, plus minor per-step selection overhead. It can fail when the dependency analysis misses implicit reuse of earlier results, when tasks lack clear tool-result dependency structure, or when the 6K budget assumption does not transfer to the reader's workload and models.",
  "rationale": "The cost savings at equal task quality are directly relevant to API-budget-constrained agent deployments, but the method requires implementing the dependency-graph and rendering pipeline and validating it on the reader's own tasks before production adoption.",
  "technique": "Dependency-graph context rendering for LLM agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37743",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
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}