{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "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": "Correct completed actions, invalid calls, retries, latency, and cost per completed 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": "2609.35463",
  "title": "A.D.A.M.O. (Agent for language-Driven Actions with Multimodal Observations): A Visual-Symbolic Framework for Virtual Humans",
  "summary": "It replaces hand-wired perception, symbolic-state tracking, and action-selection modules for virtual humans with a pretrained VLM that calls tools over a dual egocentric-visual and symbolic world model. The cost is dependence on a capable VLM API or local model, instrumented 3D scenes with synchronized symbolic state and semantic labels, tool schemas, and added control-loop latency rather than model training. Adoption can fail through brittle semantic labeling, wrong or drifting symbolic state, tool-call and execution errors in the 3D environment, and limited evidence beyond controlled scenes.",
  "rationale": "The pattern is feasible on constrained infrastructure because it uses pretrained VLMs and tool calling, but the validated setting is narrow virtual-human control, so production adoption should wait for workload-specific embodied-agent evidence.",
  "technique": "VLM tool-calling visual-symbolic control loop",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35463",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
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      "claim_id": "claim-01",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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    {
      "dimension": "reproducibility",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}