{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time 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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2609.21997",
  "title": "Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents",
  "summary": "It replaces implicit, in-context opinion revision in LLM-agent social simulations with an explicit probabilistic belief layer in which each stance is a probability updated by one Bayesian step per utterance and a single prior-strength parameter kappa controls stubbornness. The cost is minimal compute (no training, one Bayesian update per utterance) but added architectural complexity: belief state must be maintained and injected into prompts separately from generation, decoupling what agents believe from how they speak. It can fail if the language round-trip distorts prescribed kappa on untested models, if per-model stance biases surfaced by the layer dominate the dynamics, or if FJ-style dynamics are not the correct model for the target social phenomenon.",
  "rationale": "The method is cheap and API-compatible, making it directly usable on constrained infrastructure, but its value is specific to social-simulation and opinion-dynamics workloads, so adoption should follow validation on the reader's own models and scenarios.",
  "technique": "Bayesian belief layer for LLM agent opinions",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-09-18",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.21997",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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