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    "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": "2609.27123",
  "title": "PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception",
  "summary": "PEARL replaces per-agent-type interpreter retraining and configuration sharing in collaborative vehicle perception with two lightweight multi-scale interpreters using low-rank visual prompts, plus a real-time interpreter-selection step for anonymously joining agents. It costs the training and maintenance of two parallel interpreters and adds a small selection latency (1.67 ms), while keeping communication and compute low through low-rank prompts. It can fail when new agents' sensor domains fall outside what the domain-invariant interpreter was trained to cover, and all reported gains come from driving-perception benchmarks (OPV2V, V2XSet, DAIR-V2X), so transfer to other collaborative settings is unverified.",
  "rationale": "The method targets multi-agent vehicle perception, not LLM inference or agent workflows, so it is only relevant to the reader if they work on cooperative perception systems; otherwise it is outside their production scope despite its efficiency.",
  "technique": "Prompt-based interpreters for heterogeneous collaborative perception",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27123",
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  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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