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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": "2610.29328",
  "title": "Seeing the Forest for the Trees: Emergent Agreement from Individual Conversations",
  "summary": "This is an interpretability and analysis method, not a production component: it replaces ad hoc monitoring of multi-agent agreement with linear probes on activations plus cross-conversation aggregation and a Markov model of turn-level agreement. The cost is the instrumentation itself: activation capture, probe training, and aggregation infrastructure, with no claimed gain in speed, memory, cost, or task quality. It can fail for deployment purposes because the per-conversation internal signal is explicitly unreliable, and the findings are descriptive of model behavior rather than a mechanism for controlling or improving it.",
  "rationale": "The paper offers scientific insight into how LLM pairs converge to agreement but proposes no deployable technique with a quantified benefit, so it merits awareness rather than adoption on constrained infrastructure.",
  "technique": "Aggregated internal probing of multi-agent agreement dynamics",
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  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-25",
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  "source_url": "https://arxiv.org/abs/2610.29328",
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