{
  "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": "2610.03394",
  "title": "EdgeAgent: Orchestrating On-Device LLM inference for End-User Multi-Agent Systems on CPU-GPU Unified Memory Architectures",
  "summary": "EdgeAgent replaces naive batched speculative decoding and static scheduling on unified-memory edge SoCs with zero-copy CPU-GPU tensor parallelism, dynamic speculative draft budgets, and suspend-and-yield eviction of tool-stalled agents. It costs substantial engineering complexity: bypassing graph compilers, implementing asymmetric memory layouts, and building agent-aware scheduling, all tied to UMA hardware. It can fail when ported to discrete-GPU or API-based deployments where UMA contention does not exist, and gains shrink if workloads lack tool stalls or high drafting variance.",
  "rationale": "The system targets Apple-silicon UMA hardware with custom low-level execution, which the reader's 24 GB GPU or API-based infrastructure cannot run without a full reimplementation whose benefits may not transfer.",
  "technique": "UMA-aware multi-agent LLM inference orchestration",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03394",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 12,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "EdgeAgent replaces naive batched speculative decoding and static scheduling on unified-memory edge SoCs with zero-copy CPU-GPU tensor parallelism, dynamic speculative draft budgets, and suspend-and-yield eviction of tool-stalled agents. It costs substantial engineering complexity: bypassing graph compilers, implementing asymmetric memory layouts, and building agent-aware scheduling, all tied to UMA hardware. It can fail when ported to discrete-GPU or API-based deployments where UMA contention does not exist, and gains shrink if workloads lack tool stalls or high drafting variance.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03394",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "1.77x speedup over batched speculative decoding under extreme tool-use latencies on an Apple M4 SoC; UMA-aware execution alone contributes 1.29x.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03394",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}