{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token 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 patches, regression failures, human repair time, and total cost per accepted change.",
    "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": "2610.30760",
  "title": "AgentDP: Towards Automated Model Deployment for Heterogeneous Embedded AI Applications",
  "summary": "AgentDP replaces the manual process of optimizing and adapting pre-trained AI models for heterogeneous embedded devices by using LLM agents to automate requirement elicitation, operator-aware model partitioning, and hyperparameter configuration reuse. The approach introduces dependency on LLM inference costs and requires integration of device-specific domain knowledge into the agent workflow, adding architectural complexity to the deployment pipeline. Failures can occur when the user requirement standardization misinterprets deployment constraints, when operator-aware partitioning selects incompatible execution engines, or when reused configurations from previous tasks do not transfer to new optimization targets.",
  "rationale": "This is a research framework targeting embedded deployment engineering rather than a production-ready tool, requiring infrastructure for LLM agent orchestration and validation on specific target hardware before operational use.",
  "technique": "LLM-Agent Automated Model Deployment",
  "family": "agentes_de_codigo",
  "recommendation": "observar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30760",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
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      "dimension": "compatibility",
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    {
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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",
  "core_kind": "",
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  "equations_source_url": "",
  "equations_source_sha256": "",
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}