{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2609.12412",
  "title": "HoliBench: A Cross-Platform Benchmarking and Deployment Toolkit for Foundation Models in CPS-IoT Applications",
  "summary": "HoliBench replaces ad-hoc, platform-specific profiling scripts and accuracy-only evaluation harnesses with a unified toolkit that measures accuracy, latency, and energy once per device and reuses those profiles for constraint-aware deployment selection. The cost is an upfront profiling campaign per device-model-backend combination, integration effort with its abstraction layer, and the engineering overhead of maintaining an open-source toolkit rather than a model-level optimization. It can fail if the reader's targets are only a single 24 GB GPU or third-party APIs (where cross-device calibration adds little), if profile composition breaks down under concurrent rather than sequential co-resident execution, or if measurement drift and quantization-backend interactions make reused profiles stale.",
  "rationale": "The toolkit is directly usable on constrained infrastructure and its compose-once profiles can eliminate repeated profiling, but its value depends on whether the reader actually deploys across heterogeneous edge devices rather than a single GPU or APIs, so workload-specific validation is warranted before adoption.",
  "technique": "Cross-platform deployment benchmarking and profiling toolkit",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-09-11",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.12412",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "HoliBench replaces ad-hoc, platform-specific profiling scripts and accuracy-only evaluation harnesses with a unified toolkit that measures accuracy, latency, and energy once per device and reuses those profiles for constraint-aware deployment selection. The cost is an upfront profiling campaign per device-model-backend combination, integration effort with its abstraction layer, and the engineering overhead of maintaining an open-source toolkit rather than a model-level optimization. It can fail if the reader's targets are only a single 24 GB GPU or third-party APIs (where cross-device calibration adds little), if profile composition breaks down under concurrent rather than sequential co-resident execution, or if measurement drift and quantization-backend interactions make reused profiles stale.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.12412",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Standalone single-model profiles predict multi-model pipeline latency within 1.2% and power within 2.5% under sequential co-resident execution; other findings are qualitative (e.g., quantization reduces latency only on low-precision hardware, autoregressive average power is approximately constant across output lengths).",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.12412",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [
    {
      "anchor": "S3.E4",
      "label": "(4)",
      "section": "3. System Architecture",
      "role": "proposed_method",
      "latex": "\\text{Maximize }U_{i}=\\sum_{j}w_{j}\\hat{v}_{i,j}\\quad\\text{subject to:}",
      "mathml": "<math display=\"block\"><mrow><mtext>Maximize </mtext><mi>U</mi><msub><mrow></mrow><mi>i</mi></msub><mo rspace=\"0.111em\">=</mo><mo movablelimits=\"false\">∑</mo><msub><mrow></mrow><mi>j</mi></msub><mi>w</mi><msub><mrow></mrow><mi>j</mi></msub><mover accent=\"true\"><mi>v</mi><mo>^</mo></mover><msub><mrow></mrow><mrow><mi>i</mi><mo>,</mo><mi>j</mi></mrow></msub><mtext>subject to:</mtext></mrow></math>",
      "context": "The second stage is a pluggable solver. The solver interface accepts any implementation conforming to the contract solve(candidate_set, constraints, weights) <math display=\"inline\"><mo stretchy=\"false\">→</mo></math> ranked_configurations, separating the calibrated measurement infrastructure (the profile table) from the selection policy applied to it. HoliBench ships with an Integer Linear Programming (ILP) solver as the representative implementation. The ILP defines a weighted utility <math display=\"inline\"><msub><mi>U</mi><mi>i</mi></msub></math> over normalized metrics for each configuration <math display=\"inline\"><mrow><mi>i</mi><mo>∈</mo><mi>𝒞</mi></mrow></math> and selects the configuration maximizing <math display=\"inline\"><msub><mi>U</mi><mi>i</mi></msub></math>, subject to the user-specified deployment constraints. Formally,",
      "headline": "",
      "explanation": "",
      "objective": "",
      "symbols": {},
      "evidence": "",
      "evidence_url": "",
      "source_url": ""
    },
    {
      "anchor": "S3.Ex1",
      "label": "",
      "section": "3. System Architecture",
      "role": "complexity",
      "latex": "l_{i}\\leq L_{\\max},\\quad e_{i}\\leq E_{\\max},\\quad m_{i}\\leq M_{\\max},\\quad a_{i}\\geq A_{\\min}",
      "mathml": "<math display=\"block\"><mrow><mrow><msub><mi>l</mi><mi>i</mi></msub><mo>≤</mo><msub><mi>L</mi><mi>max</mi></msub></mrow><mo rspace=\"1.167em\">,</mo><mrow><msub><mi>e</mi><mi>i</mi></msub><mo>≤</mo><msub><mi>E</mi><mi>max</mi></msub></mrow><mo rspace=\"1.167em\">,</mo><mrow><msub><mi>m</mi><mi>i</mi></msub><mo>≤</mo><msub><mi>M</mi><mi>max</mi></msub></mrow><mo rspace=\"1.167em\">,</mo><mrow><msub><mi>a</mi><mi>i</mi></msub><mo>≥</mo><msub><mi>A</mi><mi>min</mi></msub></mrow></mrow></math>",
      "context": "The second stage is a pluggable solver. The solver interface accepts any implementation conforming to the contract solve(candidate_set, constraints, weights) <math display=\"inline\"><mo stretchy=\"false\">→</mo></math> ranked_configurations, separating the calibrated measurement infrastructure (the profile table) from the selection policy applied to it. HoliBench ships with an Integer Linear Programming (ILP) solver as the representative implementation. The ILP defines a weighted utility <math display=\"inline\"><msub><mi>U</mi><mi>i</mi></msub></math> over normalized metrics for each configuration <math display=\"inline\"><mrow><mi>i</mi><mo>∈</mo><mi>𝒞</mi></mrow></math> and selects the configuration maximizing <math display=\"inline\"><msub><mi>U</mi><mi>i</mi></msub></math>, subject to the user-specified deployment constraints. Formally,",
      "headline": "",
      "explanation": "",
      "objective": "",
      "symbols": {},
      "evidence": "",
      "evidence_url": "",
      "source_url": ""
    }
  ],
  "equations_status": "selected",
  "core_kind": "formula",
  "equations_fetched_at": "2026-09-29",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}