{ "schema_version": 1, "builder_plan": { "scenario": "Your workload might fit a smaller or differently structured model.", "change": "Check for available weights and a compatible runtime before planning a product trial.", "baseline": "Your current model on identical held-out tasks and the same resource 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": "Task success, latency, memory, context limits, and migration cost.", "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": "2607.12599", "title": "Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices", "summary": "Replaces heavy deep-learning time-series anomaly detectors with a small autoencoder using DWT multiscale features and a multiscale loss. It requires a wavelet pipeline and training for the series or domain, and is limited to univariate series. Benefits need validation on the reader's data, especially for subtle anomalies or workloads unlike the benchmarks; multivariate data is outside the stated scope.", "rationale": "The small model and modest training requirements fit limited infrastructure, but the gains depend on the specific anomaly-detection task and data.", "technique": "Wavelet multiscale autoencoder for anomaly detection", "family": "arquitetura_eficiente", "recommendation": "testar", "published": "2026-07-14", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2607.12599", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "Replaces heavy deep-learning time-series anomaly detectors with a small autoencoder using DWT multiscale features and a multiscale loss. It requires a wavelet pipeline and training for the series or domain, and is limited to univariate series. Benefits need validation on the reader's data, especially for subtle anomalies or workloads unlike the benchmarks; multivariate data is outside the stated scope.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2607.12599", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Reports 9x lower inference latency and 2x lower energy use on Jetson Nano, with a model smaller than 500 KB and competitive or better performance.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2607.12599", "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": "S4.E16", "label": "(16)", "section": "4 Design Methodology", "role": "loss", "latex": "\\mathcal{L}_{total}=\\beta||x-\\hat{x}||_{2}+\\sum_{k=1}^{L}\\lambda_{k}\\mathcal{L}_{h}^{k}+\\gamma\\mathcal{L}_{l}^{L}", "mathml": "ℒt​o​t​a​l=β​‖x−x^‖2+∑k=1Lλk​ℒhk+γ​ℒlL", "context": "", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S4.E14", "label": "(14)", "section": "4 Design Methodology", "role": "proposed_method", "latex": "\\mathcal{L}_{h}^{k}=||y_{h}^{k}-\\hat{y}_{h}^{k}||_{2}", "mathml": "ℒhk=‖yhk−y^hk‖2", "context": "", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S4.E15", "label": "(15)", "section": "4 Design Methodology", "role": "proposed_method", "latex": "\\mathcal{L}_{l}^{L}=||y_{l}^{L}-\\hat{y}_{l}^{L}||_{2}", "mathml": "ℒlL=‖ylL−y^lL‖2", "context": "", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" } ], "equations_status": "selected", "core_kind": "formula", "equations_fetched_at": "2026-09-13", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }