{ "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": "2609.16358", "title": "EBL: Efficient Broad Learning for Distributed Adaptive Harmonic Analysis", "summary": "The method replaces backpropagation-trained harmonic estimators and prior FPGA-accelerated estimators with a quantised broad-learning system using closed-form solutions on FPGA for power-grid harmonic analysis. It costs FPGA hardware, bespoke quantisation and sparsity engineering, and is tied to the electrical power systems domain rather than general ML serving. 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It costs FPGA hardware, bespoke quantisation and sparsity engineering, and is tied to the electrical power systems domain rather than general ML serving. 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The resulting frequency domain features are used to extract the amplitude and phase of each harmonic order, which serve as the training label vector. The raw waveform is segmented into multiple samples according to the model input size, covering a half cycle, replicating the normal input format seen by the estimation model. The backbone features are accumulated into a feature matrix, after which the task-head weights are updated through the closed-form solution of ridge regression, as shown in equation 1. Here, denotes the stacked feature matrix and denotes the target matrix. Since only the task heads are updated through regression, online retraining becomes much more practical without resorting to costly backpropagation. 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