{ "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": "2609.17815", "title": "Principled Koopman Representations with Kalman Inference for Efficient Time-Series Prediction", "summary": "K2SVD replaces neural latent Koopman-space models (and generic deep time-series forecasters) with an explicitly low-rank Koopman operator learned via a Hilbert-Schmidt objective, plus a linear Gaussian state-space model with Kalman filtering for multi-step prediction. It costs little at inference—a compact latent space and linear Kalman recursion suit a single 24 GB GPU or even CPU—though it adds the complexity of fitting the SVD-style representation and state-space model. 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