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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": "2609.31315", "title": "LUCID: Learning Under Confounding for Inference and Discovery in Time Series", "summary": "LUCID wraps an existing time-series causal discovery engine, replacing raw-input discovery with a Marčenko–Pastur spectral routing step that estimates the confounding regime, attenuates factor-driven variation, and calibrates edge selection against a data-driven null. Cost is modest: a CPU-feasible preprocessing layer plus hyperparameter and null-calibration overhead, with no large models or GPU requirement. 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