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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": "2610.20463", "title": "Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning", "summary": "SGQN replaces uniform coarse-to-fine action-grid growth in decoupled value-based continuous control with an adaptive grid that adds bins near high-Q bins and prunes low-Q ones, using epsilon-greedy selection. It costs nothing extra in principle, but the paper's own ablation shows the adaptive grid activates all bins and behaves like uniform growth, pruning changes returns by at most 1.1%, and the real drivers are coarse initialization and epsilon-greedy exploration; it also uses one gradient update per environment step, an uncontrolled confound versus baselines. 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