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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": "2610.20400", "title": "HRTF Upsampling and Personalization via Neural Operators", "summary": "SPARO replaces per-subject neural-field fitting and classical spherical-harmonic interpolation for HRTF upsampling with a single learned operator that encodes sparse measurements into spherical latent modes, processes them with Transformer blocks, and decodes at arbitrary query directions. 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