Probability content does not by itself determine interval placement. We study the interval functional induced by relaxed quantile regression (RQR), whose residual-product check loss estimates two unlabeled raw roots without preassigning endpoint quantiles. Under explicit conditions, the unrestricted population minimizer is the unique contiguous content-c interval whose retained mean equals the population mean. A fixed mean tilt preserves content while shifting that retained mean to mu + delta. Interior admissible tilts index all finite-root interior content-c windows, with boundary members obtained as qualified one-sided limits. Equal-tailed and shortest-contiguous intervals therefore have distribution-specific recovery tilts. We construct loss-based generalized posteriors using a pseudo-asymmetric-Laplace normal-exponential augmentation. A fixed-rate mean-tilted sampler covers static regression under proper Gaussian priors, with ordinary RQR obtained exactly at zero tilt. The implemented ordinary branch also supports a conditional-Gaussian regularized-horseshoe adapter using the Nishimura-Suchard augmentation (RHS-NS), and a frozen deep echo-state-network feature matrix is a deterministic nonlinear-design specialization of the same static scan. A dynamic linear extension replaces coefficient blocks by alternating root-specific forward-filtering backward-sampling steps: the stacked state prior is Gaussian, but the joint augmented observation kernel is quartic. Current software and empirical evidence concern ordinary RQR; nonzero-tilt algorithms are derived but not yet implemented or validated. All updates concern interval-root functionals under a loss and prior, not a response likelihood or posterior-predictive responses.

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