Transformer-based CTR models face a growing bottleneck at the residual connection: under Pre-Norm, early user-interest signals are diluted layer by layer; the identity skip cannot forget stale interests; and each layer sees only its immediate predecessor, losing long-range cross-layer dependencies. Recent attention-based residual variants (AttnRes) address parts of this in language models, but drop the protective identity skip and have not been tried in recommendation. Drawing on Dual Path Networks (DPN) and the HORNN view of residuals, we present DeRes, which routes each layer through two parallel paths -- an Identity residual path that preserves first-order feature reuse and gradient flow, and a Block Attention Residual path that attends over compressed outputs of all earlier blocks for high-order recall. A vector-wise gate decides, per hidden dimension, the weight given to each path. We further propose Pointwise AttnRes, replacing the Softmax in the cross-layer attention with SiLU so that multiple past blocks can be activated simultaneously and irrelevant ones receive negative (forgetting) weights -- better aligned with CTR's parallel multi-interest patterns. On a large-scale industrial dataset (331M interactions from a major social-media platform), Criteo (45M), and Avazu (40M), DeRes outperforms twelve baselines including OneTrans, TokenMixer-Large, UniMixer, mHC, and AttnRes, achieving up to +0.32% AUC at under 5% extra FLOPs. Beyond a single operating point, DeRes fits a markedly steeper compute-AUC scaling law (gamma=0.118 vs. 0.071 for OneTrans, a 1.66x gap), so an 8-layer DeRes matches a 16-layer OneTrans -- about 2x compute saving at equivalent AUC. Ablations confirm that the dual-path design outperforms either single path, Identity beats learnable residuals, and SiLU beats Softmax.