Modeling lifetime user behavior sequences is essential for capturing long-range preferences in industrial recommendation, yet 100K-length user sequences make target-aware ranking prohibitively expensive. Search-based methods that retrieve from raw sequences incur candidate-dependent costs that grow with sequence length, whereas latent compression methods reduce computation at the expense of item-level addressability. We propose ECHO (Efficient Cascade with Homogeneous Operators), a framework that balances efficiency and fine-grained retrieval by retaining a subset of original event records per user via a Distilled Selector (DS) and retrieving a smaller set of target-relevant records per candidate via a Target-aware Fine-grained Retriever (TFR). DS is supervised by a candidate-aware full-history teacher via per-target distillation, enabling target-aware user-level selection without incurring full-history computation for every candidate. The retriever and target-attention scorer share the teacher's learned relevance parameters, ensuring consistent relevance estimation across the cascade. At a sequence length of L = 100K, ECHO reduces online sequence-branch FLOPs by approximately 160x compared with a representative end-to-end model. Three deployment optimizations, including common user inputs, compact record encoding, and key-side projection absorption, further reduce memory, storage, and data-movement overhead. Experiments on KuaiRand-1K and an industrial-scale short-video platform demonstrate relative GAUC improvements of 0.113%-1.520% over state-of-the-art (SOTA) baselines across multiple tasks. Furthermore, online A/B testing shows a 0.613% improvement in app usage time.

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