We present TransX, a sequence-transduction framework and co-designed infrastructure for scaling industrial recommendation with explicit control over model-compute allocation and execution amortization. TransX separates sequence encoding, behavior memory, and candidate-conditioned fusion into independently scalable components. Training amortizes causal trajectory computation across behavioral prefixes and fans out memory states to candidates; serving disaggregates bulk prefill, nearline refresh, and online prediction through compact behavior-memory KV. We observe anisotropic scaling returns: encoder scaling may saturate beyond moderate depth at fixed memory resolution and fusion capacity; increasing supervision shifts the allocation frontier toward candidate-conditioned fusion; and the memory resolution needed to preserve quality remains roughly stable as model capacity grows. We train TransX on click and conversion, post-train its shared behavioral interface for retrieval and preference ranking, and explore generative recommendation offline. In separate production A/B experiments, the core TransX model improves CTR by 6.02% and conversion by 4.45%, while post-trained retrieval and preference-ranking variants improve CTR by 2.96% and conversion by 1.33%, respectively, over corresponding production baselines. Under matched training conditions, the training infrastructure increases supervised-target throughput by 84%. In serving, disaggregation reduces TransX's total serving cost by nearly 80% versus colocated serving at matched traffic and SLO, enabling approximately 5X the per-request model FLOPs of the HSTU-style production baseline at the same total serving cost. At matched per-request model FLOPs, TransX delivers approximately 4X higher request throughput per H200. These results show that online serving cost need not grow proportionally with recommendation model capacity.

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