AI RESEARCH
From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction
arXiv CS.LG
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ArXi:2511.12081v2 Announce Type: replace-cross Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark contrast to the {predictable scaling laws} seen in large language models (LLMs). We identify the root cause as a {fundamental} \textit{structural misalignment}: {standard} Transformers assume sequential compositionality, whereas CTR data demand combinatorial reasoning over {heterogeneous} fields. To re alignment, we.