# RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems

arXiv: https://arxiv.org/abs/2604.17878 · HTML: https://mlsanity.com/paper/2604.17878/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems](https://mlsanity.com/paper/2606.26859/) (2026)
- [SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation](https://mlsanity.com/paper/2607.24025/) (2026)
- [Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning](https://mlsanity.com/paper/2609.07406/) (2026)

## Abstract (arXiv)

The scaling laws for recommender systems have been increasingly validated, where MetaFormer-based architectures consistently benefit from increased model depth, hidden dimensionality, and user behavior sequence length. However, whether representation capacity scales proportionally with parameter growth remains unexplored. Prior studies on RankMixer reveal that the effective rank of token representations exhibits a damped oscillatory trajectory across layers, failing to increase consistently with depth and even degrading in deeper layers. Motivated by this observation, we propose RankUp, an architecture designed to mitigate representation collapse and enhance expressive capacity through randomized permutation splitting over sparse features, a multi-embedding paradigm, global token integration and crossed pretrained embedding tokens. RankUp has been fully deployed in large-scale production across Weixin Video Accounts, Official Accounts and Moments, yielding GMV improvements of 3.41%, 4.81% and 2.12%, respectively.