# OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender

arXiv: https://arxiv.org/abs/2510.26104 · HTML: https://mlsanity.com/paper/2510.26104/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2025.

## Built on by (most cited first, 30 of 35)

- [Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design](https://mlsanity.com/paper/2602.10016/) (2026)
- [UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems](https://mlsanity.com/paper/2604.00590/) (2026)
- [NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations](https://mlsanity.com/paper/2511.18793/) (2025)
- [LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation](https://mlsanity.com/paper/2601.20083/) (2026)
- [GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation](https://mlsanity.com/paper/2604.14878/) (2026)
- [End-to-End Semantic ID Generation for Generative Advertisement Recommendation](https://mlsanity.com/paper/2602.10445/) (2026)
- [TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds](https://mlsanity.com/paper/2604.13737/) (2026)
- [RelayGR: Scaling Long-Sequence Generative Recommendation via Cross-Stage Relay-Race Inference](https://mlsanity.com/paper/2601.01712/) (2026)
- [MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches](https://mlsanity.com/paper/2604.22881/) (2026)
- [Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation](https://mlsanity.com/paper/2606.03866/) (2026)
- [HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent](https://mlsanity.com/paper/2512.24787/) (2025)
- [Closing the Auto-Research Loop: An AI Co-Scientist for Production Search Ranking](https://mlsanity.com/paper/2603.22376/) (2026)
- [SlimPer: Make Personalization Model Slim and Smart](https://mlsanity.com/paper/2607.12281/) (2026)
- [Field-Aware RankMixer with Dual-Stream Bilinear Fusion for the Tencent UNI-REC Challenge](https://mlsanity.com/paper/2607.15590/) (2026)
- [Think2Go: Generative Next POI Recommendation with LLM Reasoning](https://mlsanity.com/paper/2607.28997/) (2026)
- [UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation](https://mlsanity.com/paper/2608.16797/) (2026)
- [MDL: A Unified Multi-Distribution Learner in Large-scale Industrial Recommendation through Tokenization](https://mlsanity.com/paper/2602.07520/) (2026)
- [RGAlign-Rec: Ranking-Guided Alignment for Latent Query Reasoning in Recommendation Systems](https://mlsanity.com/paper/2602.12968/) (2026)
- [Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features](https://mlsanity.com/paper/2603.12586/) (2026)
- [UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking](https://mlsanity.com/paper/2603.24226/) (2026)
- [Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation](https://mlsanity.com/paper/2604.20861/) (2026)
- [Conditional Memory Enhanced Item Representation for Generative Recommendation](https://mlsanity.com/paper/2605.11447/) (2026)
- [Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search](https://mlsanity.com/paper/2605.17994/) (2026)
- [CMSL: Constructive Multi-Sequence Learning for Recommendation Systems](https://mlsanity.com/paper/2606.28533/) (2026)
- [MESH: Scaling Up Retrieval with Heterogeneous Content Unification](https://mlsanity.com/paper/2607.12392/) (2026)
- [WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture](https://mlsanity.com/paper/2607.17017/) (2026)
- [RoleMix: Unifying Sequential and Non-Sequential Features via Semantic Tokenization for Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2607.22700/) (2026)
- [SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation](https://mlsanity.com/paper/2607.24025/) (2026)
- [ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation](https://mlsanity.com/paper/2607.27744/) (2026)
- [SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences](https://mlsanity.com/paper/2608.15429/) (2026)

## Abstract (arXiv)

In recommendation systems, scaling up feature-interaction modules (e.g., Wukong, RankMixer) or user-behavior sequence modules (e.g., LONGER) has achieved notable success. However, these efforts typically proceed on separate tracks, which not only hinders bidirectional information exchange but also prevents unified optimization and scaling. In this paper, we propose OneTrans, a unified Transformer backbone that simultaneously performs user-behavior sequence modeling and feature interaction. OneTrans employs a unified tokenizer to convert both sequential and non-sequential attributes into a single token sequence. The stacked OneTrans blocks share parameters across similar sequential tokens while assigning token-specific parameters to non-sequential tokens. Through causal attention and cross-request KV caching, OneTrans enables precomputation and caching of intermediate representations, significantly reducing computational costs during both training and inference. Experimental results on industrial-scale datasets demonstrate that OneTrans scales efficiently with increasing parameters, consistently outperforms strong baselines, and yields a 5.68% lift in per-user GMV in online A/B tests.