# KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation

arXiv: https://arxiv.org/abs/2609.39828 · HTML: https://mlsanity.com/paper/2609.39828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 15 of 15)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025)
- [OpenCodeReasoning: Advancing Data Distillation for Competitive Coding](https://mlsanity.com/paper/2504.01943/) (2025)
- [Kwai Keye-VL 1.5 Technical Report](https://mlsanity.com/paper/2509.01563/) (2025)
- [OneRec-Think: In-Text Reasoning for Generative Recommendation](https://mlsanity.com/paper/2510.11639/) (2025)
- [Kwai Keye-VL Technical Report](https://mlsanity.com/paper/2507.01949/) (2025)
- [OneRec-V2 Technical Report](https://mlsanity.com/paper/2508.20900/) (2025)
- [OpenOneRec Technical Report](https://mlsanity.com/paper/2512.24762/) (2025)
- [OneReason Technical Report](https://mlsanity.com/paper/2606.06260/) (2026)
- [OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce](https://mlsanity.com/paper/2601.21770/) (2026)
- [OnePiece: The Great Route to Generative Recommendation -- A Case Study from Tencent Algorithm Competition](https://mlsanity.com/paper/2512.07424/) (2025)
- [Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation](https://mlsanity.com/paper/2604.04976/) (2026)
- [Field-Aware RankMixer with Dual-Stream Bilinear Fusion for the Tencent UNI-REC Challenge](https://mlsanity.com/paper/2607.15590/) (2026)
- [UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation](https://mlsanity.com/paper/2608.16797/) (2026)

## Abstract (arXiv)

Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recommender. Under the significant developing wave of large language model, our team have been developed Semantic ID based OneRec/OneRec-V2. These models have been widely deployed in production and demonstrate the scaling potential of the autoregressive next-item prediction paradigm for industrial recommender systems. Building on the success of OneRec, we further explored a series of models, including OneRec-Think, OpenOneRec, and OneReason, that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation. However, our preliminary works found that introducing reasoning CoT does not always improve the recommendation performance. To address this issue, OneReason strengthens the semantic alignment between items and language, introduces structured template-based supervision for interest reasoning, and applies advanced reinforcement learning techniques to make reasoning more beneficial to recommendation. As a frontier topic to building recommendation foundation models, we believe this topic has significant research value and hope to encourage more researchers to explore it together. To this end, together with the SIGIR 2026 community, we organized the KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation.