4 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
MiniMax, :, Aili Chen +219
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation
Chao Feng, Yuanhao Pu, Chenghao Zhang +9
The Generator-Evaluator (G-E) framework generates K candidate sequences and uses an evaluator to select the highest-scoring one, which is widely used in recommender systems (RecSys…
DUET: Dual Model Co-Training for Entire Space CTR Prediction
Yutian Xiao, Meng Yuan, Fuzhen Zhuang +9
The pre-ranking stage plays a pivotal role in large-scale recommender systems but faces an intrinsic trade-off between model expressiveness and computational efficiency. Owing to t…
MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
Yutian Xiao, Shukuan Wang, Binhao Wang +6
Click-through rate (CTR) prediction serves as a cornerstone of recommender systems. Despite the strong performance of current CTR models based on user behavior modeling, they are s…