6 citations · 7 across the 8 of their papers we have counts for
10 papers
Efficient Context Scaling with LongCat ZigZag Attention
Chen Zhang, Yang Bai, Jiahuan Li +19
We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited c…
EvoDR: Evolving Dispatching Rules via Large Language Model for Dynamic Flexible Assembly Flow Shop Scheduling
Junhao Qiu, Haoyang Zhuang, Fei Liu +2
Dynamic flexible assembly flow shop scheduling with multi-product delivery is a critical combinatorial problem, characterized by kitting supply and machine flexibility. Genetic pro…
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding
Yuhang Zhou, Mingrui Zhang, Ke Li +12
Understanding and reasoning over tables is a critical capability for many real-world applications. Large language models (LLMs) have shown promise on this task, but current approac…
RecoWorld: Building Simulated Environments for Agentic Recommender Systems
Fei Liu, Xinyu Lin, Hanchao Yu +12
We present RecoWorld, a blueprint for building simulated environments tailored to agentic recommender systems. Such environments give agents a proper training space where they can…
Knowledge-aware Diffusion-Enhanced Multimedia Recommendation
Xian Mo, Fei Liu, Rui Tang +3
Multimedia recommendations aim to use rich multimedia content to enhance historical user-item interaction information, which can not only indicate the content relatedness among ite…
Large Language Models for Multi-Facility Location Mechanism Design
Nguyen Thach, Fei Liu, Houyu Zhou +1
Designing strategyproof mechanisms for multi-facility location that optimize social costs based on agent preferences had been challenging due to the extensive domain knowledge requ…
HARBOR: Exploring Persona Dynamics in Multi-Agent Competition
Kenan Jiang, Li Xiong, Fei Liu
We investigate factors contributing to LLM agents' success in competitive multi-agent environments, using auctions as a testbed where agents bid to maximize profit. The agents are…
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
Zhu Xu, Zhiqiang Zhao, Zihan Zhang +6
Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. T…
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation
Weipu Chen, Zhuangzhuang He, Fei Liu
Learning user preferences from implicit feedback is one of the core challenges in recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore, va…
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
Yao Lu, Song Bian, Lequn Chen +19
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in t…