activity
20232026
most citedSLMRec: Distilling Large Language Models into Small for Sequential Recommendation

3 citations · 9 across the 27 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.IR2025

CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

Tianxin Wei, Xuying Ning, Xuxing Chen +6

In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items. However, existing generative recomme…

cs.CL2025

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory

Tianxin Wei, Noveen Sachdeva, Benjamin Coleman +12

Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and ev…

cs.LG2025

Graph4MM: Weaving Multimodal Learning with Structural Information

Xuying Ning, Dongqi Fu, Tianxin Wei +2

Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in…

cs.CL2025

Harnessing Consistency for Robust Test-Time LLM Ensemble

Zhichen Zeng, Qi Yu, Xiao Lin +6

Different large language models (LLMs) exhibit diverse strengths and weaknesses, and LLM ensemble serves as a promising approach to integrate their complementary capabilities. Desp…

cs.LG2025★ 2 cited

Flow Matching Meets Biology and Life Science: A Survey

Zihao Li, Zhichen Zeng, Xiao Lin +9

Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological…

cs.CL2025

AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Junyu Zhang, Runpei Dong, Han Wang +8

This paper presents AlphaOne (1), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. 1 first introduces moment, which…

cs.CL2025★ 1 cited

iAgent: LLM Agent as a Shield between User and Recommender Systems

Wujiang Xu, Yunxiao Shi, Zujie Liang +6

Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, th…