3 citations · 6 across the 6 of their papers we have counts for
6 papers
Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
Minhua Lin, Juncheng Wu, Zijun Wang +14
LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task execution without changing…
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
Shijun Li, Wooseong Yang, Pranav Belligundu +3
Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and natural-language reasoning abi…
GT2Vec: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data
Jiacheng Lin, Kun Qian, Haoyu Han +9
Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive e…
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Tianxin Wei, Bowen Jin, Ruirui Li +8
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Ou…
Scalable and Effective Generative Information Retrieval
Hansi Zeng, Chen Luo, Bowen Jin +3
Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generativ…
Language Models As Semantic Indexers
Bowen Jin, Hansi Zeng, Guoyin Wang +10
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous stud…