1 citations · 2 across the 10 of their papers we have counts for
11 papers
Believed but Not Said: Measuring Hidden Belief Collapse in Multi-Agent LLM Discussions
Liangjie Huang, Henry Peng Zou, Wei-Chieh Huang +3
LLM systems increasingly rely on groups of agents that discuss, critique, and check one another, assuming that peers will catch individual errors. We test this assumption against a…
Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents
Wei-Chieh Huang, Weizhi Zhang, Yuchen Wu +12
Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which…
XBridge: Entity-Grounded Latent Bridge for Heterogeneous LLM Communication
Wooseong Yang, Wei-Chieh Huang, Weizhi Zhang +3
Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redundant reasoning patterns. Yet…
Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
Weizhi Zhang, Wooseong Yang, Yuxin Cui +9
Traditional recommender systems (RecSys) primarily infer user preferences from implicit signals (such as clicks, watches, and purchases), often neglecting the rich explicit context…
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…
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang +56
Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "…
RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
Chunyu Miao, Henry Peng Zou, Yangning Li +28
Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing…
Adaptive Candidate Retrieval with Dynamic Knowledge Graph Construction for Cold-Start Recommendation
Wooseong Yang, Weizhi Zhang, Yuqing Liu +4
The cold-start problem remains a critical challenge in real-world recommender systems, as new items with limited interaction data or insufficient information are frequently introdu…
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Weizhi Zhang, Liangwei Yang, Wooseong Yang +5
Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with…
Item Cluster-aware Prompt Learning for Session-based Recommendation
Wooseong Yang, Chen Wang, Zihe Song +2
Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly o…
Subgraph-level Universal Prompt Tuning
Junhyun Lee, Wooseong Yang, Jaewoo Kang
In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent. This trend is particularly observable i…