5 papers
MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences
Shijian Wang, Jiarui Jin, Runhao Fu +11
Research agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAg…
LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
Kangning Zhang, Wenxiang Jiao, Kounianhua Du +4
Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipel…
Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering
Kounianhua Du, Jianxing Liu, Kangning Zhang +6
The rapid evolution of large language models (LLMs) has intensified the demand for effective personalization techniques that can adapt model behavior to individual user preferences…
A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models
Congmin Zheng, Jiachen Zhu, Zhuoying Ou +8
Although Large Language Models (LLMs) exhibit advanced reasoning ability, conventional alignment remains largely dominated by outcome reward models (ORMs) that judge only final ans…
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Rong Shan, Jianghao Lin, Chenxu Zhu +7
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation method…