13 papers
Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization
Xuefei Wang, Jun Han, Zixuan Wang +4
Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions. Existin…
MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer
Xuefei Wang, Jialu Wang, Fengbo Zhang +6
Multi-agent systems (MAS) powered by large language models (LLMs) have emerged as a powerful paradigm for complex problem solving, where performance critically depends on the under…
AdaSTORM: Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Temporal Multi-Agent Collaboration
Bing Hao, Ruijie Wang, Haodong Qian +5
Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of n…
G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
Yuhan Wang, Yibo Ding, Yutong Ye +4
LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style co…
SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning
Chenzhi Hu, Qinzhe Hu, Yuhang Xu +6
Large reasoning models (LRMs) like OpenAI o1 and DeepSeek-R1 achieve high accuracy on complex tasks by adopting long chain-of-thought (CoT) reasoning paths. However, the inherent v…
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
Yuhan Wang, Haopeng Zhang, Yibo Ding +6
Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and text representations through…