6 papers
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors
Chaohao Yuan, Chenghao Xiao, Yu Rong +2
SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadth while RLVR enhances reason…
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
Tian Bian, Yifan Niu, Chaohao Yuan +7
Circuit discovery has recently attracted attention as a potential research direction to explain the non-trivial behaviors of language models. It aims to find the computational subg…
Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In real-world recommendation scenarios, users typically engage with platforms through multiple types of behavioral interactions. Multi-behavior recommendation algorithms aim to lev…
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogen…
Can Large Language Models Be Query Optimizer for Relational Databases?
Jie Tan, Kangfei Zhao, Rui Li +6
Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in dat…
Natural Language-Assisted Multi-modal Medication Recommendation
Jie Tan, Yu Rong, Kangfei Zhao +5
Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patie…