35 citations · 41 across the 22 of their papers we have counts for
26 papers
Act First, Reason Later: Accelerating On-Policy Distillation for Multi-Turn Agents via Reference-Conditioned Inverse Dynamics
Zubin Zheng, Jiahao Wu, Shaofeng Zhang +3
On-policy distillation (OPD) trains multi-turn language agents with dense teacher supervision on student-generated responses. However, standard think-then-act rollouts require leng…
VulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection
Samal Mukhtar, Yinghua Yao, Zhu Sun +3
Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations alongside predictions, but most w…
Pix2Fact: When Vision Is Not Enough -- Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes
Yifan Jiang, Cong Zhang, Bofei Zhang +4
Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy ove…
Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation
Xiaofeng Cao, Mingwei Xu, Xin Yu +8
Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain…
An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems
Ni Zhang, Zhiguang Cao, Jianan Zhou +2
Complex vehicle routing problems (VRPs) remain a fundamental challenge, demanding substantial expert effort for intent interpretation and algorithm design. While large language mod…
Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation
Kaidong Feng, Zhu Sun, Hui Fang +3
Large Language Models (LLMs) have shown potential in automatic bundle generation but suffer from prohibitive computational costs. Although knowledge distillation offers a pathway t…
Lifelong Learner: Discovering Versatile Neural Solvers for Vehicle Routing Problems
Shaodi Feng, Zhuoyi Lin, Jianan Zhou +5
Deep learning has been extensively explored to solve vehicle routing problems (VRPs), which yields a range of data-driven neural solvers with promising outcomes. However, most neur…
Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles
Shengcai Liu, Hui Ou-yang, Zhiyuan Wang +4
Learning the structure of Bayesian networks (BNs) from data is challenging, especially for datasets involving a large number of variables. The recently proposed divide-and-conquer…
Context-Adaptive Graph Neural Networks for Next POI Recommendation
Yu Lei, Limin Shen, Zhu Sun +2
Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict users' next visits based on their check-in histories. While many existi…
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
Ning Lu, Shengcai Liu, Jiahao Wu +5
Large language models (LLMs) have shown great potential as general-purpose AI assistants across various domains. To fully leverage this potential in specific applications, many com…
BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
Junxiao Yang, Jinzhe Tu, Haoran Liu +9
Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond w…
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Hongxu Wang, Zhu Sun, Yingpeng Du +3
Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional researc…
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
Yingpeng Du, Zhu Sun, Ziyan Wang +3
Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillat…
Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization
Hongliang Qiao, Shanshan Feng, Min Zhou +5
The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diff…
MRP-LLM: Multitask Reflective Large Language Models for Privacy-Preserving Next POI Recommendation
Ziqing Wu, Zhu Sun, Dongxia Wang +3
Large language models (LLMs) have shown promising potential for next Point-of-Interest (POI) recommendation. However, existing methods only perform direct zero-shot prompting, lead…
The Digital Ecosystem of Beliefs: does evolution favour AI over humans?
David M. Bossens, Shanshan Feng, Yew-Soon Ong
As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understa…
Prototype Optimization with Neural ODE for Few-Shot Learning
Baoquan Zhang, Shanshan Feng, Bingqi Shan +3
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a…
Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons
Jiao Liu, Zhu Sun, Shanshan Feng +2
In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functiona…
Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges
Zubin Zheng, Shengcai Liu, Yew-Soon Ong
With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work ad…
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
Mengmeng Chen, Xiaohu Wu, Xiaoli Tang +5
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data het…
Learning Structurally Stabilized Representations for Multi-modal Lossless DNA Storage
Ben Cao, Tiantian He, Xue Li +4
In this paper, we present Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for multi-modal lossless DNA stor…
Road Network Representation Learning with the Third Law of Geography
Haicang Zhou, Weiming Huang, Yile Chen +3
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we id…
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
Ziqi Yin, Shanshan Feng, Shang Liu +3
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that considers both spatial and…
Large Language Models for Intent-Driven Session Recommendations
Zhu Sun, Hongyang Liu, Xinghua Qu +3
Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to th…