6 citations · 8 across the 10 of their papers we have counts for
11 papers
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
Weile Chen, Bingchen Miao, Qifan Yu +6
Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on handcrafted execution pipelines…
Enhancing Post-Training Quantization via Future Activation Awareness
Zheqi Lv, Zhenxuan Fan, Qi Tian +2
Post-training quantization (PTQ) is a widely used method to compress large language models (LLMs) without fine-tuning. It typically sets quantization hyperparameters (e.g., scaling…
OmniEduBench: A Comprehensive Chinese Benchmark for Evaluating Large Language Models in Education
Min Zhang, Hao Chen, Wenqi Zhang +6
With the rapid development of large language models (LLMs), various LLM-based works have been widely applied in educational fields. However, most existing LLMs and their benchmarks…
Tackling Device Data Distribution Real-time Shift via Prototype-based Parameter Editing
Zheqi Lv, Wenqiao Zhang, Kairui Fu +6
The on-device real-time data distribution shift on devices challenges the generalization of lightweight on-device models. This critical issue is often overlooked in current researc…
Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
Zhaoyu Fan, Kaihang Pan, Mingze Zhou +7
Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information. However, existing benchmarks primarily emphasize cogniti…
Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration
Zheqi Lv, Keming Ye, Zishu Wei +7
Deep neural networks have become foundational to advancements in multiple domains, including recommendation systems, natural language processing, and so on. Despite their successes…
Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
Zheqi Lv, Tianyu Zhan, Wenjie Wang +6
Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to c…
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
Jinluan Yang, Zhengyu Chen, Teng Xiao +3
Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a…
Semantic Codebook Learning for Dynamic Recommendation Models
Zheqi Lv, Shaoxuan He, Tianyu Zhan +5
Dynamic sequential recommendation (DSR) can generate model parameters based on user behavior to improve the personalization of sequential recommendation under various user preferen…
Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration
Wei Ji, Li Li, Zheqi Lv +5
In our increasingly interconnected world, where intelligent devices continually amass copious personalized multi-modal data, a pressing need arises to deliver high-quality, persona…
MART: Learning Hierarchical Music Audio Representations with Part-Whole Transformer
Dong Yao, Jieming Zhu, Jiahao Xun +6
Recent research in self-supervised contrastive learning of music representations has demonstrated remarkable results across diverse downstream tasks. However, a prevailing trend in…