5 citations · 9 across the 19 of their papers we have counts for
21 papers
HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling
Peiyu Zhang, Heng Ping, Nikos Kanakaris +5
Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dependencies arising from co-oc…
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +5
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sust…
RaMem: Contextual Reinstatement for Long-term Agentic Memory
Wei Yang, Bryce Kan, Shixuan Li +5
Long-term memory has become increasingly important for LLM agents that operate across extended interactions and evolving task contexts. Recent memory systems have made past experie…
COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation
Heng Ping, Peiyu Zhang, Shixuan Li +5
LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple the two objectives, optimizing P…
POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization
Heng Ping, Peiyu Zhang, Zhenkun Wang +5
Applying large language models (LLMs) to RTL code optimization for improved power, performance, and area (PPA) faces two key challenges: ensuring functional correctness of optimize…
Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge
Wei Yang, Shixuan Li, Heng Ping +3
Multi-agent systems (MAS) can substantially extend the reasoning capacity of large language models (LLMs). Most MAS frameworks aggregate agent outputs via simple majority voting, d…
FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning
Wei Yang, Rui Zhong, Yiqun Chen +4
Multimodal recommendation aims to enhance user preference modeling by leveraging rich item content such as images and text. Yet dominant systems fuse modalities in the spatial doma…
EMoE: Eigenbasis-Guided Routing for Mixture-of-Experts
Anzhe Cheng, Shukai Duan, Shixuan Li +5
The relentless scaling of deep learning models has led to unsustainable computational demands, positioning Mixture-of-Experts (MoE) architectures as a promising path towards greate…
Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMs
Wei Yang, Jiacheng Pang, Shixuan Li +3
Multi-agent systems (MAS) built on Large Language Models (LLMs) are being used to approach complex problems and can surpass single model inference. However, their success hinges on…
ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization
Anzhe Cheng, Shukai Duan, Shixuan Li +8
Mixture-of-Experts (MoE) architectures expand model capacity by sparsely activating experts but face two core challenges: misalignment between router logits and each expert's inter…
VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +8
Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show promise for Hardware Description Lan…
HGFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
Trung-Kien Nguyen, Heng Ping, Shixuan Li +4
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, k…
HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL
Heng Ping, Shixuan Li, Peiyu Zhang +8
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, when applied to hardware description languages (HDL), t…
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation
Anzhe Cheng, Chenzhong Yin, Yu Chang +4
Low-resolution image segmentation is crucial in real-world applications such as robotics, augmented reality, and large-scale scene understanding, where high-resolution data is ofte…
ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models
Shixuan Li, Wei Yang, Peiyu Zhang +6
Weather forecasting is crucial for public safety, disaster prevention and mitigation, agricultural production, and energy management, with global relevance. Although deep learning…
End-to-End Learning Framework for Solving Non-Markovian Optimal Control
Xiaole Zhang, Peiyu Zhang, Xiongye Xiao +4
Integer-order calculus often falls short in capturing the long-range dependencies and memory effects found in many real-world processes. Fractional calculus addresses these gaps vi…
Multi-scale Generative Modeling for Fast Sampling
Xiongye Xiao, Shixuan Li, Luzhe Huang +6
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models hav…
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
Luzhe Huang, Xiongye Xiao, Shixuan Li +4
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, wher…
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta +6
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems…
Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models
Xiongye Xiao, Heng Ping, Chenyu Zhou +6
In recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform. Nota…
Neuro-Inspired Hierarchical Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta +6
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration…