5 citations · 9 across the 19 of their papers we have counts for
8 papers · 1 filter
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…