1 citations · 1 across the 6 of their papers we have counts for
6 papers
SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning
Zebin Chen, Fei Xing, Yang Chen +4
Multi-task learning (MTL) requires navigating unavoidable trade-offs among competing objectives. This paradigm is frequently formulated as multi-objective optimization (MOO), where…
Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models
Yang Chen, Zhan Zhuang, Yanbin Wei +3
While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting on seen clas…
Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debates
Chuan He, Zebin Chen, Zhengyi Yang +5
Multi-Agent Debate (MAD) with Majority Voting is a dominant paradigm for improving LLM reasoning, yet its effectiveness rests on the Condorcet Jury Theorem's assumption of independ…
One-Token Verification for Reasoning Correctness Estimation
Zhan Zhuang, Xiequn Wang, Zebin Chen +4
Recent breakthroughs in large language models (LLMs) have led to notable successes in complex reasoning tasks, such as mathematical problem solving. A common strategy for improving…
OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation
Yehua Huang, Penglei Sun, Zebin Chen +2
Academic peer review remains the cornerstone of scholarly validation, yet the field faces some challenges in data and methods. From the data perspective, existing research is hinde…
A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning
Jiate Liu, Zebin Chen, Shaobo Qiao +10
Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure.…