activity
20242026
most citedMAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow

5 citations · 5 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CL2026

Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?

Yibo Zhao, Zichen Ding, Jiayi Wu +2

Search agents powered by large language models can autonomously decompose queries, retrieve information, and synthesize answers through multi-step reasoning. However, the rapid gro…

cs.CL2026

Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents

Jiayi Wu, Ruobing Xie, Zeqian Huang +6

Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy Optimization (GRPO) being a widely used…

cs.AI2026

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models

Can Xu, Lingyong Yan, Jiayi Wu +6

Recent advances in synergizing large reasoning models (LRMs) with retrieval-augmented generation (RAG) have shown promising results, yet two critical challenges remain: (1) reasoni…

cs.CL2025

Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference

Zhengjia Wang, Danding Wang, Qiang Sheng +2

This paper investigates the detection of misinformation, which deceives readers by explicitly fabricating misleading content or implicitly omitting important information necessary…

cs.AI2025

Benchmarking Overton Pluralism in LLMs

Elinor Poole-Dayan, Jiayi Wu, Taylor Sorensen +2

We introduce OVERTONBENCH, a novel framework for measuring Overton pluralism in LLMs--the extent to which diverse viewpoints are represented in model outputs. We (i) formalize Over…

cs.CL2025

ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement

Kangyang Luo, Yuzhuo Bai, Shuzheng Si +9

Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised ne…

cs.IR2025

Integrating LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation

Shuo Zhang, Xiao Li, Jiayi Wu +3

Session-based recommendation (SBR) is mainly based on anonymous user interaction sequences to recommend the items that the next user is most likely to click. Currently, the most po…

cs.CL2025

Towards AI Search Paradigm

Yuchen Li, Hengyi Cai, Rui Kong +20

In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…

cs.LG2025★ 5 cited

MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow

Yuan-Hao Jiang, Kezong Tang, Zi-Wei Chen +3

Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC g…

cs.CL2024

PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization

Jiayi Wu, Hengyi Cai, Lingyong Yan +5

The emergence of Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in the generation of large language models (LLMs), yet it stil…

cs.CL2024

Cross-model Control: Improving Multiple Large Language Models in One-time Training

Jiayi Wu, Hao Sun, Hengyi Cai +5

The number of large language models (LLMs) with varying parameter scales and vocabularies is increasing. While they deliver powerful performance, they also face a set of common opt…

cs.CL2024

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

Hao Sun, Jiayi Wu, Hengyi Cai +6

Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality and deploying local-based LLMs f…

cs.SE2024

Structure-aware Fine-tuning for Code Pre-trained Models

Jiayi Wu, Renyu Zhu, Nuo Chen +3

Over the past few years, we have witnessed remarkable advancements in Code Pre-trained Models (CodePTMs). These models achieved excellent representation capabilities by designing s…