Papers by Ruichao Yang
Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models (2024.findings-acl)
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| Challenge: | Social media has become a fertile ground for nurturing rumors and misinformation due to its lack of systematic moderation. |
| Approach: | They propose a framework to enhance the joint predictive capabilities of LLMs for stance detection and rumor verification tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and generalizes to non-LLMs accommodated as task models. |
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom (2023.emnlp-main)
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| Challenge: | Existing methods for debunking fake news rely on blending of authentic and fabricated content by creators. |
| Approach: | They propose a model that detects misinformation at sentence-level using social media conversations . they use a bag-level annotation system to train the model . |
| Outcome: | The proposed model outperforms existing state-of-the-art models on three real-world benchmarks and outperformed existing state of the art models in debunking fake news at sentence and article levels. |
Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)
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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin
| Challenge: | Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation . |
| Approach: | They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context. |
| Outcome: | The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process. |
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026.findings-acl)
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Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma
| Challenge: | Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks . |
| Approach: | They propose a framework for LLM agent memory mechanisms that formalizes the development process into three stages: storage, reflection, and experience. |
| Outcome: | The proposed framework breaks the development process into three stages . it analyzes the need for long-range consistency, challenges in dynamic environments, and the ultimate goal of continual learning. |