136 citations · 538 across the 49 of their papers we have counts for
12 papers · 1 filter
Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning
Bincheng Gu, Min Gao, Zongwei Wang +3
Test-time adaptation has emerged as a lightweight alternative to costly post-training for improving the reasoning capabilities of Large Language Models (LLMs) on downstream tasks.…
Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging
Linh Dieu Le, Tong Chen, Shazia Sadiq +3
Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher…
Personalized Communication Skills for Agentic Recommender Systems
Zongwei Wang, Min Gao, Guangyu Hu +2
Agentic recommender systems increasingly employ large language model-based UserAgents to evaluate candidate items through simulated feedback before recommendations are delivered. H…
ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning
Jiaqi Zhang, Tong Chen, Junliang Yu +2
Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users. However, unlike natural-wo…
Where Reasoning Matters: Rethinking Latent Reasoning in Semantic ID-based Generative Recommendation
Shangxin Yang, Min Gao, Zongwei Wang +1
Semantic ID-based generative recommendation predicts an item by generating a short sequence of semantic ID tokens, where each token is produced autoregressively. Latent reasoning h…
FOSTER: First-order Dataset Distillation for Text-based Sequential Recommendation
Hung Vinh Tran, Tong Chen, Xinyi Gao +3
Text-based sequential recommender systems, while greatly improving recommendation accuracy by incorporating item contexts, are undeniably more expensive to train. By condensing a l…
GRAFT: Graph-Tokenized LLMs for Tool Planning
Xinyi Gao, Xinyu Ren, Junliang Yu +3
Large language models (LLMs) are increasingly used to complete complex tasks by selecting and coordinating external tools across multiple steps. This requires aligning tool choices…
Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems
Yuchuan Zhao, Tong Chen, Junliang Yu +3
Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance, enabling recommendations through prompt-driven inference o…
Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
Zongwei Wang, Min Gao, Hongzhi Yin +5
Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling itera…
PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection
Zongwei Wang, Min Gao, Junliang Yu +2
Misinformation on social media poses a critical threat to information credibility, as its diverse and context-dependent nature complicates detection. Large language model-empowered…
Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection
Bincheng Gu, Min Gao, Junliang Yu +4
Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements…
When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents
Zongwei Wang, Bincheng Gu, Hongyu Yu +5
This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such grou…