9 papers
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7
The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…
Verifiable Reasoning for LLM-based Generative Recommendation
Xinyu Lin, Hanqing Zeng, Hanchao Yu +8
Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing…
Chart Deep Research in LVLMs via Parallel Relative Policy Optimization
Jiajin Tang, Gaoyang, Wenjie Wang +2
With the rapid advancement of data science, charts have evolved from simple numerical presentation tools to essential instruments for insight discovery and decision-making support.…
NextMem: Towards Latent Factual Memory for LLM-based Agents
Zeyu Zhang, Rui Li, Xiaoyan Zhao +4
Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches…
Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery
Chaoqun Yang, Xinyu Lin, Shulin Li +4
Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rigorously evaluating an AI's capacity fo…
Bringing Reasoning to Generative Recommendation Through the Lens of Cascaded Ranking
Xinyu Lin, Pengyuan Liu, Wenjie Wang +5
Generative Recommendation (GR) has become a promising end-to-end approach with high FLOPS utilization for resource-efficient recommendation. Despite the effectiveness, we show that…