1 citations · 1 across the 8 of their papers we have counts for
10 papers
ALPBench: A Benchmark for Attribution-level Long-term Personal Behavior Understanding
Lu Ren, Junda She, Xinchen Luo +23
Recent advances in large language models have highlighted their potential for personalized recommendation, where accurately capturing user preferences remains a key challenge. Leve…
DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing
Qian Cao, Yahui Liu, Wei Bi +6
Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative w…
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
Zhiyang Zhang, Junda She, Kuo Cai +8
Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerg…
PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations
Chengcheng Guo, Kuo Cai, Yu Zhou +5
Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, exist…
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing
Hongzhi Zhang, Yuanze Hu, Tinghai Zhang +9
The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retriev…
OpenOneRec Technical Report
Guorui Zhou, Honghui Bao, Jiaming Huang +44
While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation sy…