7 papers
Preserving General Capabilities during Domain Specialization with Uncertainty-Calibrated MOPD
Ziyuan Liu, Jiao Ou, Jian Liang +2
Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities such as reasoning, coding, instruction following, a…
From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents
Zijie Zhuang, Changxin Lao, Pengbo Xu +13
Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.…
WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models
Hao Jiang, Peiru Du, Pengfei Yao +10
Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs). Existing approache…
Multi-Decoder OneRec: Controllable Generative Retrieval for Multi-Objective Industrial Recommendation
You Wang, Zhao Liu, Guoping Tang +11
Industrial recommender systems build candidate pools by assigning explicit quotas to objective-specific retrieval routes. This design offers quota control but increasingly fragment…
RECAP: Feedback-Driven Streaming Semantic User Profiles for Short-Video Recommendation
Ziyi Zhao, Xiaoyou Zhou, Xiao Lv +13
Language-based user profiles convert long behavioral histories into explicit semantic representations for recommendation. However, most profile generators are optimized in an open…
UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation
Bo Chen, Jinlong Jiao, Tijian Hu +12
Recently, substantial progress has been made in industrial recommendation through component-centric model scaling, where individual components such as behavior modeling, feature in…