arXiv Computation and Language

REALMS: An AI-Assistant Conversational System for Real-Time Exact Audience Sizing over High-Dimensional Nested Profiles

REALMS is a conversational system that provides real‑time, exact audience sizing for digital marketers. It uses embedding‑based vector search to retrieve relevant categorical attributes, an LLM‑powered NL2SQL pipeline for accurate query generation over complex nested schemas, and schema standardization for industry‑agnostic deployment. Evaluations on real enterprise data show high recall, accurate SQL execution, and low latency, enabling interactive audience insights that previously required hours.

arXiv AI

Jul 29

SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence

arXiv:2607. 25042v1 Announce Type: new Abstract: The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints.

By Bhanu Teja Rangaraju, Chandan Kumar

agents data-engineering

arXiv Machine Learning

Jun 25

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems

arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.

By Qingyun Liu, Bo Yan, Yang Liu, Yuji Roh, Ekansh Sharma, Likang Yin, Emma Olowo, Min-hsuan Tsai, Yuxuan Li, Diego Uribe, Saksham Aggarwal, Siqi Wu, Yuan Hao, Vikas Kedigehalli, Lukasz Heldt, Lichan Hong, Li Wei, Xinyang Yi

llms rag nlp

arXiv Machine Learning

Jun 2

Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent

arXiv:2604. 26197v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential.

By Zhentao Xu, Shangjin Zhang, Emir Poyraz, Yvonne Li, Ye Jin, Xie Lu, Xiaoyang Gu, Karthik Ramgopal, Praveen Kumar Bodigutla, Xiaofeng Wang

llms agents safety

arXiv Machine Learning

Jun 9

A Unified Structured Query Understanding Framework for Industrial Semantic Search

arXiv:2605. 27441v2 Announce Type: replace-cross Abstract: Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components.

By Ping Liu, Qianqi Shen, Jianqiang Shen, Chunnan Yao, Kevin Kao, Rajat Arora, Dan Xu, Baofen Zheng, Yunxiang Ren, Benjamin Le, Ali Hooshmand, Igor Lapchuk, Juan Bottaro, Raghavan Muthuregunathan, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang

llms rag efficiency

Hugging Face Trending Papers

Jun 9

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.

llms rag benchmarks

arXiv Machine Learning

Jul 28

SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads

arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).

By Congfei Zhang, Jingxiao Ma, Xiaodong Liu, Hsiang-wei Chao, Siman Wang, Ge Liu, Shantanu Aggarwal, Vincent Zhang, Meghana Missula, Rachel Liao, Zichu Li, Xiao Bai, Yunzhi Zhou, Yajun Wang, Zhe Liu, Jinchao Li, Yu Zhang

llms rag

arXiv AI

Jun 8

Re-Centering Humans in LLM Personalization

arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.

By Lechen Zhang, Jiarui Liu, Tal August

llms

arXiv Machine Learning

Aug 12

UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking

arXiv:2603. 24226v4 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have inspired a surge of scaling research in industrial search, advertising, and recommendation systems.

By Liren Yu, Caiyuan Li, Feiyi Dong, Tao Zhang, Zhixuan Zhang, Dan Ou, Haihong Tang, Bo Zheng

llms

arXiv Machine Learning

Jun 5

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling

arXiv:2604. 12110v2 Announce Type: replace Abstract: Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity.

By Zikun Liu, Liang Luo, Qianru Li, Zhengyu Zhang, Wei Ling, Jingyi Shen, Zeliang Chen, Yaning Huang, Jingxian Huang, Abdallah Aboelela, Chonglin Sun, Feifan Gu, Fenggang Wu, Hang Qu, Huayu Li, Jill Pan, Kaidi Pei, Laming Chen, Longhao Jin, Qin Huang, Tongyi Tang, Varna Puvvada, Wenlin Chen, Xiaohan Wei, Xu Cao, Yantao Yao, Yuan Jin, Yunchen Pu, Yuxin Chen, Zijian Shen, Zhengkai Zhang, Jing Zhu, Dong Liang, Ellie Wen

rag efficiency

arXiv AI

Jun 2

NBQ: Next-Best-Question for Dynamic Profiling

arXiv:2606. 00809v1 Announce Type: new Abstract: Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person.

By Yimin Shi, Clarice Wang, Haixun Wang, Xiaokui Xiao

rag

arXiv Machine Learning

Jun 10

Generative Archetype-Grounded Item Representations for Sequential Recommendation

arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.

By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King

llms rag benchmarks

arXiv AI

Aug 10

Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation

arXiv:2608. 06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.

By Ziyun Xu, Bosen Ding, Yue Zhang, Ji Qi, Qingyuan Song, Jizhou Huang, Liwei Wang, Jefferey Santelli, Yue Weng, Qichao Que, Zhenheng Yang, Junfeng Pan, Linhong Zhu

llms agents reinforcement-learning efficiency multimodal safety

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