1 citations · 2 across the 14 of their papers we have counts for
14 papers
Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation
Rui Wang, Jiazhou Wang, Zheng Wei +16
Search and recommendation serve a shared discovery objective but encode intent differently. We study this boundary through Dear Algo on Threads, a deployed product where open-ended…
CMI-Mem: Toward Generalizable Long-Term Memory Management via CMI-Augmented Reinforcement Learning
Yubo Wang, Qiuyu Zhao, Zenghui Sun +6
Memory Manager models are pivotal in agent systems. Existing reinforcement-learning methods commonly use LLM-judged synthetic question-answer (QA) pairs: this provides useful downs…
Memory Layer: Train the In-Model Cache for Recommendation Models
Liangyuan Na, Gufan Yin, Yixin Bao +19
Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Because this cache exists only at s…
When AI reviews science: Can we trust the referee?
Jialiang Wang, Yuchen Liu, Hang Xu +7
The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large langu…
Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
Lei Chen, Chen Ju, Xu Chen +13
In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical chal…
RAC: Relation-Aware Cache Replacement for Large Language Models
Yuchong Wu, Zihuan Xu, Wangze Ni +5
The scaling of Large Language Model (LLM) services faces significant cost and latency challenges, making effective caching under tight capacity crucial. Existing cache replacement…
Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System
Jiang Zhang, Yubo Wang, Wei Chang +14
Approximate nearest neighbor (ANN) search is widely used in the retrieval stage of large-scale recommendation systems. In this stage, candidate items are indexed using their learne…
SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs
Bi Xue, Hong Wu, Lei Chen +29
Serving deep learning based recommendation models (DLRM) at scale is challenging. Existing approaches rely on dedicated ANN indexing and filtering services on CPUs, suffering from…
Optimizing Retrieval for RAG via Reinforcement Learning
Jiawei Zhou, Lei Chen
As retrieval-augmented generation (RAG) becomes more widespread, the role of retrieval is shifting from retrieving information for human browsing to retrieving context for AI reaso…
Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Luyu Qiu, Jianing Li, Chi Su +1
Relational databases are foundational to numerous domains, including business intelligence, scientific research, and enterprise systems. However, accessing and analyzing structured…
OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning
Jiawei Zhou, Lei Chen
In this paper, we analyze and empirically show that the learned relevance for conventional information retrieval (IR) scenarios may be inconsistent in retrieval-augmented generatio…
SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration
Yuanhao Shen, Xiaodan Zhu, Lei Chen
The tool-use ability of Large Language Models (LLMs) has a profound impact on a wide range of industrial applications. However, LLMs' self-control and calibration capability in app…
Semi-Parametric Retrieval via Binary Bag-of-Tokens Index
Jiawei Zhou, Li Dong, Furu Wei +1
Information retrieval has transitioned from standalone systems into essential components across broader applications, with indexing efficiency, cost-effectiveness, and freshness be…
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation
Yeongseo Jung, Eunseo Jung, Lei Chen
In Conversational Recommendation System (CRS), an agent is asked to recommend a set of items to users within natural language conversations. To address the need for both conversati…