6 papers
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Lei Shi, Di Wang, Harry Tran +22
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topic…
CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems
Yannan Wang, Longli Yang, Zhen Liu +2
Deploying lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agentic services closer to users, but resource-constrained edge models often str…
LLM-Driven Reasoning for Constraint-Aware Feature Selection in Industrial Systems
Yuhang Zhou, Zhuokai Zhao, Ke Li +14
Feature selection is a crucial step in large-scale industrial machine learning systems, directly affecting model accuracy, efficiency, and maintainability. Traditional feature sele…
EBPO: Empirical Bayes Shrinkage for Stabilizing Group-Relative Policy Optimization
Kevin Han, Yuhang Zhou, Mingze Gao +6
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing the reasoning capabilities of Large Language Models (LLMs). However, dominant approaches li…
Domain-Specific Query Understanding for Automotive Applications: A Modular and Scalable Approach
Isha Motiyani, Abhishek Kumar, Tilak Kasturi
Despite the growing prevalence of large language models (LLMs) in domain-specific applications, the challenge of query understanding in the automotive sector still remains underexp…
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding
Yuhang Zhou, Mingrui Zhang, Ke Li +12
Understanding and reasoning over tables is a critical capability for many real-world applications. Large language models (LLMs) have shown promise on this task, but current approac…