5 papers
Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement
Aaditya Shukla, Sidney Knowles, Meenakshi Madugula +9
Enterprise AI agents must continuously adapt to maintain accuracy, reduce latency, and remain aligned with user needs. We present a practical implementation of a data flywheel in N…
Beyond the limitation of a single query: Train your LLM for query expansion with Reinforcement Learning
Shu Zhao, Tan Yu, Anbang Xu
Reasoning-augmented search agents, such as Search-R1, are trained to reason, search, and generate the final answer iteratively. Nevertheless, due to their limited capabilities in r…
ParallelSearch: Train your LLMs to Decompose Query and Search Sub-queries in Parallel with Reinforcement Learning
Shu Zhao, Tan Yu, Anbang Xu +3
Reasoning-augmented search agents such as Search-R1, trained via reinforcement learning with verifiable rewards (RLVR), demonstrate remarkable capabilities in multi-step informatio…
In Defense of RAG in the Era of Long-Context Language Models
Tan Yu, Anbang Xu, Rama Akkiraju
Overcoming the limited context limitations in early-generation LLMs, retrieval-augmented generation (RAG) has been a reliable solution for context-based answer generation in the pa…
FACTS About Building Retrieval Augmented Generation-based Chatbots
Rama Akkiraju, Anbang Xu, Deepak Bora +35
Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs)…