11 citations · 17 across the 5 of their papers we have counts for
5 papers
Evaluating Skills, Not Just Agents: Agentic Continuous Evaluation of Skills
Christopher Kevin, Narendran Raghavan, Jean-Francois Puget +9
Enterprise agent programs are moving from prototypes into production, where reusable skills, tools, and workflow packages must be reviewed with evidence rather than prose. Current…
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…
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)…