4 papers
OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning
Krista Opsahl-Ong, Arnav Singhvi, Jasmine Collins +10
We introduce OfficeQA Pro, a benchmark for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus. The corpus consists of U.S. Tr…
KARL: Knowledge Agents via Reinforcement Learning
Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal +23
We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic sea…
Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs
Jacob Portes, Connor Jennings, Erica Ji Yuen +2
How does retrieval performance scale with pretraining FLOPs? We benchmark retrieval performance across LLM model sizes from 125 million parameters to 7 billion parameters pretraine…
Long Context RAG Performance of Large Language Models
Quinn Leng, Jacob Portes, Sam Havens +2
Retrieval Augmented Generation (RAG) has emerged as a crucial technique for enhancing the accuracy of Large Language Models (LLMs) by incorporating external information. With the a…