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Jonathan D. Chang
Databricks AI Research
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Andrew Drozdov
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Shubham Toshniwal
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Owen Oertell
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Founder's Pitch
"KARL leverages innovative reinforcement learning for affordable, high-performance enterprise search agents."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
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4/4 signals
Series A Potential
4/4 signals
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Why It Matters
This research advances the capabilities of enterprise search agents to efficiently handle complex, multi-step queries and deliver reliable grounded reasoning. Without it, the potential of AI-driven search in retrieving and synthesizing information across various documents and data types would remain suboptimal, hindering effective decision-making in knowledge-intensive industries.
Product Angle
Commercialize KARL as a customizable enterprise search platform that businesses can integrate into their internal systems to improve data retrieval and synthesis across complex proprietary datasets.
Disruption
KARL could disrupt traditional search and data retrieval systems in enterprises, replacing less efficient and non-AI-based information retrieval methods with its more advanced and adaptable system.
Product Opportunity
The market for enterprise search solutions is large, with industries like finance, law, and healthcare needing advanced tools to handle and process vast amounts of data. Companies will pay for solutions that enhance efficiency and data utilization across their operations.
Use Case Idea
Develop on-demand enterprise-grade search agents tailored for industries heavily relying on document-based information like finance and healthcare, enhancing their internal data handling and decision-making processes.
Science
The paper introduces KARL, a system trained using reinforcement learning to create knowledge agents capable of addressing complex, agentic search tasks. It involves a multi-task reinforcement learning process that is efficient enough to generalize across tasks using synthetic data generation and off-policy reinforcement learning. The system is optimized not just for cost but also for quality and latency, proving its effectiveness compared to current state-of-the-art models.
Method & Eval
KARL was evaluated using KARLBench, a newly introduced suite of tasks designed to measure the system's performance in various search settings. It consistently outperformed existing models like GPT 5.2 and others in cost-efficiency, quality, and latency, demonstrating its robustness across multiple benchmarks.
Caveats
The primary limitation is the reliance on proprietary datasets which might limit applicability in contexts where such data isn't available. The training and deployment need to be carefully managed to ensure robust security and privacy controls.
Author Intelligence
Jonathan D. Chang
Databricks AI Research
Andrew Drozdov
Databricks AI Research
Shubham Toshniwal
Databricks AI Research
Owen Oertell
Databricks AI Research
Alexander Trott
Databricks AI Research
Jacob Portes
Databricks AI Research
Abhay Gupta
Databricks AI Research
Pallavi Koppol
Databricks AI Research
Ashutosh Baheti
Databricks AI Research
Sean Kulinski
Databricks AI Research
Ivan Zhou
Databricks AI Research
Irene Dea
Databricks AI Research
Krista Opsahl-Ong
Databricks AI Research
Simon Favreau-Lessard
Databricks AI Research
Sean Owen
Databricks AI Research
Jose Javier Gonzalez Ortiz
Databricks AI Research
Arnav Singhvi
Databricks AI Research
Xabi Andrade
Databricks AI Research
Cindy Wang
Databricks AI Research
Kartik Sreenivasan
Databricks AI Research
Sam Havens
Databricks AI Research
Jialu Liu
Databricks AI Research
Peyton DeNiro
Databricks AI Research
Wen Sun
Databricks AI Research
Michael Bendersky
Databricks AI Research
Jonathan Frankle
Databricks AI Research