activity
20232026
most citedMedGemma Technical Report

41 citations · 60 across the 25 of their papers we have counts for

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Showing cs.AIShow all

7 papers · 1 filter

cs.AI2026

TeleTune: Evolving Agent Skills From Offline Telemetry

Justin Chih-Yao Chen, Elias Stengel-Eskin, Yan Chen +6

Computer-use agents need to capture procedural knowledge of how people use software. User telemetry offers a scalable source of this knowledge. However, learning reusable skills fr…

cs.AI2026

Accelerating Scientific Research with Gemini in the Real-World

Samuel Schmidgall, Xiaokai Zhu, Marian Shaw +32

We present an extension and comprehensive real-world validation of Co-Scientist, a Gemini-based multi-agent system designed to accelerate end-to-end scientific research across hypo…

cs.AI2026

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

Valentin Liévin, Samuel Schmidgall, Tim Strother +32

In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of fee…

cs.AI2026

A History-Aware Visually Grounded Critic for Computer Use Agents

Jaewoo Lee, Zaid Khan, Archiki Prasad +7

Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in compl…

cs.AI2025

PRInTS: Reward Modeling for Long-Horizon Information Seeking

Jaewoo Lee, Archiki Prasad, Justin Chih-Yao Chen +3

Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step infor…

cs.AI2025★ 41 cited

MedGemma Technical Report

Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78

Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…

cs.AI2024★ 1 cited

System-1.x: Learning to Balance Fast and Slow Planning with Language Models

Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen +3

Language models can be used to solve long-horizon planning problems in two distinct modes: a fast 'System-1' mode, directly generating plans without any explicit search or backtrac…