activity
20232026
most citedPhi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

163 citations · 216 across the 21 of their papers we have counts for

collaborators
Showing 2026Show all

8 papers · 1 filter

cs.AI2026

CUAWright: A Minimal Unified Interface for Digital Agents

Yadong Lu, Theodore Lee, Yifei Li +5

The prevailing approach to computer-use agents couples a model with a domain-specific harness: a browser or desktop environment equipped with human engineered tools that are fixed…

cs.AI2026

Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

Yash Pandya, Sahil Gupta, Sarthak Harne +10

Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and s…

cs.AI2026

Fara-1.5: Scalable Learning Environments for Computer Use Agents

Ahmed Awadallah, Sahil Gupta, Yash Lara +12

Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two key ingredients: environment…

cs.LG2026

ECHO: Terminal Agents Learn World Models for Free

Vaishnavi Shrivastava, Piero Kauffmann, Ahmed Awadallah +1

CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream -- stdout, errors, files…

cs.AI2026

MEMENTO: Teaching LLMs to Manage Their Own Context

Vasilis Kontonis, Yuchen Zeng, Shivam Garg +7

Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state. We introduce MEMENTO: a method that teaches model…

cs.CR2026

The Art of Building Verifiers for Computer Use Agents

Corby Rosset, Pratyusha Sharma, Andrew Zhao +2

Verifying the success of computer use agent (CUA) trajectories is a critical challenge: without reliable verification, neither evaluation nor training signal can be trusted. In thi…

cs.LG2026

Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces

Karan Gupta, Pranav Vajreshwari, Yash Pandya +3

Agentic systems operating over large tool ecosystems must plan and execute long-horizon workflows under weak or non-verifiable supervision. While frontier models mitigate these cha…

cs.HC2026

Beyond Clicking:A Step Towards Generalist GUI Grounding via Text Dragging

Zeyi Liao, Yadong Lu, Boyu Gou +2

Graphical user interface (GUI) grounding, the process of mapping human instructions to GUI actions, serves as a fundamental basis to autonomous GUI agents. While existing grounding…