activity
20132026
most citedConditional Random Fields as Recurrent Neural Networks

2.4k citations · 2.9k across the 65 of their papers we have counts for

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7 papers · 1 filter

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.AI2026

Where Did It Go Wrong? Process-Level Evaluation of Web Agents with Semantic State Tracking

Jiwan Chung, JiHyuk Byun, Vibhav Vineet +1

Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and offering little guidance on improv…

cs.AI2025

Fara-7B: An Efficient Agentic Model for Computer Use

Ahmed Awadallah, Yash Lara, Raghav Magazine +9

Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thri…

cs.AI2025

Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness

Erfan Shayegani, Keegan Hines, Yue Dong +6

Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit Bl…

cs.AI2025★ 1 cited

Phi-4-reasoning Technical Report

Marah Abdin, Sahaj Agarwal, Ahmed Awadallah +20

We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that achieves strong performance on complex reasoning tasks. Trained via supervised fine-tuning of Phi-4 on car…

cs.AI2021★ 5 cited

CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning

Daniel McDuff, Yale Song, Jiyoung Lee +7

The ability to perform causal and counterfactual reasoning are central properties of human intelligence. Decision-making systems that can perform these types of reasoning have the…