2.4k citations · 2.9k across the 65 of their papers we have counts for
6 papers · 1 filter
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…
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…
ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction
Amirhossein Abaskohi, Yuhang He, Peter West +3
Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajector…
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…
StreamReady: Learning What to Answer and When in Long Streaming Videos
Shehreen Azad, Vibhav Vineet, Yogesh Singh Rawat
Streaming video understanding often involves time-sensitive scenarios where models need to answer exactly when the supporting visual evidence appears: answering before the evidence…
OS-Marathon: Benchmarking Computer-Use Agents on Vast-Horizon, Repetitive Tasks
Jing Wu, Wenjie Ai, Daphne Barretto +7
Vast-horizon, repetitive workflows are common in daily routines, e.g., processing expense reports from a stack of receipts, organising a collection of PDF annotations into structur…