From the 1 of 6 linked papers with an AI index.
6 papers
Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
Yash Pandya, Sahil Gupta, Sarthak Harne +10
Echoverse introduces a pipeline that compiles specifications into deep, stateful synthetic applications for training computer-use agents, using a co‑evolution loop that repairs env…
Scaling Reasoning Efficiently via Relaxed On-Policy Distillation
Jongwoo Ko, Sara Abdali, Young Jin Kim +2
On-policy distillation is pivotal for transferring reasoning capabilities to capacity-constrained models, yet remains prone to instability and negative transfer. We show that on-po…
CUA-Skill: Develop Skills for Computer Using Agent
Tianyi Chen, Yinheng Li, Michael Solodko +12
Computer-Using Agents (CUAs) aim to autonomously operate computer systems to complete real-world tasks. However, existing agentic systems remain difficult to scale and lag behind h…
AppSelectBench: Application-Level Tool Selection Benchmark
Tianyi Chen, Michael Solodko, Sen Wang +14
Computer Using Agents (CUAs) are increasingly equipped with external tools, enabling them to perform complex and realistic tasks. For CUAs to operate effectively, application selec…
Hierarchical Self-Attention: Generalizing Neural Attention Mechanics to Multi-Scale Problems
Saeed Amizadeh, Sara Abdali, Yinheng Li +1
Transformers and their attention mechanism have been revolutionary in the field of Machine Learning. While originally proposed for the language data, they quickly found their way t…
Self-reflecting Large Language Models: A Hegelian Dialectical Approach
Sara Abdali, Can Goksen, Michael Solodko +4
In this paper, we introduce a self-reflection framework for Large Language Models (LLMs) grounded in the Hegelian Dialectic, a philosophical method in which an initial proposition…