co-evolution training 1computer-use agents 1reinforcement learning 1stateful applications 1synthetic environments 1
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cs.CL2025
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…
cs.CL2025
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…
cs.CL2024
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
Yinheng Li, Rogerio Bonatti, Sara Abdali +2
Using Large Language Models (LLMs) to generate synthetic data for model training has become increasingly popular in recent years. While LLMs are capable of producing realistic trai…