2 citations · 3 across the 6 of their papers we have counts for
6 papers
Recursive Harness Self-Improvement
Hyunin Lee, Jinglue Xu, Jeffrey Seely +3
Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This…
Learning to Orchestrate Agents in Natural Language with the Conductor
Stefan Nielsen, Edoardo Cetin, Peter Schwendeman +3
Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In this work, we introduce a new ki…
Large Language Models as Particle Swarm Optimizers
Yamato Shinohara, Jinglue Xu, Tianshui Li +1
Optimization problems often require domain-specific expertise to design problem-dependent methodologies. Recently, several approaches have gained attention by integrating large lan…
Automatic Adaptation Rule Optimization via Large Language Models
Yusei Ishimizu, Jialong Li, Jinglue Xu +3
Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adapta…
Exploring the Improvement of Evolutionary Computation via Large Language Models
Jinyu Cai, Jinglue Xu, Jialong Li +3
Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains. However, as the complexity of problems increases, the limitations of E…
Large Language Models Synergize with Automated Machine Learning
Jinglue Xu, Jialong Li, Zhen Liu +5
Recently, program synthesis driven by large language models (LLMs) has become increasingly popular. However, program synthesis for machine learning (ML) tasks still poses significa…