LATIDIA · Investigación

RSIFlow: Flujos de trabajo de auto-mejora adaptativa con un metaagente en evolución

arXiv: 2610.21791v1Tipo de anuncio: nuevo Resumen: Los agentes del modelo de lenguaje grande (LLM) han demostrado capacidades sólidas en tareas complejas, incluida la generación de código, el uso de herramientas y la respuesta a preguntas de varios saltos.

La noticia

arXiv:2610.21791v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question answering. Existing self-improvement methods enhance agent performance through model optimization, harness evolution, and executable artifact refinement, while recursive self-improvement (RSI) further extends optimization to the improvement mechanisms themselves. However, adapting self-improvement workflows to evolving performance bottlenecks while improving workflow orchestration through execution feedback remains a key challenge. We propose \textbf{RSIFlow}, a \textbf{R}ecursive \textbf{S}elf-\textbf{I}mprovement framework that integrates adaptive work\textbf{flow} orchestration with meta-agent capability evolution. RSIFlow diagnoses performance bottlenecks from task-agent trajectories and environmental feedback to dynamically select component-specific improvement workflows. The meta-agent then distills workflow outcomes