An LLM-based agent autonomously generated a SimpleMind configuration, trained, and ran inference for chest X-ray lungs, heart, and ribs segmentation, achieving mean dice scores of 0.96, 0.82, and 0.83.
Towards Autonomous Agents: Adaptive-planning, Reasoning, and Acting in Language Models
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abstract
We propose a novel in-context learning algorithm for building autonomous decision-making language agents. The language agent continuously attempts to solve the same task by self-correcting each time the task fails. Our selected language agent demonstrates the ability to solve tasks in a text-based game environment. Our results show that the gemma-2-9b-it language model, using our proposed method, can successfully complete two of six tasks that failed in the first attempt. This highlights the effectiveness of our approach in enhancing the problem-solving capabilities of a single language model through self-correction, paving the way for more advanced autonomous agents. The code is publicly available at https://github.com/YenCheHsiao/AutonomousLLMAgentwithAdaptingPlanning.
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Autonomous Computer Vision Development with Agentic AI
An LLM-based agent autonomously generated a SimpleMind configuration, trained, and ran inference for chest X-ray lungs, heart, and ribs segmentation, achieving mean dice scores of 0.96, 0.82, and 0.83.