Koki Oguri
Papers
1
Total Citations
5
H-Index
1
About
Dr. Koki Oguri is a rising researcher at the forefront of multimodal AI and autonomous agent systems. His work centers on enhancing Large Language Models (LLMs) with advanced memory and planning capabilities, enabling them to operate as more intelligent, context-aware agents in complex environments such as robotics, gaming, and API integration. His most cited work, “RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents” (2024), introduces a novel framework that allows agents to dynamically retrieve and leverage past experiences—a capability that mirrors innate human decision-making. This contribution addresses a critical limitation in current LLM agents: the inability to reflect on prior actions to improve future performance. With 5 citations in its first year, this paper is already shaping discussions in the field. Dr. Oguri’s research promises to bridge the gap between static language models and truly adaptive, learning-based agents, making him a notable voice in the next wave of AI development.
Research Focus
Key Achievements
Top Papers
- 1