Jan 30, 2024 · Here we introduce LLaMP, a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning-and-acting (ReAct) agents that can dynamically ...
Sep 27, 2024 · LLaMP is a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning-and-acting (ReAct) agents that can dynamically and recursively ...
Jan 30, 2024 · In this work, we implement a multimodal retrieval-augmented generation (RAG) framework LLaMP to connect LLMs with multiple Materials Project (MP) ...
LLaMP—Large Language Model Made Powerful for High-Fidelity ...
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Here, we introduce LLaMP, a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning-and-acting (ReAct) agents that can dynamically ...
LLaMP is a multimodal retrieval-augmented generation (RAG) framework of hierarchical ReAct agents that can dynamically and recursively interact with Materials ...
Jan 30, 2024 · Here we introduce LLaMP, a multimodal retrieval-augmented generation (RAG) framework of multiple data-aware reasoning-and-acting (ReAct) agents.
This paper introduces LLaMP, a large language model (LLM) designed to retrieve and distill high-fidelity materials knowledge. LLaMP aims to improve upon ...
Oct 10, 2024 · Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
Jan 30, 2024 · Here we introduce LLaMP,a multimodal retrieval-augmented generation (RAG) framework of multipledata-aware reasoning-and-acting (ReAct) agents ...
In this work, we propose LLaMP, a multimodal retrieval-augmented generation (RAG) framework leveraging hierarchical reasoning-and-acting (ReAct) agents to ...
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