CatalyticMLLM unifies property prediction and inverse design for catalytic materials inside one graph-text multimodal LLM and reports better performance than decoupled baselines.
arXiv preprint arXiv:2410.04223 (2024)
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The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.
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CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
CatalyticMLLM unifies property prediction and inverse design for catalytic materials inside one graph-text multimodal LLM and reports better performance than decoupled baselines.
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Agentic Reasoning for Large Language Models
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.