SciCore-Mol: Modular Framework Enhances LLMs for Molecular Reasoning
Check this out: SciCore-Mol is a groundbreaking framework aimed at improving large language models (LLMs) by adding modular cognitive components related to molecules. This approach links specific language symbols with molecular structures and reaction data. It features three interconnected parts: a module that understands topology, one that generates molecules using latent diffusion, and another that reasons about reactions. These components connect with the LLM using learned representations, allowing for deeper information sharing beyond just text. Tests on different chemical tasks show that SciCore-Mol significantly enhances both molecular comprehension and generation, addressing the challenges of information loss and unclear semantics in scientific text reasoning.
Key facts
- SciCore-Mol is a modular framework for augmenting LLMs with molecular cognition modules.
- It includes three pluggable modules: topology-aware perception, latent diffusion-based molecular generation, and reaction-aware reasoning.
- Modules are coupled to the LLM backbone through learned representation interfaces.
- The framework aims to reduce information loss and semantic noise in text-based reasoning about molecules.
- Experiments show strong performance on molecular understanding and generation tasks.
- The work is published on arXiv with ID 2605.22287.
- The approach addresses the gap between discrete language and topological/continuous molecular data.
- The framework enables richer information exchange than text-only tool feedback.
Entities
Institutions
- arXiv