SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules

• URL: http://arxiv.org/abs/2605.22287v1
• Date: Thu, 21 May 2026 10:37:53 GMT
• Title: SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules
• Abstract summary: SciCore-Mol is a modular framework that bridges the gap between discrete linguistic symbols and topological molecular or continuous reaction data.<n>Our experiments on diverse chemical tasks demonstrate that SciCore-Mol achieves strong comprehensive performance across molecular understanding, generation, reaction prediction, and general chemistry knowledge.<n>This work provides a systematic blueprint for equipping Large Language Models with scientific expertise through decoupled, pluggable, and flexibly orchestrated modules.
• Score: 37.73750078629302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are central to the one-for-all intelligent paradigm, but they face a fundamental challenge when dealing with heterogeneous scientific data such as molecules: the inherent gap between discrete linguistic symbols and topological molecular or continuous reaction data leads to significant information loss and semantic noise in text-based reasoning. We propose SciCore-Mol, a modular framework that bridges this gap through three deeply integrated pluggable cognitive modules: a topology-aware perception module, a latent diffusion-based molecular generation module, and a reaction-aware reasoning module. Each module is coupled to the LLM backbone through learned representation interfaces, enabling richer information exchange than is possible with text-only tool feedback. Our experiments on diverse chemical tasks demonstrate that SciCore-Mol achieves strong comprehensive performance across molecular understanding, generation, reaction prediction, and general chemistry knowledge, with an 8B-parameter open-source system that is competitive with and in several dimensions surpasses proprietary large models. This work provides a systematic blueprint for equipping LLMs with scientific expertise through decoupled, pluggable, and flexibly orchestrated modules, with direct implications for drug design, chemical synthesis, and broader scientific discovery.

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