Adaptive LLM-Diffusion Integration for Robust Crystal Generation

Text-guided generative models have shown strong promise for crystal generation by incorporating natural-language metadata into the generative process to produce valid and stable materials conditioned on textual descriptions. However, they have some inherent limitations: their LLM backbones are sensitive to the ordering of conditions within the prompt, the LLM and generative model are coupled through a fixed, sample-agnostic diffusion timestep $\tau$, and their effectiveness has largely been demonstrated using only a limited set of LLM architectures. In this work, we introduce , a robust and adaptive LLM-diffusion framework for crystal generation. First, we improve robustness to prompt ordering through order-invariant training augmentation and auxiliary condition-specific objectives that encourage consistent generation across different permutations of the conditioning information. Second, we replace the fixed diffusion timestep with a lightweight per-sample policy network ${\pi }_{\varphi }$ that adaptively determines the amount of diffusion refinement required for each LLM-generated structure. Finally, we benchmark across a diverse collection of open-source LLMs spanning multiple model families and scales, and observe that , across all backbones, consistently outperforms existing baselines in generating valid, novel, and stable materials, providing a broad assessment of the generality and robustness of LLM-diffusion integration for crystal generation.