Choosing the Right Representations: Task-Aware Fusion for Multimodal Materials Language Models

Multimodal language models for materials science rely on effective mechanisms for incorporating three-dimensional structural representations. MatterChat addresses this challenge using a large bridge module between a pretrained structural encoder and the LLM, together with a separate contrastive alignment stage. We instead ask whether this bridge can adaptively select and combine representations from multiple structural encoders according to the target property. We find that supervised and generative material encoders provide complementary information for property prediction. For Zatom-1, this information is not concentrated in the final layer. Instead, the most informative representations occur at different depths depending on the target property. This motivates Task-Conditioned Attention Fusion (TCAF), a lightweight attention-based bridge that uses task-conditioned latent queries to combine CHGNet features with intermediate Zatom-1 representations. The bridge is trained directly on downstream prediction tasks, allowing representation selection and material-language alignment to be learned jointly without separate contrastive pretraining. Across classification and regression tasks, TCAF outperforms MatterChat, with up to 47.8% lower RMSE, while using substantially fewer trainable parameters and halving the overall training time.