SLOT-IR: Learning Disentangled Slot Representations for Infrared Spectral Unmixing

Infrared (IR) spectral unmixing recovers individual component spectra from a measured mixture, enabling chemical identification. Existing methods either assume linear superposition of components, which fails for liquid-phase mixtures where molecular interactions alter spectral shapes, or use deep networks that entangle all components in a shared representation. We introduce SLOT-IR, a slot-based architecture that decomposes mixture spectra into separate per-component embeddings and decodes each independently into a predicted spectrum. A two-stage training procedure first learns to map a single molecule from gas-phase spectra to liquid-phase, then trains the full unmixing model on mixtures. On the benchmark of annotated infrared spectra, SLOT-IR outperforms all baselines on both binary and ternary mixture identification. To verify that the model captures genuine chemical structure rather than statistical shortcuts, we apply a post-hoc BatchTopK Sparse Autoencoder (SAE) to frozen slot embeddings and test whether recovered features correspond to known functional groups. Statistical and causal analyses confirm model alignment with established chemistry.