A Unified 2D-3D Encoding and Joint Geometric Metric Framework for Olfactory Mixture Perception

Existing olfactory perception methods often fail to capture critical three-dimensional spatial information and the distinction between causal odorants and environmental noise, limiting their ability to generalize across complex mixture scenarios. In this paper, we propose a unified molecular encoding framework that synergizes Causal Attention Learning with a joint geometric metric space to accurately map molecular features to perceptual outcomes. First, we integrate 2D topology with 3D spatial configurations, constructing a model that effectively addresses the representation deficiencies inherent in traditional topological graphs. Second, we introduce a causal aggregation module reinforced by orthogonal constraints to explicitly disentangle true odorants from background solvents, ensuring robustness in data-scarce environments. Crucially, we propose a joint hyperbolic-cosine metric space for mixture similarity prediction; the hyperbolic component naturally encodes the hierarchical generative ancestry of odorants, while the cosine component captures the holistic, permutation-invariant nature of complex mixtures. Extensive experiments demonstrate that integrating these diverse geometric priors with causal representations yields state-of-the-art performance on complex mixture benchmarks, while providing profound biological interpretability within the latent olfactory space. The code is available at https://anonymous.4open.science/r/3D-2D-Geo-metric-mix-A2CE.