arXiv:2610.17164v1 Announce Type: new Abstract: The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecul...
The study fine‑tunes the Uni‑Mol2 molecular foundation model on the GS‑LF benchmark for multi‑label odor descriptor prediction. The resulting model matches or surpasses state‑of‑the‑art baselines on the primary benchmark and successfully transfers to four downstream olfactory tasks—including cross‑dataset prediction, odorless vs. odorous classification, enantiomer evaluation, and odor mixture discriminability—without further deep‑learning training. The enantiomer analysis demonstrates that 3D molecular representations can distinguish mirror‑image molecules, a capability lacking in 2D graph models, though predicting stereochemistry’s perceptual effects remains unresolved.
arXiv:2607. 06402v1 Announce Type: cross Abstract: Images tell us what a scene looks like, but rarely what it would feel like to be there.
The paper introduces a bio‑inspired deep learning framework that models olfactory perception of complex chemical mixtures. It constructs neural response curves for molecule‑receptor interactions, fuses attention‑weighted multi‑receptor and concentration‑dependent multi‑molecule data, and transfers knowledge from molecular associations to improve mixture recognition. The model achieves 92.2% accuracy and offers a generalizable computational pathway from chemical blending to perceptual formation.
arXiv:2609.05694v1 Announce Type: new Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to o...
The study evaluates the reliability of shared odor descriptor words across four public corpora from Pyrfume, finding substantial disagreement (I² = 80 %) and limited agreement on descriptor application (median tetrachoric = 0.795, κ = 0.413). Only a fraction of the achievable variance in odor perception is captured by current models and descriptor sets, with valence emerging as the primary missing component. Even with extensive model capacity and merged corpora, the gap remains, indicating that valence must be measured directly to improve machine olfaction.
arXiv:2602.04192v3 Announce Type: replace Abstract: Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging proble...
arXiv:2607. 24848v1 Announce Type: cross Abstract: Pretrained molecular encoders are commonly evaluated through downstream prediction, but predictive accuracy alone does not establish that a learned representation captures reproducible scientific structure, adds information beyond strong conventional baselines, or transfers out of distribution.
AromaGen is a 12‑channel wearable system that translates free‑form natural‑language descriptions into olfactory experiences by selecting from 12 base odorants that span a semantically derived olfactory space. The system supports iterative refinement via natural‑language feedback. In a study with 60 participants and 50 benchmark smells, users could identify AromaGen‑generated scents above chance, and AI‑generated compositions performed comparably to human experts, with feedback improving perceived similarity.
arXiv:2511. 20544v2 Announce Type: replace-cross Abstract: While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines.
arXiv:2607. 15018v1 Announce Type: cross Abstract: High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables.
The study audits six vision‑language models (VLMs) to assess whether they consistently encode affective qualities of 3D shapes, using Kansei adjective pairs as affective axes. Across ten ShapeNet categories, models show moderate agreement (mean rank correlation 0.36) that is lower than geometric controls but higher than unrelated adjective pairs, with convergence varying widely by category and axis. The authors demonstrate how this audit informs a UI prototype that selectively exposes Kansei descriptors for generative design interfaces.