arXiv AI
Jul 8
What Images Cannot Say: Language-Guided Olfactory Representation Learning
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.
AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models
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.
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arXiv Machine Learning
Aug 4
New York Smells: A Large Multimodal Dataset for Olfaction
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 AI
Oct 15
Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors
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...
arXiv Machine Learning
Sep 14
Decoding Mixture Perception through Computational Modeling of Component Interactions
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 Computation and Language
Sep 28
Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
The paper introduces UniSandbox, a decoupled evaluation framework with controlled synthetic datasets, to study whether understanding informs generation in Unified Multimodal Models. Results show a notable understanding‑generation gap, especially in reasoning generation and knowledge transfer. Explicit Chain‑of‑Thought (CoT) in the understanding module bridges this gap, and self‑training can internalize CoT for implicit reasoning during generation; query‑based architectures also exhibit latent CoT‑like properties that aid knowledge transfer.
arXiv Machine Learning
Aug 3
GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System
arXiv:2607. 29213v1 Announce Type: cross Abstract: Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effective fusion of these heterogeneous modalities remains challenging, hindering both the joint modeling of multimodal signals and adaptation to evolving user intent.
arXiv Machine Learning
Aug 27
A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks
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.
Hugging Face Trending Papers
Jul 28
Sense it with your eyes: Sensation Generation and Understanding for Advertisements
Sensory advertising evokes human senses through visual cues, enabling audiences to mentally simulate experiences and increasing persuasive impact. Despite the recent increase in using AI in generating and understanding creative and persuasive content, how advertisements visually evoke sensations remains largely unexplored.
arXiv AI
Oct 9
MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface
MetaEncoder is a bi‑encoder model that transforms a pre‑trained Muse‑Glimmer 30B decoder into an instruction‑following decision‑making encoder for multimodal systems. It represents both user requests and candidate options in natural language, leveraging image and video inputs, and uses unidirectional contrastive learning to align requests with candidates across both small closed‑set and large open‑set spaces. Extensive tests on 11 benchmark suites and 190 tasks show MetaEncoder surpasses state‑of‑the‑art multimodal encoders in many areas, while still facing challenges on reasoning‑intensive tasks.
arXiv AI
Aug 21
A Model-Internal Protocol for Assessing Multimodal Models as Integrated Systems
arXiv:2608. 11907v2 Announce Type: replace-cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
Hugging Face Trending Papers
Jul 5
Transferability Between Understanding and Generation in Unified Multimodal Models
Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied. We investigate $\boldsymbol{\mathsf{transferability}}$ in UMMs: whether training a capability on one task improves the same capability on the other without explicit supervision.
arXiv AI
Jul 14
Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning
arXiv:2607. 10678v1 Announce Type: new Abstract: Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states.