arXiv:2608. 07867v1 Announce Type: new Abstract: Multimodal emotion recognition often treats self-reported labels as reliable supervision while overlooking self-report unreliability and cross-modal conflict.
By Bojing Hou, Ruohao Li, Yitong Zhu, Luwen Yu, Yuyang Wang
arXiv:2606. 26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.
By Sinie van der Ben, Rapha\"el Baur, Yannick Metz, Mennatallah El-Assady
Sofroniew et al. (2026) showed that emotion concepts in Claude Sonnet 4.5 are encoded as vectors whose geometry mirrors human affect psychology. This study replicates that finding using the base pretrained model google/gemma-2-27b, generating 205,200 Claude Sonnet 4.5 stories, extracting 171 emotion vectors, and recovering a similar affective circumplex with principal components explaining comparable variance. The analysis further identifies a sharp geometric seam at layers 22‑26, demonstrates that much of the geometry already exists in static token embeddings, and shows that the geometry predicts token‑level co‑activation with high correlation.
By Adam Hollowell
arXiv:2608. 15619v1 Announce Type: new Abstract: Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipline.
By Keito Inoshita
The paper introduces Evidential Multimodal Transformers with Dempster-Shafer Fusion (EMT-DS) for emotion recognition, which models predictions as Dirichlet distributions to separate epistemic and aleatoric uncertainty. EMT-DS fuses modality-specific belief masses using Dempster's Rule, explicitly quantifying conflict and producing more diffuse predictions when evidence is contradictory. Across four benchmarks, EMT-DS outperforms baselines, reduces calibration error, remains robust to noise, and its conflict metric can flag misclassifications for selective prediction.
By Muhammad Usama, Yeoreum Jung
arXiv:2606. 07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect.
By Lemei Zhang, Peng Liu, Hans Dahle Kvadsheim, August S{\ae}tre Aasv{\ae}r, Shuer Ye, Reza Bonyadi, Maryam Ziaei, Jon Atle Gulla