arXiv:2606. 01906v1 Announce Type: new Abstract: Emotions evolve through the dynamics of conversation, and understanding their transition structure is foundational to applications ranging from mental-health screening to dialogue systems.
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.
arXiv:2606. 26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.
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.
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.
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.
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.
The paper demonstrates that a single internal direction in modern language models—called the valence axis (V-axis)—captures how positive or negative a sentence feels. By using only nine emotion category names and 50 short narrative paragraphs per emotion, the authors identify this axis via principal component analysis of frozen encoder embeddings, achieving 93% of supervised performance on SST‑2 and strong correlations with human valence ratings across images, audio, and brain recordings. The method transfers across modalities without target‑modality labels, but works only for continuous attributes and is specific to certain model families.
arXiv:2606. 31442v1 Announce Type: new Abstract: Emotion-sensing AI is rapidly becoming embedded in vehicles, home appliances, dialogue agents, and social infrastructure, giving rise to a sphere in which emotion is no longer confined to individual experience but is instead observed and computed at a societal scale, a domain we term the Affectosphere.
WELD is the first dataset that combines long‑term (30.1 months), naturalistic workplace recordings, a stable small‑team social structure, and a fully passive sensing protocol approved by institutional review boards. It contains 733,780 per‑frame seven‑class facial‑expression probability vectors from 49 employees of a Chinese software company, making it the longest in‑the‑wild emotion corpus that supports both within‑individual longitudinal and within‑team relational analyses. The authors validate the corpus by reproducing known affective phenomena and report four novel findings, including variance decomposition of daily valence, hidden Markov emotional regimes, turnover prediction metrics, and systematic over‑prediction of “angry” on neutral Asian faces by an off‑the‑shelf FER model.
arXiv:2608.28932v1 Announce Type: new Abstract: Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-o...
arXiv:2610.35411v1 Announce Type: new Abstract: We measure the emotion geometry of six LLMs spanning Google's Gemma 2 family. Using an extraction pipeline established by Sofroniew et al. (2026) on Cl...
arXiv:2601. 00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns.