AINeutralarXiv – CS AI · May 97/10
🧠Researchers introduce Chameleon, a dataset of 5,001 contextual psychological profiles revealing that 74% of user behavior variance stems from situational context (state) rather than personality traits (26%). The study finds language models are state-blind, responding similarly regardless of context, while reward models inconsistently evaluate the same users differently across scenarios.
AIBearisharXiv – CS AI · Mar 167/10
🧠Researchers introduced CoRE, a benchmark testing whether large language models can reason about human emotions through cognitive dimensions rather than just labels. The study found that while LLMs capture systematic relations between cognitive appraisals and emotions, they show misalignment with human judgments and instability across different contexts.
AINeutralarXiv – CS AI · 3d ago6/10
🧠Researchers present Empathic Prompting, a framework that integrates facial expression recognition into multimodal LLM conversations to capture and embed users' emotional cues as contextual signals. The system operates unobtrusively through a locally deployed DeepSeek instance and demonstrates coherent integration of non-verbal input in a preliminary evaluation (N=5), with potential applications in healthcare and education.
AIBullisharXiv – CS AI · 3d ago6/10
🧠Researchers introduce E3AD, an emotion-aware vision-language-action model that enhances autonomous driving systems by interpreting passenger emotional states alongside driving commands. The framework combines semantic understanding with emotion detection (Valence-Arousal-Dominance model) and dual-pathway spatial reasoning to improve both trajectory planning and human-vehicle comfort alignment.
AINeutralarXiv – CS AI · 4d ago6/10
🧠Researchers introduce Variance-Regularised Pruning (VR), a neural network pruning technique that reduces model size while maintaining robust performance across diverse users. The method balances computational efficiency with cross-participant stability in affective computing systems, achieving 80% sparsity without sacrificing reliability on the AGAIN emotion recognition dataset.
AINeutralarXiv – CS AI · Apr 206/10
🧠Researchers demonstrate that integrating facial expression analysis into large language model prompts improves empathetic tutoring responses without requiring model retraining. Testing across three major LLM backbones with 960 multi-turn conversations, Action Unit estimation-based conditioning consistently enhanced emotional responsiveness while maintaining pedagogical quality.
🧠 GPT-5🧠 Claude🧠 Gemini
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers investigate how large language models represent emotions in their latent spaces, discovering that LLMs develop coherent emotional representations aligned with established psychological models of valence and arousal. The findings support the linear representation hypothesis used in AI transparency methods and demonstrate practical applications for uncertainty quantification in emotion processing tasks.
AINeutralarXiv – CS AI · Apr 106/10
🧠Researchers introduce A-MBER, a benchmark dataset designed to evaluate AI assistants' ability to recognize emotions based on long-term interaction history rather than immediate context. The benchmark tests whether models can retrieve relevant past interactions, infer current emotional states, and provide grounded explanations—revealing that memory's value lies in selective, context-aware interpretation rather than simple historical volume.
AINeutralarXiv – CS AI · Mar 276/10
🧠Researchers have developed TAAC, a framework for trustable audio-based depression diagnosis that protects user identity information while maintaining diagnostic accuracy. The system uses adversarial loss-based subspace decomposition to separate depression features from sensitive identity data, enabling secure AI-powered mental health screening.
AIBullisharXiv – CS AI · Mar 37/108
🧠Researchers have developed Nano-EmoX, a compact 2.2B parameter multimodal language model that unifies emotional intelligence tasks across perception, understanding, and interaction levels. The model achieves state-of-the-art performance on six core affective tasks using a novel curriculum-based training framework called P2E (Perception-to-Empathy).
AINeutralOpenAI News · Mar 215/105
🧠OpenAI and MIT Media Lab have announced a research collaboration focused on developing early methods for studying affective use and emotional well-being in ChatGPT interactions. This partnership aims to better understand how users emotionally engage with AI systems and the psychological impacts of AI conversations.
AINeutralarXiv – CS AI · Mar 164/10
🧠HSEmotion Team developed a fast approach for facial emotion analysis using pre-trained EfficientNet models for the ABAW-10 competition. Their method combines confidence-based predictions with multi-layered perceptrons and sliding window smoothing, achieving significant improvements over existing baselines across four emotion recognition tasks.
