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This paper discusses how a deterministic artificial agent can display hedonic place preference behavior, with implications for understanding consciousness and free will.
AffectOmni is a GRPO-trained framework for verifiable affective reasoning in multimodal large language models, introducing People Focus and Temporal Order rewards to enhance people-centric evidence selection and temporally structured reasoning, with experiments showing improvements over 7B scale baselines.
This paper introduces a dual-tower architecture that integrates EEG data with literature-informed environmental priors for affective-state classification, demonstrating improved accuracy over EEG alone while highlighting methodological advances rather than causal exposure-affect associations.
The paper presents FRONT 3.1, a conceptual AI architecture that incorporates interoceptive and affective states to emulate biological cognition, featuring components like a digital somatic body and pre-causality flow.
This research paper explores emotion-sensitive neurons in multimodal foundation models, revealing shared affective mechanisms between speech and facial emotion recognition through causal interventions and cross-modal analysis.
This paper evaluates the use of self-supervised learning on PPG data for real-life emotion detection, finding that general representations fail without individual personalization.
CUE-Bench is a Chinese benchmark for affective stance that models explicit-implicit polarity interaction and provides intent and fine-grained emotion annotations, showing gains in emotion recognition and pragmatic intent detection.
This paper proposes an expanded formulation of valence and arousal in an active inference model of human driving, conditioning affective estimates on predicted future outcomes and evaluating it in interactive driving scenarios.
This paper introduces NTDH, a complex-reasoning framework for comprehensive affective analysis that unifies sentiment and emotion prediction tasks. Trained with SFT and GRPO on Qwen3-8B, it achieves strong results on SemEval-2018 EI-reg with a Pearson correlation of 0.862.
Introduces OneEmo, a unified multimodal reasoning model for emotion perception, understanding, and interaction, along with the EmoWorld-130K dataset and Emo-Chord reinforcement learning strategy.
This paper explores whether valence features can reflect morality in natural language by analyzing human annotations of moral scenarios, finding significant correlations and achieving a Matthew's correlation coefficient of 0.764 for binary morality classification.
Introduces SCoPE, a lightweight module for emotion recognition in conversations that models speaker-specific emotional priors and uses emotion shift prediction to dynamically fuse prior and multimodal evidence, achieving state-of-the-art on IEMOCAP.
The paper introduces a graph-regularized deep learning framework for EEG-based emotion recognition that incorporates psychologically-grounded emotion topology into the training objective, achieving up to +5.42% accuracy and 39% reduction in psychologically implausible misclassifications on SEED datasets.
Proposes a fine-grained multimodal framework with a Binary Advantage-weighting Ranking Loss for automatic depression detection, achieving state-of-the-art results on D-vlog and LMVD datasets.
This paper presents a zero-shot evaluation of three LLMs (Claude, GPT-5.4, Gemini) on a 13-class emotion classification task, finding no model exceeds 39.9% accuracy and revealing systematic failures on specific emotions such as love, confusion, and shame.
PRISM is a novel framework for cross-subject EEG emotion recognition that combines prioritized channel importance weighting via a lightweight expert ensemble with semi-supervised domain adaptation using confidence-filtered pseudo-labels, achieving state-of-the-art results on DEAP, DREAMER, and SEED datasets.
This paper introduces the concept of the 'Affectosphere' and argues that emotion AI cannot fully determine the meaning of an individual's emotion due to irreducible uncertainty, leading to the norm of 'affective sovereignty' where the experiencing subject retains final interpretive authority.
This paper evaluates twelve recent text encoders on their ability to encode affective cues from three psychological emotion theories, finding that instruction-aware open-weight encoders match or exceed proprietary ones at word level, while task-tuned embeddings are superior at sentence level.
This paper investigates using a Transformer-based generative model to learn emotional body motions from motion-capture data of Japanese actors, generating motions conditioned on discrete emotion labels. Evaluations show the generated motions improve emotion recognition when used for data augmentation and enable smooth transitions between emotion intensities.
This paper introduces MER-R1, a reinforcement learning framework that synergizes fast and slow thinking for multimodal emotion recognition. It achieves state-of-the-art performance by jointly optimizing recall and precision through dual-objective disentanglement and slow-fast confidence calibration.