Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features still relies primarily on external observation. This reliance leads to superficial explanations inferred from observed model behavior and computational inefficiency from collecting such behavioral evidence at scale.
arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
By Madhav Agarwal, Sotirios A. Tsaftaris, Laura Sevilla-Lara, Steven McDonagh
The paper introduces DAN, a training‑free inference‑time framework that improves affective reasoning in multimodal large language models. It combines a Hierarchical Emotional Reasoning Chain (HERC) to better capture fine‑grained visual cues and a Contrastive Discriminative Visual Pruning (CDVP) module to isolate discriminative tokens for semantically similar emotions. Experiments show significant gains, notably a +10.47% improvement on the WebEmo25 benchmark with Qwen3‑VL‑8B‑Instruct.
By Cheng Ye, Weidong Chen, Zhaobo Qi, Beier Zhu, Zhendong Mao
The study examines how emotions are represented across layers of large language models (LLMs) by probing eight 1B–9B open‑weight models on three datasets (Twitter, Reddit, autobiographical narratives). It finds that the optimal probing layer varies systematically with the dataset, moving from near‑input layers to deeper layers, and that targeted forward‑pass interventions on these layers degrade performance more than random interventions. Additionally, the selected layers transfer across datasets and emotion categories, and early‑exit representations from these layers outperform full‑depth exits by an average of 6.9 percentage points.
By Tian Fang, Ga\"el Guibon, Davide Buscaldi
arXiv:2609.01322v1 Announce Type: cross
Abstract: In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff i...
By Mian Zhong, Katherine A. Keith, Anjalie Field
arXiv:2610. 29430v1 Announce Type: new Abstract: Multimodal multi-label emotion recognition is important for affect-aware systems, but models trained with complete text, visual, and acoustic streams often degrade sharply when some streams are unavailable at deployment.
By Chuangxin Zhao, Meimei Zhang, Canran Xiao, Jiachen Luo, Jiajun He, Kaijie Chen, Huy Phan, Tomoki Toda, Bi\"orn W. Schuller
The paper investigates how large language models (LLMs) handle affective state reasoning, a type of implicit conversational state that persists across topic shifts. It introduces Self‑targeted Internal Attention Boosting (SIAB), a method that identifies and amplifies sparse affective attention heads using causal localization and the model’s own behavior, without relying on external labels. Experiments on ECC and GoEmotions datasets across multiple LLMs and prompting settings show that SIAB improves macro‑F1 scores by up to +10.3 points on ECC and +5.5 on GoEmotions, and further gains are achieved when combined with external supervision.
By Wenqiu Tang, Zhen Wan, Yihui Wang, Takahiro Komamizu, Ichiro Ide
arXiv:2601.08058v2 Announce Type: replace-cross
Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigg...
By Zhenghao He, Guangzhi Xiong, Bohan Liu, Sanchit Sinha, Aidong Zhang
The paper introduces EmoVec, a lightweight framework that enables controllable affective generation in large language models by steering latent vectors. EmoVec identifies emotion-specific directions from paired neutral and emotion-conditioned responses using contrastive activation addition, then refines these directions through task-specific debiasing and principal subspace removal. During inference, the refined vectors are injected into the final residual stream with static or scenario-adaptive scaling, allowing continuous control over emotional intensity without updating model weights, and experiments across three LLMs and eight emotions demonstrate improved emotional salience while preserving semantic content, fluency, and coherence.
By Xixian Yong, Siyuan Chang, Yingying Zhang, Xian Wu, Xiao Zhou
arXiv:2609.36563v1 Announce Type: new
Abstract: Visual emotion recognition commonly assumes that all evidence required for prediction is contained in the observed image or video. Yet the same visible...
By Yihao Qian, Runhao Zeng, Sicheng Zhao, Feng Liang, Hongmin Cai, Mingkui Tan
arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
By Sujung Oh, Jung Uk Kim, Sangmin Lee
In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in constructing text representations for adjustment:...