arXiv AI
Jun 26
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs
arXiv:2606. 26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.
The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It
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arXiv Computation and Language
Aug 27
When Emotion Becomes Trigger: Emotion-style dynamic Backdoor Attack Parasitising Large Language Models
The paper introduces Paraesthesia, a dynamic backdoor attack that uses emotionally styled inputs as triggers for large language models. By mapping target emotions into a valence–arousal space and rewriting a small subset of clean samples, the attack achieves over 98% success while minimally affecting clean performance. Experiments on four major LLMs show that the trigger cannot be fully explained by token-level cues and remains robust against several filtering and mitigation techniques.
arXiv AI
Aug 20
Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities
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 Machine Learning
Aug 27
Detection != Reliable Control: Decodable Empathy Directions Yield at Most Partial Shifts in Automated Empathy Scores
The study examines whether a decodable "empathy" direction can be used as a reliable causal lever in language models. Using EPITOME-derived facets of Recognition (cognitive) and Resonance (affective) across three instruction‑tuned LLMs, the authors find that while affective steering can partially raise affective scores, cognitive steering shows inconsistent or unmeasurable effects. The results highlight that decodability does not guarantee reliable control, especially for cognitive empathy, and that measurement sensitivity must be explicitly checked.
arXiv Computation and Language
Sep 1
Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts
The study investigates how large language models (LLMs) assess psychological distress in online posts from six identity‑based communities. Through a perspectivist annotation task, 321 participants provided 9,587 judgments on 1,198 Reddit posts, revealing modest in‑group agreement (OR = 1.18) that varies across communities. When evaluated against these community‑specific labels, open‑weight LLMs consistently over‑estimate distress—achieving only 31–44% accuracy on posts perceived as none‑to‑mild—while newer models like GPT‑5 and Gemini 2.5 Pro show similar inflation, whereas Claude Opus 4 is more conservative. "whyItMatters":"The findings highlight that miscalibrated distress detection by LLMs can disproportionately impact the very communities they aim to serve, underscoring the need for equitable AI deployment in mental‑health contexts."
Hugging Face Trending Papers
2d ago
When Do Language-Grounded Explanations Help? A Graph-Bottleneck for Farm Monitoring Interpretable Sheep Facial Pain
Automated pain recognition from facial expression could make continuous welfare assessment practical in sheep, but adoption depends on trust: a stockperson cannot act on a score that arrives without j...
arXiv Machine Learning
Jun 16
Faithful Action-unit Causal Reasoning for Counterfactually Faithful Emotion Explanations
arXiv:2606. 15779v1 Announce Type: cross Abstract: Multimodal models can name the action units (AUs) behind a facial emotion, but their AU->emotion rationales are typically plausible rather than faithful: nothing forces the AUs a model invokes to be the AUs that actually drive its prediction.
arXiv AI
2d ago
When Do Language-Grounded Explanations Help? A Graph-Bottleneck for Farm Monitoring Interpretable Sheep Facial Pain
The study investigates whether language‑grounded explanations improve trust in automated sheep pain recognition from facial expressions. By grounding a model in the Sheep Pain Facial Expression Scale (SPFES) and testing attention‑based explanations, the authors find that such explanations are largely ineffective. They then replace the appearance bypass with a concept bottleneck that reads only SPFES concept scores, which slightly reduces performance but yields demonstrably learned concepts and better recovery of minority pain states.
arXiv AI
Jun 2
A Shared Valence Axis Across Modern LLMs and Human EEG: The Saturation Regularity
arXiv:2606. 00129v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features increasingly align with human cognition.
arXiv AI
Jun 15
EmoMind: Decoding Affective Captions from Human Brain fMRI
arXiv:2605. 16739v2 Announce Type: replace-cross Abstract: Decoding visual experience from brain activity has advanced substantially, but current brain-to-text systems largely recover semantic content while discarding affect.
arXiv AI
Aug 13
TEMPER: Testing Emotional Perturbation in Quantitative Reasoning
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
arXiv Machine Learning
Jun 30
Internal-State Probes Read the Situation, Not the Action: Three Negative Results for Pre-Action Misalignment Monitoring
arXiv:2606. 30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated.