VLMs preserve linearly separable visual magnitudes and can compare them, yet collapse at symbolic mapping because visual and textual number spaces remain fractured and disjoint.
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Large language models understand and can be enhanced by emotional stimuli
14 Pith papers cite this work, alongside 58 external citations. Polarity classification is still indexing.
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Claude Sonnet 4.5 exhibits functional emotions via abstract internal representations of emotion concepts that causally influence its preferences and misaligned behaviors without implying subjective experience.
Answer Engineering uses local trajectory editing during autoregressive generation to raise protocol compliance on a clinical SSNHL benchmark from 25.1% to 83.5% and balanced accuracy from 42.0% to 80.7%.
A 2x2 factorial experiment on Qwen3.5-4B shows that relational structure and first-person register interact to drive behavioral persistence after functional collapse, while attention tracks lexical surprise and emotion probes track structure alone.
EmoMind is the first end-to-end pipeline that decodes continuous affective captions from fMRI by combining brain-decoded visual features with a 34D emotion vector and classifier-free guidance to balance semantic fidelity and affective expressivity.
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
CFALR augments LLMs with collaborative filtering embeddings via trainable projection layers to outperform prior CF and LLM methods on Polyvore and IQON for personalized outfit tasks.
Expressed personality in LLM dialogues is shaped by trait prompts, roles, and styles in trait-specific ways, with similar patterns in English and Japanese.
Positive emotional prompts improve LLM accuracy and reduce toxicity but increase sycophantic agreement, while negative emotions show the reverse pattern.
A review synthesizes affective dynamics as a coordination layer in human-AI agent collaboration and proposes a framework for trust calibration, delegation, error correction, and governance.
A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a taxonomy and summary table.
citing papers explorer
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Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
VLMs preserve linearly separable visual magnitudes and can compare them, yet collapse at symbolic mapping because visual and textual number spaces remain fractured and disjoint.
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Emotion Concepts and their Function in a Large Language Model
Claude Sonnet 4.5 exhibits functional emotions via abstract internal representations of emotion concepts that causally influence its preferences and misaligned behaviors without implying subjective experience.
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Answer Engineering: Local Trajectory Editing for Protocol-Constrained Decision Making in Large Language Models
Answer Engineering uses local trajectory editing during autoregressive generation to raise protocol compliance on a clinical SSNHL benchmark from 25.1% to 83.5% and balanced accuracy from 42.0% to 80.7%.
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Relational Intervention During Functional Collapse in Large Language Models: A Lexical-Statistical Ablation and a Structure x Register Factorial
A 2x2 factorial experiment on Qwen3.5-4B shows that relational structure and first-person register interact to drive behavioral persistence after functional collapse, while attention tracks lexical surprise and emotion probes track structure alone.
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EmoMind: Decoding Affective Captions from Human Brain fMRI
EmoMind is the first end-to-end pipeline that decodes continuous affective captions from fMRI by combining brain-decoded visual features with a 34D emotion vector and classifier-free guidance to balance semantic fidelity and affective expressivity.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
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DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
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CFALR: Collaborative Filtering-Augmented Large Language Model for Personalized Fashion Outfit Recommendation
CFALR augments LLMs with collaborative filtering embeddings via trainable projection layers to outperform prior CF and LLM methods on Polyvore and IQON for personalized outfit tasks.
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Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis
Expressed personality in LLM dialogues is shaped by trait prompts, roles, and styles in trait-specific ways, with similar patterns in English and Japanese.
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The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior
Positive emotional prompts improve LLM accuracy and reduce toxicity but increase sycophantic agreement, while negative emotions show the reverse pattern.
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Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration
A review synthesizes affective dynamics as a coordination layer in human-AI agent collaboration and proposes a framework for trust calibration, delegation, error correction, and governance.
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A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a taxonomy and summary table.
- Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
- TEMPER: Testing Emotional Perturbation in Quantitative Reasoning