Tootfinder

A Survey of Cognitive Distortion Detection and Classification in NLP
Archie Sage, Jeroen Keppens, Helen Yannakoudakis
https://arxiv.org/abs/2508.09878 https://

A Survey of Cognitive Distortion Detection and Classification in NLP
As interest grows in the application of natural language processing (NLP) techniques to mental health, a growing body of work explores the automatic detection and classification of cognitive distortions (CDs). CDs are habitual patterns of negatively biased or flawed thinking that distort how people perceive events, judge themselves, and react to the world around them. Identifying and addressing them is an important part of therapy. Despite its momentum, the field remains fragmented, with incons…

Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
https://arxiv.org/abs/2508.09996

Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative analysis of three attention mechanisms (i.e., baseline multi-head attention, causal attention, and sparse attention) integrated with Convolutional Neural Networks (CNNs) for radio frequency (RF) signal classification. It proposes a novel CNN-Transformer hybrid architecture that leverages different attention patterns to…

SimViews: An Interactive Multi-Agent System Simulating Visitor-to-Visitor Conversational Patterns to Present Diverse Perspectives of Artifacts in Virtual Museums
Mingyang Su, Chao Liu, Jingling Zhang, WU Shuang, Mingming Fan
https://arxiv.org/abs/2508.07730

SimViews: An Interactive Multi-Agent System Simulating Visitor-to-Visitor Conversational Patterns to Present Diverse Perspectives of Artifacts in Virtual Museums
Offering diverse perspectives on a museum artifact can deepen visitors' understanding and help avoid the cognitive limitations of a single narrative, ultimately enhancing their overall experience. Physical museums promote diversity through visitor interactions. However, it remains a challenge to present multiple voices appropriately while attracting and sustaining a visitor's attention in the virtual museum. Inspired by recent studies that show the effectiveness of LLM-powered multi-agents in p…

Here's my Boles Bells article about aging:
https://bolesbells.com/blog/2025/9/10/three-overlooked-secrets-to-aging-better-than-average-the-science-of-growing-stronger-with-age

Three Overlooked Secrets to Aging Better Than Average: The Science of Growing Stronger With Age — David Boles Bells
What strikes me most is how these patterns all share a common thread of deliberate discomfort: cognitive, physical, and social. The average person's relationship with discomfort fundamentally shifts somewhere between ages twenty five and thirty five, from seeing it as growth to viewing it as somethi

Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media
Yujie Wang, Yunwei Zhao, Jing Yang, Han Han, Shiguang Shan, Jie Zhang
https://arxiv.org/abs/2509.04823

Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media
Digital social media platforms frequently contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. While cognitive-behavioral fixation has been extensively studied in psychology, methods for computationally detecting and evaluating such fixation remain underexplored. To address this gap, we propose a novel framework for assessing cognitive-behavioral fixation by analyzing users' multimodal social media enga…

Cognitive Loop via In-Situ Optimization: Self-Adaptive Reasoning for Science
Newman Cheng, Gordon Broadbent, William Chappell
https://arxiv.org/abs/2508.02789 https://

Cognitive Loop via In-Situ Optimization: Self-Adaptive Reasoning for Science
The capacity for artificial intelligence (AI) to formulate, evolve, and test altered thought patterns under dynamic conditions indicates advanced cognition that is crucial for scientific discovery. The existing AI development landscape falls into two categories: 1) frameworks over non-reasoning models that natively incorporate opinions on how humans think, and 2) reasoning models that abstract precise control of the reasoning intuition away from end users. While powerful, for scientists to maxi…

M$^3$AD: Multi-task Multi-gate Mixture of Experts for Alzheimer's Disease Diagnosis with Conversion Pattern Modeling
Yufeng Jiang, Hexiao Ding, Hongzhao Chen, Jing Lan, Xinzhi Teng, Gerald W. Y. Cheng, Zongxi Li, Haoran Xie, Jung Sun Yoo, Jing Cai
https://arxiv.org/abs/2508.01819

M$^3$AD: Multi-task Multi-gate Mixture of Experts for Alzheimer's Disease Diagnosis with Conversion Pattern Modeling
Alzheimer's disease (AD) progression follows a complex continuum from normal cognition (NC) through mild cognitive impairment (MCI) to dementia, yet most deep learning approaches oversimplify this into discrete classification tasks. This study introduces M$^3$AD, a novel multi-task multi-gate mixture of experts framework that jointly addresses diagnostic classification and cognitive transition modeling using structural MRI. We incorporate three key innovations: (1) an open-source T1-weighted sM…

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning
Rui Pu, Chaozhuo Li, Rui Ha, Litian Zhang, Lirong Qiu, Xi Zhang
https://arxiv.org/abs/2508.03054

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning
Defending large language models (LLMs) against jailbreak attacks is essential for their safe and reliable deployment. Existing defenses often rely on shallow pattern matching, which struggles to generalize to novel and unseen attack strategies. To address this challenge, we propose the Cognitive-Driven Defense (CDD) framework, which targets the underlying structure of jailbreak prompts by applying meta-operations, defined as basic manipulations that conceal harmful intent.CDD emulates human cog…

Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Bosubabu Sambana, Kondreddygari Archana, Suram Indhra Sena Reddy, Shaik Meethaigar Jameer Basha, Shaik Karishma
https://arxiv.org/abs/2506.23503

Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analy…

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
Akash Kundu, Rishika Goswami
https://arxiv.org/abs/2506.18156 https://

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned wi…