Tootfinder

Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
Runlong Ye, Zeling Zhang, Boushra Almazroua, Michael Liut
https://arxiv.org/abs/2506.20062

Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
AI-powered code assistants are widely used to generate code completions, significantly boosting developer productivity. However, these tools typically present suggestions without explaining their rationale, leaving their decision-making process inscrutable. This opacity hinders developers' ability to critically evaluate the output, form accurate mental models, and build calibrated trust in the system. To address this, we introduce CopilotLens, a novel interactive framework that reframes code co…

AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
Arya VarastehNezhad, Reza Tavasoli, Soroush Elyasi, MohammadHossein LotfiNia, Hamed Farbeh
https://arxiv.org/abs/2508.11285

AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries
Depression, anxiety, and stress are widespread mental health concerns that increasingly drive individuals to seek information from Large Language Models (LLMs). This study investigates how eight LLMs (Claude Sonnet, Copilot, Gemini Pro, GPT-4o, GPT-4o mini, Llama, Mixtral, and Perplexity) reply to twenty pragmatic questions about depression, anxiety, and stress when those questions are framed for six user profiles (baseline, woman, man, young, old, and university student). The models generated …

the sheer amount of prominent old hollywood actresses and models and shit who just got diagnosed with schizophenia without a care as to everything leading up to their mental health collapse

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, Alexandre R. Franco, Michael P. Milham, Aviv Dotan, Scott Makeig, Alexandre Gramfort, Jean-Remi King, Marie-Constance Corsi, Pedro A. Vald\'es-Sosa, Amit Majumdar, Alan Evans, Terrence J Sejnowski, Oren Shriki, Sylvain Chevallier, Arnaud Delorme

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG da…

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery https://arxiv.org/abs/2506.06898

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a chal…

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Weiheng Lu, Chunfeng Song, Jiamin Wu, Pengyu Zhu, Yuchen Zhou, Weijian Mai, Qihao Zheng, Wanli Ouyang
https://arxiv.org/abs/2506.18962

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse applications in mental state assessment, clinical monitoring, and human-machine interaction. Recent efforts have extensively explored EEG-based brain foundation models for generalized brain decoding, employing large-scale training on multiple datasets. However, most of these attempts struggle with generalizability and fa…

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Khalid Hasan, Jamil Saquer
https://arxiv.org/abs/2507.14231

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text. We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embe…

GenIR: Generative Visual Feedback for Mental Image Retrieval
Diji Yang, Minghao Liu, Chung-Hsiang Lo, Yi Zhang, James Davis
https://arxiv.org/abs/2506.06220

GenIR: Generative Visual Feedback for Mental Image Retrieval
Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind, that is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which…

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Manish Bhatt
https://arxiv.org/abs/2506.11423 https://

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Debates about whether Large Language or Reasoning Models (LLMs/LRMs) truly reason or merely pattern-match suffer from shifting goal posts. In my personal opinion, two analytic--hence "tautological"--benchmarks cut through that fog in my mental model. In this paper, I attempt to write down my mental model in concrete terms.

Political alignment correlates with self-reported mental health. I hate this.
I wonder how much of the correlation is actual mental health as opposed to willingness to admit when it’s bad.
(source: https://www.natesilver.net/p/sbsq-21-why-young-men-do…

SBSQ #21: Why young men don't like Democrats
Plus, the New York City mayor’s race — and whether win probability models are broken.

How attention simplifies mental representations for planning
Jason da Silva Castanheira, Nicholas Shea, Stephen M. Fleming
https://arxiv.org/abs/2506.09520

How attention simplifies mental representations for planning
Human planning is efficient -- it frugally deploys limited cognitive resources to accomplish difficult tasks -- and flexible -- adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning -- but the perceptual and attentional …

On the capabilities of LLMs for classifying and segmenting time series of fruit picking motions into primitive actions
Eleni Konstantinidou, Nikolaos Kounalakis, Nikolaos Efstathopoulos, Dimitrios Papageorgiou
https://arxiv.org/abs/2507.07745

On the capabilities of LLMs for classifying and segmenting time series of fruit picking motions into primitive actions
Despite their recent introduction to human society, Large Language Models (LLMs) have significantly affected the way we tackle mental challenges in our everyday lives. From optimizing our linguistic communication to assisting us in making important decisions, LLMs, such as ChatGPT, are notably reducing our cognitive load by gradually taking on an increasing share of our mental activities. In the context of Learning by Demonstration (LbD), classifying and segmenting complex motions into primitiv…

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong, Katherine M. Collins, Lance Ying, Cedegao E. Zhang, Adrian Weller, Tobias Gersternberg, Timothy O'Donnell, Alexander K. Lew, Jacob D. Andreas, Joshua B. Tenenbaum, Tyler Brooke-Wilson
https://arxiv.org/abs/2507.12547

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. What permits us to draw in globally relevant information and reason over it coherently? Here, we explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations. We propose a computational implementation of this idea -- a `…

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang, Violeta J. Rodriguez, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.15047

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot …

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…

EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition
Hugo Thimonier, Antony Perzo, Renaud Seguier
https://arxiv.org/abs/2508.14130 https://

EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition
Emotion recognition from speech is a challenging task that requires capturing both linguistic and paralinguistic cues, with critical applications in human-computer interaction and mental health monitoring. Recent works have highlighted the ability of Large Language Models (LLMs) to perform tasks outside of the sole natural language area. In particular, recent approaches have investigated coupling LLMs with other data modalities by using pre-trained backbones and different fusion mechanisms. Thi…

Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis
Trong-Thang Pham, Anh Nguyen, Zhigang Deng, Carol C. Wu, Hien Van Nguyen, Ngan Le
https://arxiv.org/abs/2507.12461

Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis
Radiologists rely on eye movements to navigate and interpret medical images. A trained radiologist possesses knowledge about the potential diseases that may be present in the images and, when searching, follows a mental checklist to locate them using their gaze. This is a key observation, yet existing models fail to capture the underlying intent behind each fixation. In this paper, we introduce a deep learning-based approach, RadGazeIntent, designed to model this behavior: having an intention t…

The Problem of Atypicality in LLM-Powered Psychiatry
Bosco Garcia, Eugene Y. S. Chua, Harman Singh Brah
https://arxiv.org/abs/2508.06479 https://arxiv.org/…

The Problem of Atypicality in LLM-Powered Psychiatry
Large language models (LLMs) are increasingly proposed as scalable solutions to the global mental health crisis. But their deployment in psychiatric contexts raises a distinctive ethical concern: the problem of atypicality. Because LLMs generate outputs based on population-level statistical regularities, their responses -- while typically appropriate for general users -- may be dangerously inappropriate when interpreted by psychiatric patients, who often exhibit atypical cognitive or interpreti…

Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
Chadha Degachi, Samuel Kernan Freire, Evangelos Niforatos, Gerd Kortuem
https://arxiv.org/abs/2506.03807

Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
The experience and adoption of conversational search is tied to the accuracy and completeness of users' mental models -- their internal frameworks for understanding and predicting system behaviour. Thus, understanding these models can reveal areas for design interventions. Transparency is one such intervention which can improve system interpretability and enable mental model alignment. While past research has explored mental models of search engines, those of generative conversational search re…

From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning
Yichao Feng
https://arxiv.org/abs/2508.02458 https://…

From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning
Large Language Models show promise in emotion understanding, social reasoning, and empathy, yet they struggle with psychologically grounded tasks that require inferring implicit mental states in context-rich, ambiguous settings. These limitations arise from the absence of theory-aligned supervision and the difficulty of capturing nuanced mental processes in real-world narratives. To address this gap, we leverage expert-labeled, psychologically rich scenarios and propose a trajectory-aware reinf…

How popular media gets love wrong
Okay, my attempt at (hopefully widely-applicable) advice about relationships based on my mental "engineering" model and how it differs from the popular "fire" and "appeal" models:
1. If you're looking for a partner, don't focus too much on external qualities, but instead ask: "Do they respect me?" "Are they interested in active consent in all aspects of our relationship?" "Are they willing to commit a little now, and open to respectfully negotiating deeper commitment?" "Are they trustworthy, and willing to trust me?" Finding your partner attractive can come *from* trusting/appreciating/respecting them, rather than vice versa.
2. If you're looking for a partner, don't wait for infatuation to start before you try building a relationship. Don't wait to "fall in love;" if you "fall" into love you could just as easily "fall" out, but if you build up love, it won't be so easy to destroy. If you're feeling lonely and want a relationship, pick someone who seems interesting and receptive in your social circles and ask if they'd like to do something with you (doesn't have to be a date at first). *Pursue active consent* at each stage (if they're not interested; ask someone else, this will be easier if you're not already infatuated). If they're judging you by the standards in point 1, this is doubly important.
3. When building a relationship, try to synchronize your levels of commitment & trust even as you're trying to deepen them, or at least try to be honest and accepting when they need to be out-of-step. Say things and do things that show your partner the things (like trust, commitment, affection, etc.) that are important in your relationship, and ask them to do the same (or ideally you don't have to ask if they're conscious of this too). Do these things not as a chore or a transaction when your partner does them, but because they're the work of building the relationship that you value for its own sake (and because you value your partner for themselves too).
4. When facing big external challenges to your commitment to a relationship, like a move, ensure that your partner has an appropriate level of commitment too, but then don't undervalue the relationship relative to other things in life. Everyone is different, but *to me*, my committed relationship has been far more rewarding than e.g., a more "successful" career would have been. Of course worth noting here that non-men are taught by our society to undervalue their careers & other aspects of their life and sacrifice everything for their partners, which is toxic. I'm not saying "don't value other things" but especially for men, *do* value romantic relationships and be prepared to make decisions that prioritize them over other things, assuming a partner who is comfortable with that commitment and willing to reciprocate.
Okay, this thread is complete for now, until I think of something else that I've missed. I hope this advice is helpful in some way (or at least not harmful). Feel free to chime in if you've got different ideas...
#relationships #love

Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Yifan Gao, Jiao Fu, Long Guo, Hong Liu
https://arxiv.org/abs/2507.00693

Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we present the results of our work in the 1st SpeechWellness Challenge (SW1), which aims to explore speech as a non-invasive and easily accessible mental health indicator for identifying adolescents at risk of suicide.Our approach leverages large language model (LLM…

Excessive Screen Time is Associated with Mental Health Problems and ADHD in US Children and Adolescents: Physical Activity and Sleep as Parallel Mediators
Ying Dai, Na Ouyang
https://arxiv.org/abs/2508.10062

Excessive Screen Time is Associated with Mental Health Problems and ADHD in US Children and Adolescents: Physical Activity and Sleep as Parallel Mediators
To examine associations between screen time and anxiety, depression, behavior or conduct problems, and ADHD among children and adolescents during the pandemic, and to assess whether physical activity, sleep duration, and bedtime regularity mediate these associations. Data from 50231 US children and adolescents aged 6 to 17 years in the 2020 to 2021 National Survey of Childrens Health were analyzed. Exact natural effect models and structural equation modeling assessed mediation by physical activ…

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Xun Yang, Meng Wang
https://arxiv.org/abs/2508.10848 …

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in mathematics and programming, while research in the psychological domain has predominantly emphasized emotional support and empathetic dialogue, with limited attention to reasoning mechanisms that are bene…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
Bujar Raufi
https://arxiv.org/abs/2508.07283 https://arxiv.o…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction pa…

Visualizing Cloud-native Applications with KubeDiagrams
Philippe Merle, Fabio Petrillo
https://arxiv.org/abs/2505.22879 https://arxiv…

Visualizing Cloud-native Applications with KubeDiagrams
Modern distributed applications increasingly rely on cloud-native platforms to abstract the complexity of deployment and scalability. As the de facto orchestration standard, Kubernetes enables this abstraction, but its declarative configuration model makes the architectural understanding difficult. Developers, operators, and architects struggle to form accurate mental models from raw manifests, Helm charts, or cluster state descriptions. We introduce KubeDiagrams, an open-source tool that trans…

Human vs. LLM-Based Thematic Analysis for Digital Mental Health Research: Proof-of-Concept Comparative Study
Karisa Parkington, Bazen G. Teferra, Marianne Rouleau-Tang, Argyrios Perivolaris, Alice Rueda, Adam Dubrowski, Bill Kapralos, Reza Samavi, Andrew Greenshaw, Yanbo Zhang, Bo Cao, Yuqi Wu, Sirisha Rambhatla, Sridhar Krishnan, Venkat Bhat
https:/…

Human vs. LLM-Based Thematic Analysis for Digital Mental Health Research: Proof-of-Concept Comparative Study
Thematic analysis provides valuable insights into participants' experiences through coding and theme development, but its resource-intensive nature limits its use in large healthcare studies. Large language models (LLMs) can analyze text at scale and identify key content automatically, potentially addressing these challenges. However, their application in mental health interviews needs comparison with traditional human analysis. This study evaluates out-of-the-box and knowledge-base LLM-based t…

A Quantum-Inspired Conceptual Model of Collective Subjective Evaluation via Bloch Sphere Dynamics and Like-Polarization
Bumned Soodchomshom
https://arxiv.org/abs/2506.01847

A Quantum-Inspired Conceptual Model of Collective Subjective Evaluation via Bloch Sphere Dynamics and Like-Polarization
We propose a conceptual quantum-inspired model to describe subjective evaluation processes such as taste testing or consumer preference scoring-through the continuous time evolution of cognitive states represented on the Bloch sphere. Unlike conventional quantum cognition models that rely on projective measurements and state collapse, our framework treats evaluation as a weak or generalized measurement, preserving quantum coherence during the act of judgment. The mental state of preference is m…

This https://arxiv.org/abs/2505.17663 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States
As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present \textsc{DynToM}, a novel benchmark specifically designed to evaluate LLM…

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation
Adam Kostka, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2507.21969

Towards Cognitive Synergy in LLM-Based Multi-Agent Systems: Integrating Theory of Mind and Critical Evaluation
Recently, the field of Multi-Agent Systems (MAS) has gained popularity as researchers are trying to develop artificial intelligence capable of efficient collective reasoning. Agents based on Large Language Models (LLMs) perform well in isolated tasks, yet struggle with higher-order cognition required for adaptive collaboration. Human teams achieve synergy not only through knowledge sharing, but also through recursive reasoning, structured critique, and the ability to infer others' mental states…

How popular media gets love wrong
Okay, so what exactly are the details of the "engineered" model of love from my previous post? I'll try to summarize my thoughts and the experiences they're built on.
1. "Love" can be be thought of like a mechanism that's built by two (or more) people. In this case, no single person can build the thing alone, to work it needs contributions from multiple people (I suppose self-love might be an exception to that). In any case, the builders can intentionally choose how they build (and maintain) the mechanism, they can build it differently to suit their particular needs/wants, and they will need to maintain and repair it over time to keep it running. It may need winding, or fuel, or charging plus oil changes and bolt-tightening, etc.
2. Any two (or more) people can choose to start building love between them at any time. No need to "find your soulmate" or "wait for the right person." Now the caveat is that the mechanism is difficult to build and requires lots of cooperation, so there might indeed be "wrong people" to try to build love with. People in general might experience more failures than successes. The key component is slowly-escalating shared commitment to the project, which is negotiated between the partners so that neither one feels like they've been left to do all the work themselves. Since it's a big scary project though, it's very easy to decide it's too hard and give up, and so the builders need to encourage each other and pace themselves. The project can only succeed if there's mutual commitment, and that will certainly require compromise (sometimes even sacrifice, though not always). If the mechanism works well, the benefits (companionship; encouragement; praise; loving sex; hugs; etc.) will be well worth the compromises you make to build it, but this isn't always the case.
3. The mechanism is prone to falling apart if not maintained. In my view, the "fire" and "appeal" models of love don't adequately convey the need for this maintenance and lead to a lot of under-maintained relationships many of which fall apart. You'll need to do things together that make you happy, do things that make your partner happy (in some cases even if they annoy you, but never in a transactional or box-checking way), spend time with shared attention, spend time alone and/or apart, reassure each other through words (or deeds) of mutual beliefs (especially your continued commitment to the relationship), do things that comfort and/or excite each other physically (anywhere from hugs to hand-holding to sex) and probably other things I'm not thinking of. Not *every* relationship needs *all* of these maintenance techniques, but I think most will need most. Note especially that patriarchy teaches men that they don't need to bother with any of this, which harms primarily their romantic partners but secondarily them as their relationships fail due to their own (cultivated-by-patriarchy) incompetence. If a relationship evolves to a point where one person is doing all the maintenance (& improvement) work, it's been bent into a shape that no longer really qualifies as "love" in my book, and that's super unhealthy.
4. The key things to negotiate when trying to build a new love are first, how to work together in the first place, and how to be comfortable around each others' habits (or how to change those habits). Second, what level of commitment you have right now, and what how/when you want to increase that commitment. Additionally, I think it's worth checking in about what you're each putting into and getting out of the relationship, to ensure that it continues to be positive for all participants. To build a successful relationship, you need to be able to incrementally increase the level of commitment to one that you're both comfortable staying at long-term, while ensuring that for both partners, the relationship is both a net benefit and has manageable costs (those two things are not the same). Obviously it's not easy to actually have conversations about these things (congratulations if you can just talk about this stuff) because there's a huge fear of hearing an answer that you don't want to hear. I think the range of discouraging answers which actually spell doom for a relationship is smaller than people think and there's usually a reasonable "shoulder" you can fall into where things aren't on a good trajectory but could be brought back into one, but even so these conversations are scary. Still, I think only having honest conversations about these things when you're angry at each other is not a good plan. You can also try to communicate some of these things via non-conversational means, if that feels safer, and at least being aware that these are the objectives you're pursuing is probably helpful.
I'll post two more replies here about my own experiences that led me to this mental model and trying to distill this into advice, although it will take me a moment to get to those.
#relationships #love

Bayesian Regression Analysis with the Drift-Diffusion Model
Zekai Jin (Mental Health Data Science, New York State Psychiatric Institute, New York, USA), Yaakov Stern (Departments of Neurology, Columbia University Irving Medical Center, New York, USA), Seonjoo Lee (Mental Health Data Science, New York State Psychiatric Institute, New York, USA, Department of Biostatistics, Columbia University Irving Medical Center, New York, USA)

Bayesian Regression Analysis with the Drift-Diffusion Model
The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated v…

Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Shuning Zhang, Rongjun Ma, Ying Ma, Shixuan Li, Yiqun Xu, Xin Yi, Hewu Li
https://arxiv.org/abs/2508.07664

Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Large Language Models (LLMs) are increasingly integrating memory functionalities to provide personalized and context-aware interactions. However, user understanding, practices and expectations regarding these memory systems are not yet well understood. This paper presents a thematic analysis of semi-structured interviews with 18 users to explore their mental models of LLM's Retrieval Augmented Generation (RAG)-based memory, current usage practices, perceived benefits and drawbacks, privacy conc…

Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
Khalid Hasan, Jamil Saquer, Mukulika Ghosh
https://arxiv.org/abs/2507.19511

Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against Long Short-Term Memory (LSTM) based approaches using diff…

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
T. T. J. E. Arets, G. Perugia, M. Houben, W. A. IJsselsteijn
https://arxiv.org/abs/2506.10927

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to…

Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs
Shintaro Sakai, Jisun An, Migyeong Kang, Haewoon Kwak
https://arxiv.org/abs/2508.03247

Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs
Prior clinical psychology research shows that Western individuals with depression tend to report psychological symptoms, while Eastern individuals report somatic ones. We test whether Large Language Models (LLMs), which are increasingly used in mental health, reproduce these cultural patterns by prompting them with Western or Eastern personas. Results show that LLMs largely fail to replicate the patterns when prompted in English, though prompting in major Eastern languages (i.e., Chinese, Japan…

This https://arxiv.org/abs/2503.16456 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care
This position paper argues for a fundamental shift in how Large Language Models (LLMs) are integrated into the mental health care domain. We advocate for their role as co-creators rather than mere assistive tools. While LLMs have the potential to enhance accessibility, personalization, and crisis intervention, their adoption remains limited due to concerns about bias, evaluation, over-reliance, dehumanization, and regulatory uncertainties. To address these challenges, we propose two structured …

"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei Choo
https://arxiv.org/abs/2506.09354

"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. Peer support, grounded in lived experience, offers a valuable complement to professional care. However, variability in training, effectiveness, and definitions raises concerns about quality, consistency, and safety. Large Language Models (LLMs) present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions. We present …

The Thin Line Between Comprehension and Persuasion in LLMs
Adrian de Wynter, Tangming Yuan
https://arxiv.org/abs/2507.01936 https://a…

The Thin Line Between Comprehension and Persuasion in LLMs
Large language models (LLMs) are excellent at maintaining high-level, convincing dialogues. They are being fast deployed as chatbots and evaluators in sensitive areas, such as peer review and mental health applications. This, along with the disparate accounts on their reasoning capabilities, calls for a closer examination of LLMs and their comprehension of dialogue. In this work we begin by evaluating LLMs' ability to maintain a debate--one of the purest yet most complex forms of human communic…

From Reality to Recognition: Evaluating Visualization Analogies for Novice Chart Comprehension
Oliver Huang, Patrick Lee, Carolina Nobre
https://arxiv.org/abs/2506.03385

From Reality to Recognition: Evaluating Visualization Analogies for Novice Chart Comprehension
Novice learners often have difficulty learning new visualization types because they tend to interpret novel visualizations through the mental models of simpler charts they have previously encountered. Traditional visualization teaching methods, which usually rely on directly translating conceptual aspects of data into concrete data visualizations, often fail to attend to the needs of novice learners navigating this tension. To address this, we conducted an empirical exploration of how analogies…

ProMemAssist: Exploring Timely Proactive Assistance Through Working Memory Modeling in Multi-Modal Wearable Devices
Kevin Pu, Ting Zhang, Naveen Sendhilnathan, Sebastian Freitag, Raj Sodhi, Tanya Jonker
https://arxiv.org/abs/2507.21378

ProMemAssist: Exploring Timely Proactive Assistance Through Working Memory Modeling in Multi-Modal Wearable Devices
Wearable AI systems aim to provide timely assistance in daily life, but existing approaches often rely on user initiation or predefined task knowledge, neglecting users' current mental states. We introduce ProMemAssist, a smart glasses system that models a user's working memory (WM) in real-time using multi-modal sensor signals. Grounded in cognitive theories of WM, our system represents perceived information as memory items and episodes with encoding mechanisms, such as displacement and interf…

Using Tactile Charts to Support Comprehension and Learning of Complex Visualizations for Blind and Low-Vision Individuals
Tingying He, Maggie McCracken, Daniel Hajas, Sarah Creem-Regehr, Alexander Lex
https://arxiv.org/abs/2507.21462

Using Tactile Charts to Support Comprehension and Learning of Complex Visualizations for Blind and Low-Vision Individuals
We investigate whether tactile charts support comprehension and learning of complex visualizations for blind and low-vision (BLV) individuals and contribute four tactile chart designs and an interview study. Visualizations are powerful tools for conveying data, yet BLV individuals typically can rely only on assistive technologies -- primarily alternative texts -- to access this information. Prior research shows the importance of mental models of chart types for interpreting these descriptions, …