Tootfinder

This https://arxiv.org/abs/2502.17701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation dec…

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Gabriele Digregorio, Salvatore Maccarrone, Mario D'Onghia, Luigi Gallo, Michele Carminati, Mario Polino, Stefano Zanero
https://arxiv.org/abs/2506.02660

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an effective solution in both the feature and problem spaces. This issue arises from not addressing the intrinsic nondeterministic nature of malware, namely executing the same sample multiple times may yield significantly different behaviors. Hence, the perturbations computed for a specific behavior may be ineffective for…

This https://arxiv.org/abs/2505.02945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The Cognitive Foundations of Economic Exchange: A Modular Framework Grounded in Behavioral Evidence
A key challenge in multi-agent AI is modeling social cooperation under realistic behavioral constraints. Many foundational concepts in economics and ethics such as "trust" or "morality" are often defined informally, without operational criteria or cognitive grounding, which limits their testability and implementation in artificial agents. Drawing on converging empirical evidence from primate behavior, infant cognition, and economic anthropology, we propose a conceptual framework composed of thr…

Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments
Yuan Bian, Xingche Guo, Yuanjia Wang
https://arxiv.org/abs/2506.02394 https:…

Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments
Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration issues. Motivated by the probabilistic reward task from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel framework that integrates the reinforcement learning (RL) model and drift-diffusion model (DDM) to jointly analyze reward-based decision-making with response times. To acc…

This https://arxiv.org/abs/2505.22086 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
High-Level Synthesis (HLS) serves as an agile hardware development tool that streamlines the circuit design by abstracting the register transfer level into behavioral descriptions, while allowing designers to customize the generated microarchitectures through optimization directives. However, the combinatorial explosion of possible directive configurations yields an intractable design space. Traditional design space exploration (DSE) methods, despite adopting heuristics or constructing predicti…

This https://arxiv.org/abs/2504.18073 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Modular integration of neural connectomics, dynamics and biomechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans
Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting model variations. Here we develop modWorm: a modular modeling framework for the nematode C. elegans. modWorm allows for construction of a model as an integrated series of configurabl…

Behavioral alignment in social networks
Yu Xia, Alex McAvoy, Qi Su
https://arxiv.org/abs/2506.00046 https://arxiv.org/pdf/2506.00046

Behavioral alignment in social networks
The orderly behaviors observed in large-scale groups, such as fish schooling and the organized movement of crowds, are both ubiquitous and essential for the survival and stability of these systems. Such complex collective behaviors often emerge from simple local interactions and strategy adjustments among individuals. Understanding how these basic rules shape complex group dynamics has long been a significant scientific challenge. Historically, research has predominantly focused on imitation an…

This https://arxiv.org/abs/2505.16214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional functional safety approaches, which primarily verify the reliability, robustness, and adequacy of AV hardware and software systems from a vehicle-centric perspective, do not sufficiently address the AV's broader interactions and behavioral impact on the surrounding traffic environment. To overcome this limitation, we pr…

This https://arxiv.org/abs/2309.15174 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

A stochastic explanation for observed local-to-global foraging states in Caenorhabditis elegans
Abrupt changes in behavior can often be associated with changes in underlying behavioral states. When placed off food, the foraging behavior of C. elegans can be described as a change between an initial local-search behavior characterized by a high rate of reorientations, followed by a global-search behavior characterized by sparse reorientations. This is commonly observed in individual worms, but when numerous worms are characterized, only about half appear to exhibit this behavior. We propose…

Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Meng Xu, Frank Neumann, Aneta Neumann, Yew Soon Ong
https://arxiv.org/abs/2507.02235

Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Real-world optimization often demands diverse, high-quality solutions. Quality-Diversity (QD) optimization is a multifaceted approach in evolutionary algorithms that aims to generate a set of solutions that are both high-performing and diverse. QD algorithms have been successfully applied across various domains, providing robust solutions by exploring diverse behavioral niches. However, their application has primarily focused on static problems, with limited exploration in the context of dynami…

This https://arxiv.org/abs/2502.18744 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction
Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence (AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose ZEBRA - a model behavior-wise zero-annotation framework that constructs preference data by leveraging model behavior knowledge derived from benchmark performances. ZEBRA binarizes response pairs by …

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
Brady D. Lund, Ting Wang
https://arxiv.org/abs/2506.03165

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science resea…

This https://arxiv.org/abs/2502.14879 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Limited attention and models of choice: A behavioral equivalence
We show that many models of choice can be alternatively represented as special cases of choice with limited attention (Masatlioglu, Nakajima, and Ozbay, 2012), singling out the properties of the unobserved attention filters that explain the observed choices.For each specification, information about the DM's consideration sets and preference is inferred from violations of the contraction consistency axiom, and it is compared with the welfare indications obtained from equivalent models. Remarkabl…

Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation
Djaber Rouabhia, Ismail Hadjadj
https://arxiv.org/abs/2506.00788

This https://arxiv.org/abs/2404.18445 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

AI and the Dynamic Supply of Training Data
Artificial intelligence (AI) systems rely heavily on human-generated data, yet the people behind that data are often overlooked. Human behavior can play a major role in AI training datasets, be it in limiting access to existing works or in deciding which types of new works to create or whether to create any at all. We examine creators' behavioral change when their works become training data for commercial AI. Specifically, we focus on contributors on Unsplash, a popular stock image platform wit…

AI Meets Maritime Training: Precision Analytics for Enhanced Safety and Performance
Vishakha Lall, Yisi Liu
https://arxiv.org/abs/2507.01274 https://

AI Meets Maritime Training: Precision Analytics for Enhanced Safety and Performance
Traditional simulator-based training for maritime professionals is critical for ensuring safety at sea but often depends on subjective trainer assessments of technical skills, behavioral focus, communication, and body language, posing challenges such as subjectivity, difficulty in measuring key features, and cognitive limitations. Addressing these issues, this study develops an AI-driven framework to enhance maritime training by objectively assessing trainee performance through visual focus tra…

This https://arxiv.org/abs/2502.09716 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Principles and Policy Recommendations for Comprehensive Genetic Data Governance
Genetic data collection has become ubiquitous, producing genetic information about health, ancestry, and social traits. However, unregulated use, especially amid evolving scientific understanding, poses serious privacy and discrimination risks. These risks are intensified by advancing AI, particularly multi-modal systems integrating genetic, clinical, behavioral, and environmental data. In this work, we organize the uses of genetic data along four distinct "pillars", and develop a risk assessme…

"the operations at the two new Laguna Street sites will be contracted out which means they will no longer be eligible to work with their clients."
Somehow, I get the feeling that the great minds behind this project fail to understand the importance of community and trust.

S.F. plans to move mentally ill adults draws protest
If the city has its way, all 83 residents will be moved to two facilities on Laguna Street in Hayes Valley.

"the operations at the two new Laguna Street sites will be contracted out which means they will no longer be eligible to work with their clients."
Somehow, I get the feeling that the great minds behind this project fail to understand the importance of community and trust.

S.F. plans to move mentally ill adults draws protest
If the city has its way, all 83 residents will be moved to two facilities on Laguna Street in Hayes Valley.

Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
Amogh Mannekote, Adam Davies, Guohao Li, Kristy Elizabeth Boyer, ChengXiang Zhai, Bonnie J Dorr, Francesco Pinto
https://arxiv.org/abs/2507.02197

Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
As LLMs are increasingly studied as role-playing agents to generate synthetic data for human behavioral research, ensuring that their outputs remain coherent with their assigned roles has become a critical concern. In this paper, we investigate how consistently LLM-based role-playing agents' stated beliefs about the behavior of the people they are asked to role-play ("what they say") correspond to their actual behavior during role-play ("how they act"). Specifically, we establish an evaluation …

What Neuroscience Can Teach AI About Learning in Continuously Changing Environments
Daniel Durstewitz, Bruno Averbeck, Georgia Koppe
https://arxiv.org/abs/2507.02103

What Neuroscience Can Teach AI About Learning in Continuously Changing Environments
Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed parameters. Their training is costly, slow, and gradual, requiring billions of repetitions. In stark contrast, animals continuously adapt to the ever-changing contingencies in their environments. This is particularly important for social species, where behavioral policies and reward outcomes may frequently change in interact…

Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks
Riccardo Basilone, Eleonora Bergamin, Federica Fanelli, Egor Kotov, Kevin Morelle, Alisa Klamm, Manal Nhili, Joshua Rosen, Andrew Schendl, Olena Holubowska, Andrew Renninger, Kamil Smolak
https://arxiv.org/abs/2506.22138

Preliminary analysis of Sus scrofa movement using Hidden Markov Models and Networks
This study examines the complex movement patterns and behavioral characteristics of wild boars using GPS telemetry data collected over a two-month period. Our methodological approach centers on the application of a Hidden Markov Model (HMM) to discern distinct behavioral states embedded within the trajectories. Furthermore, the study aimed to construct behavioral networks, derived from these segmented trajectories. The resultant network structures showed that the hidden behavioral patterns are …

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
Y. Du
https://arxiv.org/abs/2506.22496

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
Large Language Models (LLMs) exhibit systematic risk-taking behaviors analogous to those observed in gambling psychology, including overconfidence bias, loss-chasing tendencies, and probability misjudgment. Drawing from behavioral economics and prospect theory, we identify and formalize these "gambling-like" patterns where models sacrifice accuracy for high-reward outputs, exhibit escalating risk-taking after errors, and systematically miscalibrate uncertainty. We propose the Risk-Aware Respons…

This is incredibly exciting!
Is It All a Fluke? Lessons From Playing God in the Long-Term Evolution Experiment - by Brian Klaas - Behavioral Scientist https://behavioralscientist.org/is-it-all-a-fluke-lessons-from-playing-god-…

Is It All a Fluke? Lessons From Playing God in the Long-Term Evolution Experiment - by Brian Klaas - Behavioral Scientist
It’s easy to believe that small, chance moments don’t make a material difference on our lives and societies. But a long-running evolution experiment suggests that life’s course may come down to the random details.

Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric
Mattson Ogg, Caitlyn Bishop, Han Yi, Sarah Robinson
https://arxiv.org/abs/2506.01655

Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric
Methods for automatically assessing speech quality are critical for many human language technologies. Behavioral ratings provided by human raters (e.g., mean opinion scores; MOS) are considered the gold standard, but they are susceptible to variability between individual raters, cannot easily be generalized across corpora, and are labor-intensive to collect, thus limiting the acoustic challenges they can quantify. Here, we present a new, scalable method for automatically assessing speech qualit…

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
Yifan Wang, Weinan Gan, Longtao Xiao, Jieming Zhu, Heng Chang, Haozhao Wang, Rui Zhang, Zhenhua Dong, Ruiming Tang, Ruixuan Li
https://arxiv.org/abs/2506.23643

Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
Generative recommendation (GR) typically encodes behavioral or semantic aspects of item information into discrete tokens, leveraging the standard autoregressive (AR) generation paradigm to make predictions. However, existing methods tend to overlook their intrinsic relationship, that is, the semantic usually provides some reasonable explainability "$\textbf{why}$" for the behavior "$\textbf{what}$", which may constrain the full potential of GR. To this end, we present Chunk AutoRegressive Model…

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction
Liliang Ye (Huazhong University of Science and Technology, Wuhan, China), Yunyao Zhang (Huazhong University of Science and Technology, Wuhan, China), Yafeng Wu (Huazhong University of Science and Technology, Wuhan, China), Yi-Ping Phoebe Chen (La Trobe University, Melbourne, Australia), Junqing Yu (Huazhong University of Science and Technology, Wuhan, China), Wei Yang (Huazhong University of S…

HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction
Social media popularity prediction plays a crucial role in content optimization, marketing strategies, and user engagement enhancement across digital platforms. However, predicting post popularity remains challenging due to the complex interplay between visual, textual, temporal, and user behavioral factors. This paper presents HyperFusion, a hierarchical multimodal ensemble learning framework for social media popularity prediction. Our approach employs a three-tier fusion architecture that pro…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

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…

„While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, #LLM users consistently underperformed at neural, linguistic, and behavioral levels.“ #AI

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Ziyi Chen, Yiyang Liu, Mattia Prosperi, Krishna Vaddiparti, Robert L Cook, Jiang Bian, Yi Guo, Yonghui Wu
https://arxiv.org/abs/2506.09279

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States. Methods: We identified 9,140 cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma dimen…

Reduced Efficiency in the Right Fronto-Parietal Attentional Network During Distractor Suppression in Mild Cognitive Impairment
Jatupong Oboun, Piyanon Charoenpoonpanich, Anna Raksapatcharawong, Chaipat Chunharas, Itthi Chatnuntawech, Chainarong Amornbunchornvej, Sirawaj Itthipuripat
https://arxiv.org/abs/2507.01433

Reduced Efficiency in the Right Fronto-Parietal Attentional Network During Distractor Suppression in Mild Cognitive Impairment
Mild Cognitive Impairment (MCI) is a critical transitional stage between normal cognitive aging and dementia, making its early detection essential. This study investigates the neural mechanisms of distractor suppression in MCI patients using EEG and behavioral data during an attention-cueing Eriksen flanker task. A cohort of 56 MCIs and 26 healthy controls (HCs) performed tasks with congruent and incongruent stimuli of varying saliency levels. During these tasks, EEG data were analyzed for alph…

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things
Kai Li, Conggai Li, Xin Yuan, Shenghong Li, Sai Zou, Syed Sohail Ahmed, Wei Ni, Dusit Niyato, Abbas Jamalipour, Falko Dressler, Ozgur B. Akan
https://arxiv.org/abs/2505.23792

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things
This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversari…

How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students
Tawfiq Ammari, Meilun Chen, S M Mehedi Zaman, Kiran Garimella
https://arxiv.org/abs/2505.24126

How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students
This study investigates how undergraduate students engage with ChatGPT in self directed learning contexts. Analyzing naturalistic interaction logs, we identify five dominant use categories of ChatGPT information seeking, content generation, language refinement, meta cognitive engagement, and conversational repair. Behavioral modeling reveals that structured, goal driven tasks like coding, multiple choice solving, and job application writing are strong predictors of continued use. Drawing on Sel…

Om Malik writes: “I have been talking about this for a very long time, but the establishment media is always the last to realize their own existential threats. Just as they were slow to recognize the emergence of blogs, social media, and how Facebook was a chimera, they have been slow to realize that the old “destination internet” as a behavioral construct is over.”
In my opinion, these threats are ignored or kicked down the road due to management pressure (“we can’t invest into ...”) combined with peer pressure (“… while everyone else is doing X”).
The result is usually:
(image found on http://thethoughtsoflouise.blogspot.com/2016/07/be-example-not-warning.html)

Adaptive network dynamics and behavioral contagion in multi-state drug use propagation
Hsuan-Wei Lee, Yi-Hsuan Huang, Nishant Malik
https://arxiv.org/abs/2506.21766

Adaptive network dynamics and behavioral contagion in multi-state drug use propagation
Addictive behavior spreads through social networks via feedback among choice, peer pressure, and shifting ties, a process that eludes standard epidemic models. We present a comprehensive multi-state network model that integrates utility-based behavioral transitions with adaptive network rewiring, capturing the co-evolutionary dynamics between drug use patterns and social structure. Our framework distinguishes four distinct individual states by combining drug use behavior with addiction status, …

Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study
Laura M\"uller, Piklu Mallick, Antonio B. Mar\'in-Carballo, Philipp D\"onges, Robyn J. N. Kettlitz, Carolina J. Klett-Tammen, Mirjam Kretzschmar, Viola Priesemann, Seba Contreras
https://arxiv.org/abs…

Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study
HIV pre-exposure Prophylaxis (PrEP) has become essential for global HIV control, but its implementation coincides with rising bacterial STI rates among men who have sex with men (MSM). While risk-compensation behavioral changes like reduced condom use are frequently reported, we examine whether intensified asymptomatic screening in PrEP programs creates surveillance artifacts that could be misinterpreted. We developed a compartmental model to represent the simultaneous spread of HIV and chlamyd…

Spending Behavior and Economic Impacts of Urban Digital Consumption Vouchers
Ming-Huan Liou, Shou-Yung Yin, Hsiang-Wen Mao
https://arxiv.org/abs/2506.01385

Spending Behavior and Economic Impacts of Urban Digital Consumption Vouchers
This paper evaluates the Taipei Bear Vouchers 2.0 program using verified user-level survey data and a regional input-output model to assess the effectiveness of consumption vouchers as a fiscal stimulus tool. We focus on three key behavioral mechanisms: expenditure substitution, induced consumption, and the intensity of treatment through varying voucher face values. Our findings show that voucher effectiveness differs by type. Accommodation vouchers stimulate the most additional spending due to…

New study on the effects of LLM use (in this case on essay writing):
https://arxiv.org/abs/2506.08872
Quote:
"LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four month…

Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalo…

Wissenschaftler:innen haben herausgefunden: Wer ChatGPT oder andere Bullshit-Generatoren nutzt, verblödet innerhalb kurzer Zeit.
#LLM

❝Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.❞
Hell of a research abstract there, via @…: https://fediscience.org/@gwagner/114690366530883451

Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation
Daniel Ciganda, Ignacio Camp\'on, I\~naki Permanyer, Jakob H Macke
https://arxiv.org/abs/2506.22607

Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation
While age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, their aggregate nature masks the underlying behavioral mechanisms that ultimately drive fertility trends. To recover these mechanisms, we develop a likelihood-free Bayesian framework that couples an individual-level model of the reproductive process with Sequential Neural Posterior Estimation (SNPE). This allows us to infer eight behavioral and biological parameters from just two aggregate serie…

This is incredibly exciting!
Is It All a Fluke? Lessons From Playing God in the Long-Term Evolution Experiment - by Brian Klaas - Behavioral Scientist https://behavioralscientist.org/is-it-all-a-fluke-lessons-from-playing-god-…

Is It All a Fluke? Lessons From Playing God in the Long-Term Evolution Experiment - by Brian Klaas - Behavioral Scientist
It’s easy to believe that small, chance moments don’t make a material difference on our lives and societies. But a long-running evolution experiment suggests that life’s course may come down to the random details.

Dual-level Behavioral Consistency for Inter-group and Intra-group Coordination in Multi-Agent Systems
Shuocun Yang, Huawen Hu, Enze Shi, Shu Zhang
https://arxiv.org/abs/2506.18651

Dual-level Behavioral Consistency for Inter-group and Intra-group Coordination in Multi-Agent Systems
Behavioral diversity in Multi-agent reinforcement learning(MARL) represents an emerging and promising research area. Prior work has largely centered on intra-group behavioral consistency in multi-agent systems, with limited attention given to behavioral consistency in multi-agent grouping scenarios. In this paper, we introduce Dual-Level Behavioral Consistency (DLBC), a novel MARL control method designed to explicitly regulate agent behaviors at both intra-group and inter-group levels. DLBC par…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

"Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task"
https://doi.org/10.48550/arXiv.2506.08872
"[…] While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four mont…

Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
Patrick Gerard, Hans W. A. Hanley, Luca Luceri, Emilio Ferrara
https://arxiv.org/abs/2505.21729

Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
Political discourse has grown increasingly fragmented across different social platforms, making it challenging to trace how narratives spread and evolve within such a fragmented information ecosystem. Reconstructing social graphs and information diffusion networks is challenging, and available strategies typically depend on platform-specific features and behavioral signals which are often incompatible across systems and increasingly restricted. To address these challenges, we present a platform…

Behavioral Traits as Dynamical Systems: Utilizing Entropy to Analyze Longitudinal Psychometric Data in Coupled Ordinary Differential Equations
Anderson M. Rodriguez
https://arxiv.org/abs/2506.20622

Behavioral Traits as Dynamical Systems: Utilizing Entropy to Analyze Longitudinal Psychometric Data in Coupled Ordinary Differential Equations
Traits such as neuroticism persist across species despite exhibiting characteristics typically regarded as maladaptive. This project presents an alternative model for understanding the stability of such traits by integrating findings from the Swedish Adoption/Twin Study on Aging (Pedersen, 2015) (SATSA) with a system of recursive, biologically inspired ordinary differential equations (ODEs). To utilize the ODEs analytically, Shannon entropy is extracted from longitudinal Likert-scale psychometr…

A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
Vinaik Chhetri, Yousaf Reza, Moghis Fereidouni, Srijata Maji, Umar Farooq, AB Siddique
https://arxiv.org/abs/2506.17285

A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
Modern recommendation systems typically follow two complementary paradigms: collaborative filtering, which models long-term user preferences from historical interactions, and conversational recommendation systems (CRS), which interact with users in natural language to uncover immediate needs. Each captures a different dimension of user intent. While CRS models lack collaborative signals, leading to generic or poorly personalized suggestions, traditional recommenders lack mechanisms to interacti…

Mapping Brain-Behavior Correlations in Autism Using Heat Kernel Smoothing
Moo K. Chung, Kim M. Dalton, Daniel J. Kelley, Richard J. Davidson
https://arxiv.org/abs/2507.00473

Mapping Brain-Behavior Correlations in Autism Using Heat Kernel Smoothing
This paper presents a streamlined image analysis framework for correlating behavioral measures to anatomical measures on the cortex and detecting the regions of abnormal brain-behavior correlates. We correlated a facial emotion discrimination task score and its response time to cortical thickness measurements in a group of high functioning autistic subjects. Many previous correlation studies in brain imaging neglect to account for unwanted age effect and other variables and the subsequent stati…

Toward a Data Processing Pipeline for Mobile-Phone Tracking Data
Marcin Jurek, Catherine A. Calder, Corwin Zigler, Bethany Boettner, Christopher R. Browning
https://arxiv.org/abs/2507.00952

Toward a Data Processing Pipeline for Mobile-Phone Tracking Data
As mobile phones become ubiquitous, high-frequency smartphone positioning data are increasingly being used by researchers studying the mobility patterns of individuals as they go about their daily routines and the consequences of these patterns for health, behavioral, and other outcomes. A complex data pipeline underlies empirical research leveraging mobile phone tracking data. A key component of this pipeline is transforming raw, time-stamped positions into analysis-ready data objects, typical…

Finance as Extended Biology: Reciprocity as the Cognitive Substrate of Financial Behavior
Egil Diau
https://arxiv.org/abs/2506.00099 https://

Finance as Extended Biology: Reciprocity as the Cognitive Substrate of Financial Behavior
A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge from decentralized social interaction. We argue that these functions are not institutionally engineered, but arise from a single behavioral substrate: reciprocity. Observed across primates and foundational to early human societies, reciprocity governed the exchange of goods, regulation of obligation, and maintenance of cooperation-without formal rules. T…

Auditory gamma-frequency entrainment abolishes working memory deficits in a rodent model of autism #40Hz sensory entra…

Auditory Gamma-Frequency Entrainment Abolishes Working Memory Deficits in a Rodent Model of Autism
Individuals on the autism spectrum often exhibit atypical responses to sensory stimuli and difficulties with behavioral regulation, reflecting altered functional patterns in core sensory-motor circuits. These sensory processing differences suggest the potential for using patterned auditory stimulation to modulate neural activity and mitigate symptoms. While impairments in higher cognitive functions are a hallmark of autism, they remain underexplored in rodent models compared to basic sensory-mo…

Classifying and Clustering Trading Agents
Mateusz Wilinski, Anubha Goel, Alexandros Iosifidis, Juho Kanniainen
https://arxiv.org/abs/2505.21662 https://

Classifying and Clustering Trading Agents
The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial investors based on their behavioral patterns. Not only do we have access to both classification and clustering tools for high-dimensional data, but also data identifying individual investors is fina…

"Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity."
"LLM users also struggled to accurately quote their own work."
"Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels."

Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market
Penggan Xu
https://arxiv.org/abs/2505.20608 https://

Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market
This study replicates the findings of Wang et al. (2017) on reference-dependent preferences and their impact on the risk-return trade-off in the Chinese stock market, a unique context characterized by high retail investor participation, speculative trading behavior, and regulatory complexities. Capital Gains Overhang (CGO), a proxy for unrealized gains or losses, is employed to explore how behavioral biases shape cross-sectional stock returns in an emerging market setting. Utilizing data from 1…

The Evolution of Altruistic Rationality Provides a Solution to Social Dilemmas via Rational Reciprocity
Mohammad Salahshour, Iain D. Couzin
https://arxiv.org/abs/2507.00858

The Evolution of Altruistic Rationality Provides a Solution to Social Dilemmas via Rational Reciprocity
Decades of scientific inquiry have sought to understand how evolution fosters cooperation, a concept seemingly at odds with the belief that evolution should produce rational, self-interested individuals. Most previous work has focused on the evolution of cooperation among boundedly rational individuals whose decisions are governed by behavioral rules that do not need to be rational. Here, using an evolutionary model, we study how altruism can evolve in a community of rational agents and promote…

The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Avinash Baidya, Kamalika Das, Xiang Gao
https://arxiv.org/abs/2506.12266

The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot scenarios. While prior work has noted this performance gap, the behavioral factors driving the performance gap remain under-explored. This study proposes a comprehensive evaluation framework to quantify the behavior gap between AI agents and human experts, focusing on discrepancies in dialog acts, tool usage, and kno…

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
Runkai Li, Jia Xiong, Xi Wang
https://arxiv.org/abs/2505.22086 https://…

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
High-Level Synthesis (HLS) serves as an agile hardware development tool that streamlines the circuit design by abstracting the register transfer level into behavioral descriptions, while allowing designers to customize the generated microarchitectures through optimization directives. However, the combinatorial explosion of possible directive configurations yields an intractable design space. Traditional design space exploration (DSE) methods, despite adopting heuristics or constructing predicti…

Decidable Reversible Equivalences for Finite Petri Nets
Roberto Gorrieri, Ivan Lanese
https://arxiv.org/abs/2506.11517 https://arxiv.…

Decidable Reversible Equivalences for Finite Petri Nets
In the setting of Petri nets, we prove that {\em causal-net bisimilarity} \cite{G15,Gor22,Gor25a}, which is a refinement of history-preserving bisimilarity \cite{RT88,vGG89,DDM89}, and the novel {\em hereditary} causal-net bisimilarity, which is a refinement of hereditary history-preserving bisimilarity \cite{Bed91,JNW96}, do coincide. This means that causal-net bisimilarity is a {\em reversible behavioral equivalence}, as causal-net bisimilar markings not only are able to match each other's fo…

Very interesting discussion!
https://www.thetransmitter.org/attention/the-brains-quiet-conductor-how-hidden-cells-fine-tune-arousal/

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Feature Integration Spaces: Joint Training Reveals Dual Encoding in Neural Network Representations
Omar Claflin
https://arxiv.org/abs/2507.00269 https://…

Feature Integration Spaces: Joint Training Reveals Dual Encoding in Neural Network Representations
Current sparse autoencoder (SAE) approaches to neural network interpretability assume that activations can be decomposed through linear superposition into sparse, interpretable features. Despite high reconstruction fidelity, SAEs consistently fail to eliminate polysemanticity and exhibit pathological behavioral errors. We propose that neural networks encode information in two complementary spaces compressed into the same substrate: feature identity and feature integration. To test this dual enc…

This https://arxiv.org/abs/2505.13688 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Gaze-Enhanced Multimodal Turn-Taking Prediction in Triadic Conversations
Turn-taking prediction is crucial for seamless interactions. This study introduces a novel, lightweight framework for accurate turn-taking prediction in triadic conversations without relying on computationally intensive methods. Unlike prior approaches that either disregard gaze or treat it as a passive signal, our model integrates gaze with speaker localization, structuring it within a spatial constraint to transform it into a reliable predictive cue. Leveraging egocentric behavioral cues, our…

GATSim: Urban Mobility Simulation with Generative Agents
Qi Liu, Can Li, Wanjing Ma
https://arxiv.org/abs/2506.23306 https://arxiv.or…

GATSim: Urban Mobility Simulation with Generative Agents
Traditional agent-based urban mobility simulations rely on rigid rule-based systems that fail to capture the complexity, adaptability, and behavioral diversity characteristic of human travel decision-making. Recent advances in large language models and AI agent technology offer opportunities to create agents with reasoning capabilities, persistent memory, and adaptive learning mechanisms. We propose GATSim (Generative-Agent Transport Simulation), a novel framework that leverages these advances …

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

LLM-based Dynamic Differential Testing for Database Connectors with Reinforcement Learning-Guided Prompt Selection
Ce Lyu, Minghao Zhao, Yanhao Wang, Liang Jie
https://arxiv.org/abs/2506.11870

LLM-based Dynamic Differential Testing for Database Connectors with Reinforcement Learning-Guided Prompt Selection
Database connectors are critical components enabling applications to interact with underlying database management systems (DBMS), yet their security vulnerabilities often remain overlooked. Unlike traditional software defects, connector vulnerabilities exhibit subtle behavioral patterns and are inherently challenging to detect. Besides, nonstandardized implementation of connectors leaves potential risks (a.k.a. unsafe implementations) but is more elusive. As a result, traditional fuzzing method…

REDDIX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services
MSVPJ Sathvik, Manan Roy Choudhury, Rishita Agarwal, Sathwik Narkedimilli, Vivek Gupta
https://arxiv.org/abs/2505.23231

REDDIX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services
The rise of online platforms has enabled covert illicit activities, including online prostitution, to pose challenges for detection and regulation. In this study, we introduce REDDIX-NET, a novel benchmark dataset specifically designed for moderating online sexual services and going beyond traditional NSFW filters. The dataset is derived from thousands of web-scraped NSFW posts on Reddit and categorizes users into six behavioral classes reflecting different service offerings and user intentions…

ZK-SERIES: Privacy-Preserving Authentication using Temporal Biometric Data
Daniel Reijsbergen, Eyasu Getahun Chekole, Howard Halim, Jianying Zhou
https://arxiv.org/abs/2506.19393 …

ZK-SERIES: Privacy-Preserving Authentication using Temporal Biometric Data
Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as time series and compared to a set of base readings. If the distance between the new readings and the base readings falls within an acceptable threshold, then the user is successfully authenticated. Various…

Upgrade or Switch: Do We Need a New Registry Architecture for the Internet of AI Agents?
Ramesh Raskar, Pradyumna Chari, Jared James Grogan, Mahesh Lambe, Robert Lincourt, Raghu Bala, Abhishek Singh, Ayush Chopra, Rajesh Ranjan, Shailja Gupta, Dimitris Stripelis, Maria Gorskikh, Sichao Wang
https://arxiv.org/abs/2506.12003

Upgrade or Switch: Do We Need a New Registry Architecture for the Internet of AI Agents?
The emerging Internet of AI Agents challenges existing web infrastructure designed for human-scale, reactive interactions. Unlike traditional web resources, autonomous AI agents initiate actions, maintain persistent state, spawn sub-agents, and negotiate directly with peers: demanding millisecond-level discovery, instant credential revocation, and cryptographic behavioral proofs that exceed current DNS/PKI capabilities. This paper analyzes whether to upgrade existing infrastructure or implement…

Towards Bug-Free Distributed Go Programs
Zhengqun Koo
https://arxiv.org/abs/2506.15135 https://arxiv.org/pdf/2506.15135

Towards Bug-Free Distributed Go Programs
Programmers of distributed systems need to reason about concurrency to avoid races. However, reasoning about concurrency is difficult, and unexpected races show up as bugs. Data race detection in shared memory systems is well-studied (dynamic data race detection [13], behavioral types [15], dynamic race detection [31]). Similar to how a data race consists of reads and writes not related by happens-before at a shared memory location, a communication race consists of receives and sends not relate…

Haptic-Based User Authentication for Tele-robotic System
Rongyu Yu, Kan Chen, Zeyu Deng, Chen Wang, Burak Kizilkaya, Liying Emma Li
https://arxiv.org/abs/2506.14116

Haptic-Based User Authentication for Tele-robotic System
Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To …

Missing data in non-stationary multivariate time series from digital studies in Psychiatry
Xiaoxuan Cai, Charlotte R. Fowler, Li Zeng, Habiballah Rahimi Eichi, Dost Ongur, Lisa Dixon, Justin T. Baker, Jukka-Pekka Onnela, Linda Valeri
https://arxiv.org/abs/2506.14946

Missing data in non-stationary multivariate time series from digital studies in Psychiatry
Mobile technology (e.g., mobile phones and wearable devices) provides scalable methods for collecting physiological and behavioral biomarkers in patients' naturalistic settings, as well as opportunities for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous data collection through mobile devices generates highly complex data: entangled multivariate time series of outcomes, exposures, and covariates. Missing data is a pervasive problem i…

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering
Johan \"Ostman, Edvin Callisen, Anton Chen, Kristiina Ausmees, Emanuel G{\aa}rdh, Jovan Zamac, Jolanta Goldsteine, Hugo Wefer, Simon Whelan, Markus Reimeg{\aa}rd
https://arxiv.org/abs/2506.13989

AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering
Money laundering enables organized crime by allowing illicit funds to enter the legitimate economy. Although trillions of dollars are laundered each year, only a small fraction is ever uncovered. This stems from a range of factors, including deliberate evasion by launderers, the rarity of confirmed cases, and the limited visibility each financial institution has into the global transaction network. While several synthetic datasets are available, they fail to model the structural and behavioral …

Modulating task outcome value to mitigate real-world procrastination via noninvasive brain stimulation
Zhiyi Chen, Zhilin Ren, Wei Li, ZhenZhen Huo, ZhuangZheng Wang, Ye Liu, Bowen Hu, Wanting Chen, Ting Xu, Artemiy Leonov, Chenyan Zhang, Bernhard Hommel, Tingyong Feng
https://arxiv.org/abs/2506.21000

Modulating task outcome value to mitigate real-world procrastination via noninvasive brain stimulation
Procrastination represents one of the most prevalent behavioral problems affecting individual health and societal productivity. Although it is often conceptualized as a form of self-control failure, its underlying neurocognitive mechanisms are poorly understood. A leading model posits that procrastination arises from imbalanced competing motivations: the avoidance of negative task aversiveness and the pursuit of positive task outcomes, yet this theoretical framework has not fully validated in r…

LLM-Based Social Simulations Require a Boundary
Zengqing Wu, Run Peng, Takayuki Ito, Chuan Xiao
https://arxiv.org/abs/2506.19806 https://

LLM-Based Social Simulations Require a Boundary
This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries to meaningfully contribute to social science research. While LLMs offer promising capabilities for modeling human-like agents compared to traditional agent-based modeling, they face fundamental limitations that constrain their reliability for social pattern discovery. The core issue lies in LLMs' tendency towards an ``average persona'' that lacks sufficient behavioral heterogene…

This https://arxiv.org/abs/2502.20636 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Delayed-Decision Motion Planning in the Presence of Multiple Predictions
Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents to have intentions that are unknown to others, leaving an automated driving car to reason over multiple possible behaviors. This paper formalizes a behavior planning scheme in the presence of multiple possible futures with corresponding probabilities. We present a maximum entropy formulation and show how, under certai…

Toward Practical Privacy in XR: Empirical Analysis of Multimodal Anonymization Mechanisms
Azim Ibragimov, Ethan Wilson, Kevin R. B. Butler, Eakta Jain
https://arxiv.org/abs/2506.13882

Toward Practical Privacy in XR: Empirical Analysis of Multimodal Anonymization Mechanisms
As extended reality (XR) systems become increasingly immersive and sensor-rich, they enable the collection of fine-grained behavioral signals such as eye and body telemetry. These signals support personalized and responsive experiences and may also contain unique patterns that can be linked back to individuals. However, privacy mechanisms that naively pair unimodal mechanisms (e.g., independently apply privacy mechanisms for eye and body privatization) are often ineffective at preventing re-ide…

Large Language Models as Psychological Simulators: A Methodological Guide
Zhicheng Lin
https://arxiv.org/abs/2506.16702 https://arxiv…

Large Language Models as Psychological Simulators: A Methodological Guide
Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary applications: simulating roles and personas to explore diverse contexts, and serving as computational models to investigate cognitive processes. For simulation, we present methods for developing psychologically grounded personas that move beyond demographic cate…

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Tahiya Chowdhury, Veronica Romero
https://arxiv.org/abs/2506.11072

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture b…

AI Agent Behavioral Science
Lin Chen, Yunke Zhang, Jie Feng, Haoye Chai, Honglin Zhang, Bingbing Fan, Yibo Ma, Shiyuan Zhang, Nian Li, Tianhui Liu, Nicholas Sukiennik, Keyu Zhao, Yu Li, Ziyi Liu, Fengli Xu, Yong Li
https://arxiv.org/abs/2506.06366

AI Agent Behavioral Science
Recent advances in large language models (LLMs) have enabled AI systems to behave in increasingly human-like ways, exhibiting planning, adaptation, and social dynamics across increasingly diverse, interactive, and open-ended scenarios. These behaviors are not solely the product of the models' internal architecture, but emerge from their integration into agentic systems that operate within situated contexts, where goals, feedback, and interactions shape behavior over time. This shift calls for a…

Enhancing Meme Token Market Transparency: A Multi-Dimensional Entity-Linked Address Analysis for Liquidity Risk Evaluation
Qiangqiang Liu, Qian Huang, Frank Fan, Haishan Wu, Xueyan Tang
https://arxiv.org/abs/2506.05359

Enhancing Meme Token Market Transparency: A Multi-Dimensional Entity-Linked Address Analysis for Liquidity Risk Evaluation
Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This paper introduces an innovative approach to assessing liquidity risk in meme token markets using entity-linked address identification techniques. We propose a multi-dimensional method integrating fund flow analysis, behavioral similarity, and anomalous transaction detection to iden…

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Nanda Rani, Sandeep Kumar Shukla
https://arxiv.org/abs/2506.10175

AURA: A Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution
Effective attribution of Advanced Persistent Threats (APTs) increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA (Attribution Using Retrieval-Augmented Agents), a multi-agent, knowledge-enhanced framework for automated and interpretable APT attribution. AURA ingests diverse threat data including Tactics, Techniques, and Procedures (TTPs), Indicators of Compromise (IoCs), malware details, adversarial to…

This https://arxiv.org/abs/2505.05660 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs
As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles or dialects that are associated with specific minoritized lived experiences (e.g., African American English, Queer slang). In this work, we examine whether sociolect usage by an LLM agent affects user reliance on its outputs and user perception (satisfaction, frustration, trust, and social presence). We designed and co…

Doppelg\"anger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Daewon Kang, YeongHwan Shin, Doyeon Kim, Kyu-Hwan Jung, Meong Hi Son
https://arxiv.org/abs/2506.14539

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts, along with the pressing challenge of preventing those prompts from being exposed to user's attempts. In this paper, we propose the ''Doppelgänger method'' to demonstrate the risk of an agent being hij…

Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
Soyeon Choi, Kangwook Lee, Oliver Sng, Joshua M. Ackerman
https://arxiv.org/abs/2506.13783

Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to th…

SocialCredit
Thabassum Aslam, Anees Aslam
https://arxiv.org/abs/2506.12099 https://arxiv.org/pdf/2506.12099

SocialCredit+
SocialCredit+ is AI powered credit scoring system that leverages publicly available social media data to augment traditional credit evaluation. It uses a conversational banking assistant to gather user consent and fetch public profiles. Multimodal feature extractors analyze posts, bios, images, and friend networks to generate a rich behavioral profile. A specialized Sharia-compliance layer flags any non-halal indicators and prohibited financial behavior based on Islamic ethics. The platform emp…

Geo-Somatic Resonance Theory A Vibrational Framework for Sleep as Planetary Entrainment
Brathikan Vijayamohan Mankayarkarasi
https://arxiv.org/abs/2506.14760

Geo-Somatic Resonance Theory A Vibrational Framework for Sleep as Planetary Entrainment
Sleep is commonly studied through neurochemical, evolutionary, and behavioral frameworks, typically emphasizing circadian rhythms and energy conservation. However, these approaches do not fully explain a deeper biophysical question: why does sleep universally involve physical stillness, a lying posture, and disconnection from conscious control? This paper introduces a new hypothesis that sleep is not merely a biological function, but a state of vibrational synchronization between the human body…

FIST: A Structured Threat Modeling Framework for Fraud Incidents
Yu-Chen Dai, Lu-An Chen, Sy-Jye Her, Yu-Xian Jiang
https://arxiv.org/abs/2506.05740 https:…

FIST: A Structured Threat Modeling Framework for Fraud Incidents
Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework (Fraud Incident Structured Threat Framework), an innovative structured threat modeling methodology specifically designed for fraud scenarios. Inspired by MITRE ATT\&CK and DISARM, FIST systematically incorporates social engineering tactics, stage-based behavioral decomposition, and detail…

This https://arxiv.org/abs/2401.08960 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utiliz…

POCO: Scalable Neural Forecasting through Population Conditioning
Yu Duan, Hamza Tahir Chaudhry, Misha B. Ahrens, Christopher D Harvey, Matthew G Perich, Karl Deisseroth, Kanaka Rajan
https://arxiv.org/abs/2506.14957

POCO: Scalable Neural Forecasting through Population Conditioning
Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to closed-loop neurotechnology. While recent models of population activity emphasize interpretability and behavioral decoding, neural forecasting-particularly across multi-session, spontaneous recordings-remains underexplored. We introduce POCO, a unified forecasting model that combines a lightweight univariate forecaster with a population-level encoder to ca…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

Understanding Self-Regulated Learning Behavior Among High and Low Dropout Risk Students During CS1: Combining Trace Logs, Dropout Prediction and Self-Reports
Denis Zhidkikh, Ville Isom\"ott\"onen, Toni Taipalus
https://arxiv.org/abs/2506.09178

Understanding Self-Regulated Learning Behavior Among High and Low Dropout Risk Students During CS1: Combining Trace Logs, Dropout Prediction and Self-Reports
The introductory programming course (CS1) at the university level is often perceived as particularly challenging, contributing to high dropout rates among Computer Science students. Identifying when and how students encounter difficulties in this course is critical for providing targeted support. This study explores the behavioral patterns of CS1 students at varying dropout risks using self-regulated learning (SRL) as the theoretical framework. Using learning analytics, we analyzed trace logs a…

Discounting and Drug Seeking in Biological Hierarchical Reinforcement Learning
Vardhan Palod, Pranav Mahajan, Veeky Baths, Boris S. Gutkin
https://arxiv.org/abs/2506.04549

Discounting and Drug Seeking in Biological Hierarchical Reinforcement Learning
Despite a strong desire to quit, individuals with long-term substance use disorder (SUD) often struggle to resist drug use, even when aware of its harmful consequences. This disconnect between knowledge and compulsive behavior reflects a fundamental cognitive-behavioral conflict in addiction. Neurobiologically, differential cue-induced activity within striatal subregions, along with dopamine-mediated connectivity from the ventral to the dorsal striatum, contributes to compulsive drug-seeking. H…