Tootfinder

Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning
Ma\"el Macuglia, Paul Friedrich, Giorgia Ramponi
https://arxiv.org/abs/2509.26605 https…

Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning
Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by proposing a two-stage framework that first learns a safe initial policy from a reward-free dataset of expert demonstrations, then fine-tunes it online using preference-based human feedback. We provide the first principled analysis of this offline-to-online approach and introduce…

Large language models for behavioral modeling: A literature survey
Muhammad Laiq
https://arxiv.org/abs/2509.24782 https://arxiv.org/pdf/2509.24782

Large language models for behavioral modeling: A literature survey
In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, parti…

DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete
https://arxiv.org/abs/2509.26562

DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However, their unpredictable and unreliable behaviors in real-world settings require new approaches to characterize and ensure their reliability. This paper introduces DeepProv, a novel and customizable system designed to capture and characterize the runtime behavior of DNNs during inference by using their underlying graph structure. Inspired by system a…

FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization
Tianyang Luo, Xikun Zhang, Dongjin Song
https://arxiv.org/abs/2510.00014 https://

FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization
Why do trillion-dollar tech giants AAPL and MSFT diverge into different response patterns during market disruptions despite identical sector classifications? This paradox reveals a fundamental limitation: traditional community detection methods fail to capture synchronization-desynchronization patterns where entities move independently yet align during critical moments. To this end, we introduce FTSCommDetector, implementing our Temporal Coherence Architecture (TCA) to discover similar and diss…

Developer Insights into Designing AI-Based Computer Perception Tools
Maya Guhan (Center for Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA), Meghan E. Hurley (Center for Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA), Eric A. Storch (Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA), John Herrington (Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospita…

Developer Insights into Designing AI-Based Computer Perception Tools
Artificial intelligence (AI)-based computer perception (CP) technologies use mobile sensors to collect behavioral and physiological data for clinical decision-making. These tools can reshape how clinical knowledge is generated and interpreted. However, effective integration of these tools into clinical workflows depends on how developers balance clinical utility with user acceptability and trustworthiness. Our study presents findings from 20 in-depth interviews with developers of AI-based CP to…

Data-Driven Optimal Power Flow: A Behavioral Systems Approach
Sebastian Otzen, Hannes M. H. Wolf, Christian A. Hans
https://arxiv.org/abs/2509.25120 https://

Data-Driven Optimal Power Flow: A Behavioral Systems Approach
The increasing decentralization of power systems driven by a large number of renewable energy sources poses challenges in power flow optimization. Partially unknown power line properties can render model-based approaches unsuitable. With increasing deployment of sensors, data-driven methods rise as a promising alternative. They offer the flexibility to adapt to varying grid structures and unknown line properties. In this paper, we propose a novel data-driven representation of nonlinear power fl…

Aligning Video Models with Human Social Judgments via Behavior-Guided Fine-Tuning
Kathy Garcia, Leyla Isik
https://arxiv.org/abs/2510.01502 https://arxiv.o…

Aligning Video Models with Human Social Judgments via Behavior-Guided Fine-Tuning
Humans intuitively perceive complex social signals in visual scenes, yet it remains unclear whether state-of-the-art AI models encode the same similarity structure. We study (Q1) whether modern video and language models capture human-perceived similarity in social videos, and (Q2) how to instill this structure into models using human behavioral data. To address this, we introduce a new benchmark of over 49,000 odd-one-out similarity judgments on 250 three-second video clips of social interactio…

Circuit Distillation
Somin Wadhwa, Silvio Amir, Byron C. Wallace
https://arxiv.org/abs/2509.25002 https://arxiv.org/pdf/2509.25002

Circuit Distillation
Model distillation typically focuses on behavioral mimicry, where a student model is trained to replicate a teacher's output while treating its internal computations as a black box. In this work we propose an alternative approach: Distilling the underlying computational mechanisms implemented by a teacher model. Specifically, we propose circuit distillation, which introduces an objective to align internal representations between analogous circuit components in teacher and student models. We pro…

Neural and behavioral reinstatement jointly reflect retrieval of narrative events https://www.nature.com/articles/s41467-025-62375-9 by @…

Neural and behavioral reinstatement jointly reflect retrieval of narrative events - Nature Communications
When people recall a movie, their eye movements and brain activity resemble those observed during the viewing. These behavioral and neural reactivations are linked through a common process, likely reflecting the specific internal experiences that emerge in an instance of recall.

Portfolio Analysis Based on Markowitz Stochastic Dominance Criteria: A Behavioral Perspective
Peng Xu
https://arxiv.org/abs/2509.22896 https://arxiv.org/pd…

Portfolio Analysis Based on Markowitz Stochastic Dominance Criteria: A Behavioral Perspective
This paper develops stochastic optimization problems for describing and analyzing behavioral investors with Markowitz Stochastic Dominance (MSD) preferences. Specifically, we establish dominance conditions in a discrete state-space to capture all reverse S-shaped MSD preferences as well as all subjective decision weights generated by inverse S-shaped probability weighting functions. We demonstrate that these dominance conditions can be admitted as linear constraints into the stochastic optimiza…

Beyond Pass/Fail: The Story of Learning-Based Testing
Sheikh Md. Mushfiqur Rahman, Nasir Eisty
https://arxiv.org/abs/2510.00450 https://arxiv.org/pdf/2510.…

Beyond Pass/Fail: The Story of Learning-Based Testing
Learning-Based Testing (LBT) merges learning and testing processes to achieve both testing and behavioral adequacy. LBT utilizes active learning to infer the model of the System Under Test (SUT), enabling scalability for large and complex programs by requiring only a minimal set of initial test cases. The core principle of LBT is that the SUT's behavior can be thoroughly inferred by progressively generating test cases and subjecting the SUT to testing, thereby ensuring comprehensive testing. De…

“The Beginning and Ending of Threat Actors” by @…
“Instead, Volt Typhoon becomes a construct: a cluster of linked actions, behavioral tendencies, and targeting preferences linked to PRC interests.”
This article provides a concise and clear overview of what often happens with threat actor labeling. If you are working in “threat intelligence,” read …

The Beginning and Ending of Threat Actors
In July 2025, NSA officials at a conference in New York City made a surprising claim: “The good news is, [Volt Typhoon] really failed. They wanted to persist in domestic networks very quietly for a…

Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics
Ayato Kitadai, Yusuke Fukasawa, Nariaki Nishino
https://arxiv.org/abs/2508.18600 https://…

Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics
Large language models (LLMs) are increasingly used to simulate human decision-making, but their intrinsic biases often diverge from real human behavior--limiting their ability to reflect population-level diversity. We address this challenge with a persona-based approach that leverages individual-level behavioral data from behavioral economics to adjust model biases. Applying this method to the ultimatum game--a standard but difficult benchmark for LLMs--we observe improved alignment between sim…

UML-CoT: Structured Reasoning and Planning with Unified Modeling Language for Robotic Room Cleaning
Hongyu Chen, Guangrun Wang
https://arxiv.org/abs/2509.22628 https://

UML-CoT: Structured Reasoning and Planning with Unified Modeling Language for Robotic Room Cleaning
Chain-of-Thought (CoT) prompting improves reasoning in large language models (LLMs), but its reliance on unstructured text limits interpretability and executability in embodied tasks. Prior work has explored structured CoTs using scene or logic graphs, yet these remain fundamentally limited: they model only low-order relations, lack constructs like inheritance or behavioral abstraction, and provide no standardized semantics for sequential or conditional planning. We propose UML-CoT, a structure…

Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications in AI-assisted Data Analysis
Qianou Ma, Kenneth Koedinger, Tongshuang Wu
https://arxiv.org/abs/2509.21890

Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications in AI-assisted Data Analysis
LLMs promise to democratize technical work in complex domains like programmatic data analysis, but not everyone benefits equally. We study how students with varied expertise use LLMs to complete Python-based data analysis in computational notebooks in a non-major course. Drawing on homework logs, recordings, and surveys from 36 students, we ask: Which expertise matters most, and how does it shape AI use? Our mixed-methods analysis shows that technical expertise -- not AI familiarity or communic…

HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots
Shipeng Lyu, Fangyuan Wang, Weiwei Lin, Luhao Zhu, David Navarro-Alarcon, Guodong Guo
https://arxiv.org/abs/2508.19002

HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots
Achieving both behavioral similarity and appropriateness in human-like motion generation for humanoid robot remains an open challenge, further compounded by the lack of cross-embodiment adaptability. To address this problem, we propose HuBE, a bi-level closed-loop framework that integrates robot state, goal poses, and contextual situations to generate human-like behaviors, ensuring both behavioral similarity and appropriateness, and eliminating structural mismatches between motion generation an…

Mobility Behavior Evolution During Extended Emergencies: Returners, Explorers, and the 15-Minute City
Omid Armantalab, Jason Hawkins, Wissam Kontar
https://arxiv.org/abs/2510.00469

Mobility Behavior Evolution During Extended Emergencies: Returners, Explorers, and the 15-Minute City
Understanding human mobility during emergencies is critical for strengthening urban resilience and guiding emergency management. This study examines transitions between returners, who repeatedly visit a limited set of locations, and explorers, who travel across broader destinations, over a 15-day emergency period in a densely populated metropolitan region using the YJMob100K dataset. High-resolution spatial data reveal intra-urban behavioral dynamics often masked at coarser scales. Beyond stati…

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 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Evolution of cooperation among migrating resource-oriented agents under environmental variability
Masaaki Inaba, Eizo Akiyama
https://arxiv.org/abs/2508.21352 https://

Evolution of cooperation among migrating resource-oriented agents under environmental variability
Cooperation is fundamental to human societies. While several basic theoretical mechanisms underlying its evolution have been established, research addressing more realistic settings remains underdeveloped. Drawing on the hypothesis that intensified environmental fluctuations drove early behavioral evolution in humans during the Middle Stone Age in Africa, we examine the effects of environmental variability and human mobility on the evolution of cooperation. In our model, the variability is repr…

LLM Found Transmitting Behavioral Traits to 'Student' LLM Via Hidden Signals in Data - Slashdot
https://slashdot.org/story/25/08/17/0331217/llm-found-transmitting-behavioral-traits-to-student-llm-via-hidden-signals-in-data

Modeling the control of West Nile virus using mosquito reduction methods, vaccination of equids, and human behavioral adaptation to the usage of personal protective equipment
Pride Duve, Felix Gregor Sauer, Renke L\"uhken
https://arxiv.org/abs/2509.24657

Modeling the control of West Nile virus using mosquito reduction methods, vaccination of equids, and human behavioral adaptation to the usage of personal protective equipment
West Nile virus (WNV) is a mosquito-borne virus of the genus Flaviviridae circulating between mosquitoes and birds, while humans, equids, and other mammals are dead-end hosts. Several preventive measures are recommended to reduce the WNV burden among different hosts. In this work, we develop a mathematical framework for evaluating the theoretical effectiveness of various WNV control methods in Germany. We consider mosquito reduction methods such as the physical removal and destruction of potent…

Studie: Viele wollen von ihren Krankheiten nichts wissen
https://www.mpib-berlin.mpg.de/pressemeldungen/medizinische-informationsvermeidung

Ein Drittel der Menschen meidet Informationen zur eigenen Gesundheit
Eine aktuelle Übersichtsarbeit des Max-Planck-Instituts für Bildungsforschung zeigt, dass rund ein Drittel der Menschen Informationen zu schweren Krankheiten aus dem Weg gehen, wenn sie fürchten, betroffen zu sein. Ein entscheidender Grund ist mangelndes Vertrauen in das Gesundheitssystem. Die Studie ist im Fachjournal Annals of Behavioral Medicine erschienen.

Crosslisted article(s) found for q-fin.RM. https://arxiv.org/list/q-fin.RM/new
[1/1]:
- Portfolio Analysis Based on Markowitz Stochastic Dominance Criteria: A Behavioral Perspective
Peng Xu

Information Design With Large Language Models
Paul Duetting, Safwan Hossain, Tao Lin, Renato Paes Leme, Sai Srivatsa Ravindranath, Haifeng Xu, Song Zuo
https://arxiv.org/abs/2509.25565

Information Design With Large Language Models
Information design is typically studied through the lens of Bayesian signaling, where signals shape beliefs based on their correlation with the true state of the world. However, Behavioral Economics and Psychology emphasize that human decision-making is more complex and can depend on how information is framed. This paper formalizes a language-based notion of framing and bridges this to the popular Bayesian-persuasion model. We model framing as a possibly non-Bayesian, linguistic way to influenc…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[8/8]:
- EigenBench: A Comparative Behavioral Measure of Value Alignment
Jonathn Chang, Leonhard Piff, Suvadip Sana, Jasmine X. Li, Lionel Levine

Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience
Maria Glushanina, Jeffrey Huang, Michelle McCleod, Brendan Ames, Evie Malaia
https://arxiv.org/abs/2509.23479

Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience
Social communication fundamentally involves at least two interacting brains, creating a unique modeling problem. We present the first application of Contrastive Embedding for Behavioral and Neural Analysis (CEBRA) to dyadic EEG hyperscanning data, extending modeling paradigms to interpersonal neural dynamics. Using structured social interactions between participants, we demonstrate that CEBRA can learn meaningful representations of joint neural activity that captures individual roles (speaker-l…

Dynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecture
Abhishek Sharma, Anat Parush, Sumit Wadhwa, Amihai Savir, Anne Guinard, Prateek Srivastava
https://arxiv.org/abs/2509.20244

Dynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecture
Accurate forecasting in the e-commerce finance domain is particularly challenging due to irregular invoice schedules, payment deferrals, and user-specific behavioral variability. These factors, combined with sparse datasets and short historical windows, limit the effectiveness of conventional time-series methods. While deep learning and Transformer-based models have shown promise in other domains, their performance deteriorates under partial observability and limited historical data. To address…

MIT Provost
Anantha P. Chandrakasan told an audience of about 300 undergraduates from the Class of 2029:
“The goal is for MIT to become one of the world’s most prolific, collaborative, and interdisciplinary sources of technological, behavioral, and policy solutions for the global climate challenge over the next decade...
It is something we need to do urgently, and today is your opportunity to play a role in that bold mission.”

Over 1,000 MIT students inspired to work toward climate solutions
New MIT students recently stepped into the shoes of global decision-makers, using the En-ROADS climate solutions simulator to test climate policies, explore solutions, and envision the future they want to create.

MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation
Yi Xu, Moyu Zhang, Chenxuan Li, Zhihao Liao, Haibo Xing, Hao Deng, Jinxin Hu, Yu Zhang, Xiaoyi Zeng, Jing Zhang
https://arxiv.org/abs/2508.15281

MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation
Recommender systems traditionally represent items using unique identifiers (ItemIDs), but this approach struggles with large, dynamic item corpora and sparse long-tail data, limiting scalability and generalization. Semantic IDs, derived from multimodal content such as text and images, offer a promising alternative by mapping items into a shared semantic space, enabling knowledge transfer and improving recommendations for new or rare items. However, existing methods face two key challenges: (1) …

"Having Lunch Now": Understanding How Users Engage with a Proactive Agent for Daily Planning and Self-Reflection
Adnan Abbas, Caleb Wohn, Arnav Jagtap, Eugenia H Rho, Sang Won Lee
https://arxiv.org/abs/2509.24073

"Having Lunch Now": Understanding How Users Engage with a Proactive Agent for Daily Planning and Self-Reflection
Conversational agents have been studied as tools to scaffold planning and self-reflection for productivity and well-being. While prior work has demonstrated positive outcomes, we still lack a clear understanding of what drives these results and how users behave and communicate with agents that act as coaches rather than assistants. Such understanding is critical for designing interactions in which agents foster meaningful behavioral change. We conducted a 14-day longitudinal study with 12 parti…

Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Kai Hu, Parfait Atchade-Adelomou, Carlo Adornetto, Adrian Mora-Carrero, Luis Alonso-Pastor, Ariel Noyman, Yubo Liu, Kent Larson
https://arxiv.org/abs/2508.16172

Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Understanding human behavior in urban environments is a crucial field within city sciences. However, collecting accurate behavioral data, particularly in newly developed areas, poses significant challenges. Recent advances in generative agents, powered by Large Language Models (LLMs), have shown promise in simulating human behaviors without relying on extensive datasets. Nevertheless, these methods often struggle with generating consistent, context-sensitive, and realistic behavioral outputs. T…

A Hybrid Optimal Velocity Drag Model for Traffic Flow Dynamics
Nizhum Rahman, Trachette L. Jackson
https://arxiv.org/abs/2509.22671 https://arxiv.org/pdf/2…

A Hybrid Optimal Velocity Drag Model for Traffic Flow Dynamics
We develop a Hybrid Optimal Velocity-Drag (OVD) model that combines the behavioral structure of the classical Optimal Velocity Model (OVM) with a drag-based saturation law motivated by Newtonian mechanics. The model retains the OVM principle that desired speed depends on headway, but replaces the linear relaxation law with a formulation that enforces bounded accelerations and smooth convergence to equilibrium. After reviewing the OVM framework and its stability properties, we introduce the OVD …

SAHM: State-Aware Heterogeneous Multicore for Single-Thread Performance
Shayne Wadle, Karthikeyan Sankaralingam
https://arxiv.org/abs/2509.22405 https://ar…

SAHM: State-Aware Heterogeneous Multicore for Single-Thread Performance
Improving single-thread performance remains a critical challenge in modern processor design, as conventional approaches such as deeper speculation, wider pipelines, and complex out-of-order execution face diminishing returns. This work introduces SAHM-State-Aware Heterogeneous Multicore-a novel architecture that targets performance gains by exploiting fine-grained, time-varying behavioral diversity in single-threaded workloads. Through empirical characterization of performance counter data, we …

Depression diagnosis from patient interviews using multimodal machine learning
Jana Weber, Marcel Weber, Juan Miguel Lopez Alcaraz
https://arxiv.org/abs/2508.19390 https://

Depression diagnosis from patient interviews using multimodal machine learning
Background: Depression is a major public health concern, affecting an estimated five percent of the global population. Early and accurate diagnosis is essential to initiate effective treatment, yet recognition remains challenging in many clinical contexts. Speech, language, and behavioral cues collected during patient interviews may provide objective markers that support clinical assessment. Methods: We developed a diagnostic approach that integrates features derived from patient interviews, …

Reducing False Positives with Active Behavioral Analysis for Cloud Security
Dikshant, Verma
https://arxiv.org/abs/2508.12584 https://arxiv.org/pdf/2508.12…

Reducing False Positives with Active Behavioral Analysis for Cloud Security
Rule-based cloud security posture management (CSPM) solutions are known to produce a lot of false positives based on the limited contextual understanding and dependence on static heuristics testing. This paper introduces a validation-driven methodology that integrates active behavioral testing in cloud security posture management solution(s) to evaluate the exploitability of policy violations in real time. The proposed system employs lightweight and automated probes, built from open-source tool…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Kalyani Khona
https://arxiv.org/abs/2509.13337 https://arxi…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Understanding how users authentically interact with Large Language Models (LLMs) remains a significant challenge in human-computer interaction research. Most existing studies rely on self-reported usage patterns or controlled experimental conditions, potentially missing genuine behavioral adaptations. This study presents a behavioral analysis of the use of English-speaking urban professional ChatGPT in India based on 238 authentic, unedited user prompts from 40 participants in 15+ Indian cities…

BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
Donipolo Ghimire, Aamodh Suresh, Carlos Nieto-Granda, Solmaz S. Kia
https://arxiv.org/abs/2508.10363

BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy(BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior …

AutiHero: Leveraging Generative AI in Social Narratives to Engage Parents in Story-Driven Behavioral Guidance for Autistic Children
Jungeun Lee, Kyungah Lee, Inseok Hwang, SoHyun Park, Young-Ho Kim
https://arxiv.org/abs/2509.17608

AutiHero: Leveraging Generative AI in Social Narratives to Engage Parents in Story-Driven Behavioral Guidance for Autistic Children
Social narratives are known to help autistic children understand and navigate social situations through stories. To ensure effectiveness, however, the materials need to be customized to reflect each child's unique behavioral context, requiring considerable time and effort for parents to practice at home. We present AutiHero, a generative AI-based social narrative system for behavioral guidance, which supports parents to create personalized stories for their autistic children and read them toget…

Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers
Sindhuja Penchala, Saketh Reddy Kontham, Prachi Bhattacharjee, Sareh Karami, Mehdi Ghahremani, Noorbakhsh Amiri Golilarz, Shahram Rahimi
https://arxiv.org/abs/2508.15782

Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers
In early childhood education, accurately detecting behavioral and collaborative engagement is essential for fostering meaningful learning experiences. This paper presents an AI-driven approach that leverages Vision Transformers (ViTs) to automatically classify children's engagement using visual cues such as gaze direction, interaction, and peer collaboration. Utilizing the Child-Play gaze dataset, our method is trained on annotated video segments to classify behavioral and collaborative engagem…

Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
Guandong Pan, Yaqian Yang, Shi Chen, Xin Wang, Longzhao Liu, Hongwei Zheng, Shaoting Tang
https://arxiv.org/abs/2509.21381

Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
In affective neuroscience and emotion-aware AI, understanding how complex auditory stimuli drive emotion arousal dynamics remains unresolved. This study introduces a computational framework to model the brain's encoding of naturalistic auditory inputs into dynamic behavioral/neural responses across three datasets (SEED, LIRIS, self-collected BAVE). Guided by neurobiological principles of parallel auditory hierarchy, we decompose audio into multilevel auditory features (through classical algorit…

kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
Jenna Kline, Maksim Kholiavchenko, Samuel Stevens, Nina van Tiel, Alison Zhong, Namrata Banerji, Alec Sheets, Sowbaranika Balasubramaniam, Isla Duporge, Matthew Thompson, Elizabeth Campolongo, Jackson Miliko, Neil Rosser, Tanya Berger-Wolf, Charles V. Stewart, Daniel I. Rubenstein
https://

kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
A comprehensive understanding of animal behavior ecology depends on scalable approaches to quantify and interpret complex, multidimensional behavioral patterns. Traditional field observations are often limited in scope, time-consuming, and labor-intensive, hindering the assessment of behavioral responses across landscapes. To address this, we present kabr-tools (Kenyan Animal Behavior Recognition Tools), an open-source package for automated multi-species behavioral monitoring. This framework in…

Rank Ordering of Species Sensitivities to Chemicals: Discrepancies between Behavioral and Lethal Responses in Freshwater and Marine Invertebrates
Julien Mocq (RiverLy), George Ruck (RiverLy), Micha\"el Demortier (RiverLy), Flavie Brun (RiverLy), Romain Coulaud (SEBIO), Olivier Geffard (RiverLy), Arnaud Chaumot (RiverLy)
https://arxiv.…

Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions
Xiaoyi Wang, Jiwei Zhang, Guangtao Zhang, Honglei Guo
https://arxiv.org/abs/2508.20764

Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions
Synthetic therapy dialogues generated by large language models (LLMs) are increasingly used in mental health NLP to simulate counseling scenarios, train models, and supplement limited real-world data. However, it remains unclear whether these synthetic conversations capture the nuanced emotional dynamics of real therapy. In this work, we conduct the first comparative analysis of emotional arcs between real and LLM-generated Cognitive Behavioral Therapy dialogues. We adapt the Utterance Emotion …

Goal-Directedness is in the Eye of the Beholder
Nina Rajcic, Anders S{\o}gaard
https://arxiv.org/abs/2508.13247 https://arxiv.org/pdf/2508.13247

Goal-Directedness is in the Eye of the Beholder
Our ability to predict the behavior of complex agents turns on the attribution of goals. Probing for goal-directed behavior comes in two flavors: Behavioral and mechanistic. The former proposes that goal-directedness can be estimated through behavioral observation, whereas the latter attempts to probe for goals in internal model states. We work through the assumptions behind both approaches, identifying technical and conceptual problems that arise from formalizing goals in agent systems. We arr…

Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Kai Hu, Parfait Atchade-Adelomou, Carlo Adornetto, Adrian Mora-Carrero, Luis Alonso-Pastor, Ariel Noyman, Yubo Liu, Kent Larson
https://arxiv.org/abs/2508.16172

Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
Understanding human behavior in urban environments is a crucial field within city sciences. However, collecting accurate behavioral data, particularly in newly developed areas, poses significant challenges. Recent advances in generative agents, powered by Large Language Models (LLMs), have shown promise in simulating human behaviors without relying on extensive datasets. Nevertheless, these methods often struggle with generating consistent, context-sensitive, and realistic behavioral outputs. T…

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

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
Wei Xu, Jiasen Zheng, Junjiang Lin, Mingxuan Han, Junliang Du
https://arxiv.org/abs/2509.04694

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
This paper addresses the challenge of jointly modeling user intent diversity and behavioral uncertainty in recommender systems. A unified representation learning framework is proposed. The framework builds a multi-intent representation module and an uncertainty modeling mechanism. It extracts multi-granularity interest structures from user behavior sequences. Behavioral ambiguity and preference fluctuation are captured using Bayesian distribution modeling. In the multi-intent modeling part, the…

AI Behavioral Science
Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang, Yutong Xie, Walter Yuan, Seth Benzell, Erik Brynjolfsson, Colin F. Camerer, James Evans, Brian Jabarian, Jon Kleinberg, Juanjuan Meng, Sendhil Mullainathan, Asuman Ozdaglar, Thomas Pfeiffer, Moshe Tennenholtz, Robb Willer, Diyi Yang, Teng Ye
https://arxiv.org/abs/2509…

AI Behavioral Science
We discuss the three main areas comprising the new and emerging field of "AI Behavioral Science". This includes not only how AI can enhance research in the behavioral sciences, but also how the behavioral sciences can be used to study and better design AI and to understand how the world will change as AI and humans interact in increasingly layered and complex ways.

Preliminary Study on Space Utilization and Emergent Behaviors of Group vs. Single Pedestrians in Real-World Trajectories
Amartaivan Sanjjamts, Morita Hiroshi
https://arxiv.org/abs/2508.18939

Preliminary Study on Space Utilization and Emergent Behaviors of Group vs. Single Pedestrians in Real-World Trajectories
This study presents an initial framework for distinguishing group and single pedestrians based on real-world trajectory data, with the aim of analyzing their differences in space utilization and emergent behavioral patterns. By segmenting pedestrian trajectories into fixed time bins and applying a Transformer-based pair classification model, we identify cohesive groups and isolate single pedestrians over a structured sequence-based filtering process. To prepare for deeper analysis, we establish…

Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Delibration
Haoran Zhang, Yafu Li, Xuyang Hu, Dongrui Liu, Zhilin Wang, Bo Li, Yu Cheng
https://arxiv.org/abs/2509.14760

Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Delibration
Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications (spec) custom-tailored by users or organizations. These spec, categorized into safety-spec and behavioral-spec, vary across scenarios and evolve with changing preferences and requirements. We formalize this challenge as specification alignment, focusing on LLMs' ability to follow dynamic, scenario-specific spec from both behavioral and safety persp…

BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
Parsa Vahidi, Omid G. Sani, Maryam M. Shanechi
https://arxiv.org/abs/2509.18627 https://

BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
Neural populations exhibit complex recurrent structures that drive behavior, while continuously receiving and integrating external inputs from sensory stimuli, upstream regions, and neurostimulation. However, neural populations are often modeled as autonomous dynamical systems, with little consideration given to the influence of external inputs that shape the population activity and behavioral outcomes. Here, we introduce BRAID, a deep learning framework that models nonlinear neural dynamics un…

MindGuard: Tracking, Detecting, and Attributing MCP Tool Poisoning Attack via Decision Dependence Graph
Zhiqiang Wang, Junyang Zhang, Guanquan Shi, HaoRan Cheng, Yunhao Yao, Kaiwen Guo, Haohua Du, Xiang-Yang Li
https://arxiv.org/abs/2508.20412

MindGuard: Tracking, Detecting, and Attributing MCP Tool Poisoning Attack via Decision Dependence Graph
The Model Context Protocol (MCP) is increasingly adopted to standardize the interaction between LLM agents and external tools. However, this trend introduces a new threat: Tool Poisoning Attacks (TPA), where tool metadata is poisoned to induce the agent to perform unauthorized operations. Existing defenses that primarily focus on behavior-level analysis are fundamentally ineffective against TPA, as poisoned tools need not be executed, leaving no behavioral trace to monitor. Thus, we propose M…

Localized risk perception triggers early behavioral adaptations in epidemics on networks
Baltazar Espinoza, Jimmy Calvo-Monge, Fabio Sanchez, Madhav Marathe
https://arxiv.org/abs/2508.05704

Localized risk perception triggers early behavioral adaptations in epidemics on networks
The contact structure of the population shapes the progression of epidemics. Nonetheless, the joint evolution of individual behavioral adaptations and disease dynamics on networks remains poorly understood. We use a behavioral-epidemiological model to study the joint evolution of human behavior and epidemic dynamics on networks. Our results reveal how the adaptation of local social structures, influenced by risk-benefit trade-offs, affects the dynamics of epidemics. We allow the epidemic and po…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[7/7]:
- Decomposing Behavioral Phase Transitions in LLMs: Order Parameters for Emergent Misalignment
Julian Arnold, Niels L\"orch

Deterministic Frequency--Domain Inference of Network Topology and Hidden Components via Structure--Behavior Scaling
Xiaoxiao Liang, Tianlong Fan, Linyuan L\"u
https://arxiv.org/abs/2509.19857

Deterministic Frequency--Domain Inference of Network Topology and Hidden Components via Structure--Behavior Scaling
Hidden interactions and components in complex systems-ranging from covert actors in terrorist networks to unobserved brain regions and molecular regulators-often manifest only through indirect behavioral signals. Inferring the underlying network structure from such partial observations remains a fundamental challenge, particularly under nonlinear dynamics. We uncover a robust linear relationship between the spectral strength of a node's behavioral time series under evolutionary game dynamics an…

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

Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling
Avinash Ajit Nargund, Arthur Caetano, Kevin Yang, Rose Yiwei Liu, Philip Tezaur, Kriteen Shrestha, Qisen Pan, Tobias H\"ollerer, Misha Sra
https://arxiv.org/abs/2509.20666

Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling
Human-AI collaboration is typically offered in one of two of user control levels: guidance, where the AI provides suggestions and the human makes the final decision, and delegation, where the AI acts autonomously within user-defined constraints. Systems that integrate both modes, common in robotic surgery or driving assistance, often overlook shifts in user preferences within a task in response to factors like evolving trust, decision complexity, and perceived control. In this work, we investig…

AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
Jinhao Chai, Anqing Jiang, Hao Jiang, Shiyi Mu, Zichong Gu, Shugong Xu
https://arxiv.org/abs/2509.20253

AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
End-to-end multi-modal planning has become a transformative paradigm in autonomous driving, effectively addressing behavioral multi-modality and the generalization challenge in long-tail scenarios. We propose AnchDrive, a framework for end-to-end driving that effectively bootstraps a diffusion policy to mitigate the high computational cost of traditional generative models. Rather than denoising from pure noise, AnchDrive initializes its planner with a rich set of hybrid trajectory anchors. Thes…

Neural and behavioral correlates of evidence accumulation in human click-based #echolocation https://www.biorxiv.org/content/10.1101/2025.08.30.673202v1 by @…

Sub-THz Power Amplifiers: Measurements, Behavioral Modeling and Predistortion Algorithms
Lutfi Samara, Simon Haussmann, Erind Tufa, Antonio Alberto D'Amico, Tommaso Zugno, Ingmar Kallfass, Thomas K\"urner
https://arxiv.org/abs/2508.09545

Sub-THz Power Amplifiers: Measurements, Behavioral Modeling and Predistortion Algorithms
With global IMT traffic expected to grow 10-100 times from 2020 to 20301, the Terahertz (THz) spectrum offers a promising solution to satisfy such forecasts. However, occupying the THz spectrum comes with its own challenges, an important one being impairments caused by broadband RF components in THz transceivers. Nonlinearities in power amplifiers (PAs) complicate meeting link budget requirements, with amplitude and phase distortions degrading the system's performance, especially when adopting …

Behavioral-feedback SIR epidemic model: analysis and control
Martina Alutto, Leonardo Cianfanelli, Giacomo Como, Fabio Fagnani, Francesca Parise
https://arxiv.org/abs/2509.12257

Behavioral-feedback SIR epidemic model: analysis and control
This paper investigates a behavioral-feedback SIR model in which the infection rate adapts dynamically based on the fractions of susceptible and infected individuals. We introduce an invariant of motion and we characterize the peak of infection. We further examine the system under a threshold constraint on the infection level. Based on this analysis, we formulate an optimal control problem to keep the infection curve below a healthcare capacity threshold while minimizing the economic cost. For …

Sycophancy as compositions of Atomic Psychometric Traits
Shreyans Jain, Alexandra Yost, Amirali Abdullah
https://arxiv.org/abs/2508.19316 https://arxiv.org…

Sycophancy as compositions of Atomic Psychometric Traits
Sycophancy is a key behavioral risk in LLMs, yet is often treated as an isolated failure mode that occurs via a single causal mechanism. We instead propose modeling it as geometric and causal compositions of psychometric traits such as emotionality, openness, and agreeableness - similar to factor decomposition in psychometrics. Using Contrastive Activation Addition (CAA), we map activation directions to these factors and study how different combinations may give rise to sycophancy (e.g., high e…

Re-imagining Behavioral Sleep Medicine: Designing Conversational Sleep Diary and Visualization Tool
Amama Mahmood, Bokyung Kim, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Michael T. Smith, Chien-Ming Huang
https://arxiv.org/abs/2509.15378

Re-imagining Behavioral Sleep Medicine: Designing Conversational Sleep Diary and Visualization Tool
The sleep diary is a widely used clinical tool for understanding sleep disorders; however, low patient compliance and limited capture of contextual information constrain its effectiveness and leave specialists with an incomplete picture of patients' sleep-related behaviors. In this work, we re-imagine Behavioral Sleep Medicine (BSM) by designing a voice-based conversational sleep diary and specialist-facing visualization tool. Through this design process, we probed specialists' vision of how co…

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation
Shaswata Mitra, Azim Bazarov, Martin Duclos, Sudip Mittal, Aritran Piplai, Md Rayhanur Rahman, Edward Zieglar, Shahram Rahimi
https://arxiv.org/abs/2508.18684

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation
Signature-based Intrusion Detection Systems (IDS) detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence (CTI), which includes attack signatures and behavioral patterns obtained through automated tools and manual threat analysis, such as sandboxing. The CTI is then transformed into actionable rules for the IDS engine, enabling real-time detection and prevention. However, the constant evolution o…

MCeT: Behavioral Model Correctness Evaluation using Large Language Models
Khaled Ahmed, Jialing Song, Boqi Chen, Ou Wei, Bingzhou Zheng
https://arxiv.org/abs/2508.00630 https://…

MCeT: Behavioral Model Correctness Evaluation using Large Language Models
Behavioral model diagrams, e.g., sequence diagrams, are an essential form of documentation that are typically designed by system engineers from requirements documentation, either fully manually or assisted by design tools. With the growing use of Large Language Models (LLM) as AI modeling assistants, more automation will be involved in generating diagrams. This necessitates the advancement of automatic model correctness evaluation tools. Such a tool can be used to evaluate both manually and AI …

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

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

Vision encoders should be image size agnostic and task driven
Nedyalko Prisadnikov, Danda Pani Paudel, Yuqian Fu, Luc Van Gool
https://arxiv.org/abs/2508.16317 https://

Vision encoders should be image size agnostic and task driven
This position paper argues that the next generation of vision encoders should be image size agnostic and task driven. The source of our inspiration is biological. Not a structural aspect of biological vision, but a behavioral trait -- efficiency. We focus on a couple of ways in which vision in nature is efficient, but modern vision encoders not. We -- humans and animals -- deal with vast quantities of visual data, and need to be smart where we focus our limited energy -- it depends on the task.…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Localized risk perception triggers early behavioral adaptations in epidemics on networks
Baltazar Espinoza, Jimmy Calvo-Monge, Fabio Sanchez, Simon A. Levin, Madhav Marathe

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
Pinhao Song, Yurui Du, Ophelie Saussus, Sofie De Schrijver, Irene Caprara, Peter Janssen, Renaud Detry
https://arxiv.org/abs/2509.19954

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
We propose a probabilistic shared-control solution for navigation, called Robot Trajectron V2 (RT-V2), that enables accurate intent prediction and safe, effective assistance in human-robot interaction. RT-V2 jointly models a user's long-term behavioral patterns and their noisy, low-dimensional control signals by combining a prior intent model with a posterior update that accounts for real-time user input and environmental context. The prior captures the multimodal and history-dependent nature o…

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 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

A Concurrent Modular Agent: Framework for Autonomous LLM Agents
Norihiro Maruyama, Takahide Yoshida, Hiroki Sato, Atsushi Masumori, Johnsmith, Takashi Ikegami
https://arxiv.org/abs/2508.19042

A Concurrent Modular Agent: Framework for Autonomous LLM Agents
We introduce the Concurrent Modular Agent (CMA), a framework that orchestrates multiple Large-Language-Model (LLM)-based modules that operate fully asynchronously yet maintain a coherent and fault-tolerant behavioral loop. This framework addresses long-standing difficulties in agent architectures by letting intention emerge from language-mediated interactions among autonomous processes. This approach enables flexible, adaptive, and context-dependent behavior through the combination of concurren…

Attackers Strike Back? Not Anymore - An Ensemble of RL Defenders Awakens for APT Detection
Sidahmed Benabderrahmane, Talal Rahwan
https://arxiv.org/abs/2508.19072 https://

Attackers Strike Back? Not Anymore - An Ensemble of RL Defenders Awakens for APT Detection
Advanced Persistent Threats (APTs) represent a growing menace to modern digital infrastructure. Unlike traditional cyberattacks, APTs are stealthy, adaptive, and long-lasting, often bypassing signature-based detection systems. This paper introduces a novel framework for APT detection that unites deep learning, reinforcement learning (RL), and active learning into a cohesive, adaptive defense system. Our system combines auto-encoders for latent behavioral encoding with a multi-agent ensemble of …

MsFIN: Multi-scale Feature Interaction Network for Traffic Accident Anticipation
Tongshuai Wu, Chao Lu, Ze Song, Yunlong Lin, Sizhe Fan, Xuemei Chen
https://arxiv.org/abs/2509.19227

MATRIX: Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation
Ernest Lim, Yajie Vera He, Jared Joselowitz, Kate Preston, Mohita Chowdhury, Louis Williams, Aisling Higham, Katrina Mason, Mariane Melo, Tom Lawton, Yan Jia, Ibrahim Habli
https://arxiv.org/abs/2508.19163

MATRIX: Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation
Despite the growing use of large language models (LLMs) in clinical dialogue systems, existing evaluations focus on task completion or fluency, offering little insight into the behavioral and risk management requirements essential for safety-critical systems. This paper presents MATRIX (Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation), a structured, extensible framework for safety-oriented evaluation of clinical dialogue agents. MATRIX …

BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Mingxuan Xiao, Yan Xiao, Shunhui Ji, Jiahe Tu, Pengcheng Zhang
https://arxiv.org/abs/2509.17335

BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Fuzzing has shown great success in evaluating the robustness of intelligent natural language processing (NLP) software. As large language model (LLM)-based NLP software is widely deployed in critical industries, existing methods still face two main challenges: 1 testing methods are insufficiently coupled with the behavioral patterns of LLM-based NLP software; 2 fuzzing capability for the testing scenario of natural language generation (NLG) generally degrades. To address these issues, we propos…

Evasive Ransomware Attacks Using Low-level Behavioral Adversarial Examples
Manabu Hirano, Ryotaro Kobayashi
https://arxiv.org/abs/2508.08656 https://arxiv.…

Evasive Ransomware Attacks Using Low-level Behavioral Adversarial Examples
Protecting state-of-the-art AI-based cybersecurity defense systems from cyber attacks is crucial. Attackers create adversarial examples by adding small changes (i.e., perturbations) to the attack features to evade or fool the deep learning model. This paper introduces the concept of low-level behavioral adversarial examples and its threat model of evasive ransomware. We formulate the method and the threat model to generate the optimal source code of evasive malware. We then examine the method u…

Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
Ryoga Oishi, Sho Sakaino, Toshiaki Tsuji
https://arxiv.org/abs/2509.04737 https://…

Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains challenging, as such instructions are often qualitative and require exploring behaviors that satisfy varying conditions. This paper proposes a motion generation model that adapts robot actions in response to modifier directives human instructions imposing behavioral conditions during task execution. The proposed metho…

Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems
Jie Zeng, Yukiko I. Nakano
https://arxiv.org/abs/2508.20635 https://

Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems
The primary goal of Motivational Interviewing (MI) is to help clients build their own motivation for behavioral change. To support this in dialogue systems, it is essential to guide large language models (LLMs) to generate counselor responses aligned with MI principles. By employing a schema-guided approach, this study proposes a method for updating multi-frame dialogue states and a strategy decision mechanism that dynamically determines the response focus in a manner grounded in MI principles.…

LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Huizhen Shu, Xuying Li, Zhuo Li
https://arxiv.org/abs/2509.19839 https:/…

LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Achieving robust safety alignment in large language models (LLMs) while preserving their utility remains a fundamental challenge. Existing approaches often struggle to balance comprehensive safety with fine-grained controllability at the representation level. We introduce LATENTGUARD, a novel three-stage framework that combines behavioral alignment with supervised latent space control for interpretable and precise safety steering. Our approach begins by fine-tuning an LLM on rationalized datase…

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Zixia Zhou, Junyan Liu, Wei Emma Wu, Ruogu Fang, Sheng Liu, Qingyue Wei, Rui Yan, Yi Guo, Qian Tao, Yuanyuan Wang, Md Tauhidul Islam, Lei Xing
https://arxiv.org/abs/2508.11672

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive…

Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
Hossein Yousefizadeh, Shenghui Gu, Lionel C. Briand, Ali Nasr
https://arxiv.org/abs/2509.16478

Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
Autonomous systems, such as autonomous driving systems, evolve rapidly through frequent updates, risking unintended behavioral degradations. Effective system-level testing is challenging due to the vast scenario space, the absence of reliable test oracles, and the need for practically applicable and interpretable test cases. We present CoCoMagic, a novel automated test case generation method that combines metamorphic testing, differential testing, and advanced search-based techniques to identif…

Unveiling the Landscape of Clinical Depression Assessment: From Behavioral Signatures to Psychiatric Reasoning
Zhuang Chen, Guanqun Bi, Wen Zhang, Jiawei Hu, Aoyun Wang, Xiyao Xiao, Kun Feng, Minlie Huang
https://arxiv.org/abs/2508.04531

Unveiling the Landscape of Clinical Depression Assessment: From Behavioral Signatures to Psychiatric Reasoning
Depression is a widespread mental disorder that affects millions worldwide. While automated depression assessment shows promise, most studies rely on limited or non-clinically validated data, and often prioritize complex model design over real-world effectiveness. In this paper, we aim to unveil the landscape of clinical depression assessment. We introduce C-MIND, a clinical neuropsychiatric multimodal diagnosis dataset collected over two years from real hospital visits. Each participant comple…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/7]:
- Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers
Penchala, Kontham, Bhattacharjee, Karami, Ghahremani, Golilarz, Rahimi

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
Basil Abdullah AL-Zahrani
https://arxiv.org/abs/2510.02424 https://

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. …

Emotional Manipulation by AI Companions
Julian De Freitas, Zeliha O\u{g}uz-U\u{g}uralp, Ahmet Kaan-U\u{g}uralp
https://arxiv.org/abs/2508.19258 https://arx…

Emotional Manipulation by AI Companions
AI-companion apps such as Replika, Chai, and Character.ai promise relational benefits-yet many boast session lengths that rival gaming platforms while suffering high long-run churn. What conversational design features increase consumer engagement, and what trade-offs do they pose for marketers? We combine a large-scale behavioral audit with four preregistered experiments to identify and test a conversational dark pattern we call emotional manipulation: affect-laden messages that surface precise…

Realtime Multimodal Emotion Estimation using Behavioral and Neurophysiological Data
Von Ralph Dane Marquez Herbuela, Yukie Nagai
https://arxiv.org/abs/2508.09402 https://…

Realtime Multimodal Emotion Estimation using Behavioral and Neurophysiological Data
Many individuals especially those with autism spectrum disorder (ASD), alexithymia, or other neurodivergent profiles face challenges in recognizing, expressing, or interpreting emotions. To support more inclusive and personalized emotion technologies, we present a real-time multimodal emotion estimation system that combines neurophysiological EEG, ECG, blood volume pulse (BVP), and galvanic skin response (GSR/EDA) and behavioral modalities (facial expressions, and speech) in a unified arousal-v…

Artificially intelligent agents in the social and behavioral sciences: A history and outlook
Petter Holme, Milena Tsvetkova
https://arxiv.org/abs/2510.05743 https://

Artificially intelligent agents in the social and behavioral sciences: A history and outlook
We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specifi…

TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
Jiahao Liu, Bonan Ruan, Xianglin Yang, Zhiwei Lin, Yan Liu, Yang Wang, Tao Wei, Zhenkai Liang
https://arxiv.org/abs/2510.11203

TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of attacks, such as tool poisoning and malicious instructions, that compromise their execution flow and can lead to serious consequences like data breaches and financial loss. Existing studies typically attempt to mitigate such anomalies by predefining specific rules and enforcing them at runtime to enhance safety. Yet, designing comprehensive rules is di…

From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens
Hala Sheta, Eric Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi
https://arxiv.org/abs/2510.02292

From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens
We introduce VLM-Lens, a toolkit designed to enable systematic benchmarking, analysis, and interpretation of vision-language models (VLMs) by supporting the extraction of intermediate outputs from any layer during the forward pass of open-source VLMs. VLM-Lens provides a unified, YAML-configurable interface that abstracts away model-specific complexities and supports user-friendly operation across diverse VLMs. It currently supports 16 state-of-the-art base VLMs and their over 30 variants, and …

Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality
Denmar Mojan Gonzales, Snehanjali Kalamkar, Sophie J\"org, Jens Grubert
https://arxiv.org/abs/2508.11781

Behavioral and Symbolic Fillers as Delay Mitigation for Embodied Conversational Agents in Virtual Reality
When communicating with embodied conversational agents (ECAs) in virtual reality, there might be delays in the responses of the agents lasting several seconds, for example, due to more extensive computations of the answers when large language models are used. Such delays might lead to unnatural or frustrating interactions. In this paper, we investigate filler types to mitigate these effects and lead to a more positive experience and perception of the agent. In a within-subject study, we asked 2…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/7]:
- Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers
Penchala, Kontham, Bhattacharjee, Karami, Ghahremani, Golilarz, Rahimi

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Huizhen Shu, Xuying Li, Zhuo Li
https://arxiv.org/abs/2509.19839 https:/…

LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation
Achieving robust safety alignment in large language models (LLMs) while preserving their utility remains a fundamental challenge. Existing approaches often struggle to balance comprehensive safety with fine-grained controllability at the representation level. We introduce LATENTGUARD, a novel three-stage framework that combines behavioral alignment with supervised latent space control for interpretable and precise safety steering. Our approach begins by fine-tuning an LLM on rationalized datase…

Log2Sig: Frequency-Aware Insider Threat Detection via Multivariate Behavioral Signal Decomposition
Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Zhiying Li, Guanggang Geng
https://arxiv.org/abs/2508.05696

Log2Sig: Frequency-Aware Insider Threat Detection via Multivariate Behavioral Signal Decomposition
Insider threat detection presents a significant challenge due to the deceptive nature of malicious behaviors, which often resemble legitimate user operations. However, existing approaches typically model system logs as flat event sequences, thereby failing to capture the inherent frequency dynamics and multiscale disturbance patterns embedded in user behavior. To address these limitations, we propose Log2Sig, a robust anomaly detection framework that transforms user logs into multivariate behav…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/11]:
- Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations
Yunzhe Wang, Gale M. Lucas, Burcin Becerik-Gerber, Volkan Ustun

The Signalgate Case is Waiving a Red Flag to All Organizational and Behavioral Cybersecurity Leaders, Practitioners, and Researchers: Are We Receiving the Signal Amidst the Noise?
Paul Benjamin Lowry, Gregory D. Moody, Robert Willison, Clay Posey
https://arxiv.org/abs/2509.07053

The Signalgate Case is Waiving a Red Flag to All Organizational and Behavioral Cybersecurity Leaders, Practitioners, and Researchers: Are We Receiving the Signal Amidst the Noise?
The Signalgate incident of March 2025, wherein senior US national security officials inadvertently disclosed sensitive military operational details via the encrypted messaging platform Signal, highlights critical vulnerabilities in organizational security arising from human error, governance gaps, and the misuse of technology. Although smaller in scale when compared to historical breaches involving billions of records, Signalgate illustrates critical systemic issues often overshadowed by a focu…

DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation
Shenwei Kang, Xin Zhang, Wen Liu, Bin Li, Yujie Liu, Bo Gao
https://arxiv.org/abs/2509.17711

DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation
Human engagement estimation in conversational scenarios is essential for applications such as adaptive tutoring, remote healthcare assessment, and socially aware human--computer interaction. Engagement is a dynamic, multimodal signal conveyed by facial expressions, speech, gestures, and behavioral cues over time. In this work we introduce DA-Mamba, a dialogue-aware multimodal architecture that replaces attention-heavy dialogue encoders with Mamba-based selective state-space processing to achiev…

Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare
Valen Tagliabue, Leonard Dung
https://arxiv.org/abs/2509.07961 https://…

Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare
We develop new experimental paradigms for measuring welfare in language models. We compare verbal reports of models about their preferences with preferences expressed through behavior when navigating a virtual environment and selecting conversation topics. We also test how costs and rewards affect behavior and whether responses to an eudaimonic welfare scale - measuring states such as autonomy and purpose in life - are consistent across semantically equivalent prompts. Overall, we observed a no…

Structured Cognition for Behavioral Intelligence in Large Language Model Agents: Preliminary Study
Myung Ho Kim
https://arxiv.org/abs/2510.05107 https://ar…

Structured Cognition for Behavioral Intelligence in Large Language Model Agents: Preliminary Study
Large language models have advanced natural language understanding and generation, yet their use as autonomous agents raises architectural challenges for multi-step tasks. Existing frameworks often intertwine inference, memory, and control in a single prompt, which can reduce coherence and predictability. The Structured Cognitive Loop (SCL) is introduced as an alternative architecture that separates these functions. In SCL, the language model is dedicated to inference, memory is maintained exte…

EigenBench: A Comparative Behavioral Measure of Value Alignment
Jonathn Chang, Leonard Piff, Suvadip Sana, Jasmine X. Li, Lionel Levine
https://arxiv.org/abs/2509.01938 https://…

EigenBench: A Comparative Behavioral Measure of Value Alignment
Aligning AI with human values is a pressing unsolved problem. To address the lack of quantitative metrics for value alignment, we propose EigenBench: a black-box method for comparatively benchmarking language models' values. Given an ensemble of models, a constitution describing a value system, and a dataset of scenarios, our method returns a vector of scores quantifying each model's alignment to the given constitution. To produce these scores, each model judges the outputs of other models acro…