Tootfinder

We’ve published new research from the EU co-funded project NGSOTI: “Learning from large-scale IPv4 blackhole: Behavioral analysis of SNMP traffic”.
Over a 12-month period (Nov 2024–Oct 2025), our network telescope captured ~634 million unsolicited SNMP queries from more than 153,000 unique IPv4 sources scanning an unused /18 block.
The origins of the traffic are globally distributed, with notable concentrations from Indonesia, China, the United States, Germany, Chile and others, …

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

Proposition 36, approved by voters last year, gave prosecutors the ability to charge people convicted of various third-time drug offenses with a so-called
treatment-mandated felony
– a choice between behavioral health treatment or up to three years in jail or prison.
https://bit.ly/3NiBX0r
📸 J.…

How justice changed in California in 2025: Prop. 36 toughened penalties, prisons kept shrinking
Gov. Gavin Newsom in 2025 announced the closure of another prison, a shutdown that's possible because the state is incarcerating far fewer people than it did just a decade ago.

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

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…

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…

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…

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…

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…

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

Noisy traffic is making Galšpagos’ yellow warblers angry https://news.mongabay.com/short-article/2025/12/noisy-traffic-is-making-galapagos-yellow-warblers-angry/

Noisy traffic is making Galápagos’ yellow warblers angry
A recent study found that birds that live closer to roads display more aggression than birds of the same species that live farther away from noisy vehicles, Mongabay’s Spoorthy Raman reported. Researchers looked at the behavioral differences of male Galápagos yellow warblers (Setophaga petechia aureola) on two islands of the Galápagos, an Ecuadorian archipelago in […]

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.

Bio-Inspired Robotic Houbara: From Development to Field Deployment for Behavioral Studies
Lyes Saad Saoud, Irfan Hussain
https://arxiv.org/abs/2510.04692 https://

Bio-Inspired Robotic Houbara: From Development to Field Deployment for Behavioral Studies
Biomimetic intelligence and robotics are transforming field ecology by enabling lifelike robotic surrogates that interact naturally with animals under real world conditions. Studying avian behavior in the wild remains challenging due to the need for highly realistic morphology, durable outdoor operation, and intelligent perception that can adapt to uncontrolled environments. We present a next generation bio inspired robotic platform that replicates the morphology and visual appearance of the fe…

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 …

I was looking into the #DANDI archive, following a post of @… that mentioned it as a possibility for #Neuroscience data storage.. It looks like it could be useful i…

Total #SolarEclipse triggers dawn behavior in birds: Insights from acoustic recordings and community science: https://www.science.org/doi/10.1126/science.adx3025 -> "Compiling more than 10,000 community observations and artificial intelligence analyses of nearly 100,000 vocalizations, we found that bird behavior was substantially affected by these few minutes of unexpected afternoon darkness. More than half of wild bird species changed their biological rhythms, with many producing a dawn chorus in the aftermath of the eclipse. This natural experiment underscores the power of light in structuring animal behavior: Even when 'night' lasts for just 4 minutes, robust behavioral changes ensue." -> "Variation in species response also informs us about variation in sensitivity to light, with implications for the impacts of artificial light."

MODE: Learning compositional representations of complex systems with Mixtures Of Dynamical Experts
Nathan Quiblier, Roy Friedman, Matthew Ricci
https://arxiv.org/abs/2510.09594 …

MODE: Learning compositional representations of complex systems with Mixtures Of Dynamical Experts
Dynamical systems in the life sciences are often composed of complex mixtures of overlapping behavioral regimes. Cellular subpopulations may shift from cycling to equilibrium dynamics or branch towards different developmental fates. The transitions between these regimes can appear noisy and irregular, posing a serious challenge to traditional, flow-based modeling techniques which assume locally smooth dynamics. To address this challenge, we propose MODE (Mixture Of Dynamical Experts), a graphic…

Can GenAI Improve Academic Performance? Evidence from the Social and Behavioral Sciences
Dragan Filimonovic, Christian Rutzer, Conny Wunsch
https://arxiv.org/abs/2510.02408 http…

Can GenAI Improve Academic Performance? Evidence from the Social and Behavioral Sciences
This paper estimates the effect of Generative AI (GenAI) adoption on scientific productivity and quality in the social and behavioral sciences. Using matched author-level panel data and a difference-in-differences design, we find that GenAI adoption is associated with sizable increases in research productivity, measured by the number of published papers. It also leads to moderate gains in publication quality, based on journal impact factors. These effects are most pronounced among early-career …

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. …

A behavioral reinvestigation of the effect of long ties on social contagions
Luca Lazzaro, Manuel S. Mariani, Ren\'e Algesheimer, Radu Tanase
https://arxiv.org/abs/2510.04785

A behavioral reinvestigation of the effect of long ties on social contagions
Faced with uncertainty in decision making, individuals often turn to their social networks to inform their decisions. In consequence, these networks become central to how new products and behaviors spread. A key structural feature of networks is the presence of long ties, which connect individuals who share few mutual contacts. Under what conditions do long ties facilitate or hinder diffusion? The literature provides conflicting results, largely due to differing assumptions about individual dec…

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Suyash Fulay, Jocelyn Zhu, Michiel Bakker
https://arxiv.org/abs/2510.12689 ht…

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Large language models (LLMs) have shown promising accuracy in predicting survey responses and policy preferences, which has increased interest in their potential to represent human interests in various domains. Most existing research has focused on behavioral cloning, effectively evaluating how well models reproduce individuals' expressed preferences. Drawing on theories of political representation, we highlight an underexplored design trade-off: whether AI systems should act as delegates, mirr…

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…

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…

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…

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…

HatLLM: Hierarchical Attention Masking for Enhanced Collaborative Modeling in LLM-based Recommendation
Yu Cui, Feng Liu, Jiawei Chen, Canghong Jin, Xingyu Lou, Changwang Zhang, Jun Wang, Yuegang Sun, Can Wang
https://arxiv.org/abs/2510.10955

HatLLM: Hierarchical Attention Masking for Enhanced Collaborative Modeling in LLM-based Recommendation
Recent years have witnessed a surge of research on leveraging large language models (LLMs) for sequential recommendation. LLMs have demonstrated remarkable potential in inferring users' nuanced preferences through fine-grained semantic reasoning. However, they also exhibit a notable limitation in effectively modeling collaborative signals, i.e., behavioral correlations inherent in users' historical interactions. Our empirical analysis further reveals that the attention mechanisms in LLMs tend t…

Agent-Based Simulation of a Financial Market with Large Language Models
Ryuji Hashimoto, Takehiro Takayanagi, Masahiro Suzuki, Kiyoshi Izumi
https://arxiv.org/abs/2510.12189 htt…

Agent-Based Simulation of a Financial Market with Large Language Models
In real-world stock markets, certain chart patterns -- such as price declines near historical highs -- cannot be fully explained by fundamentals alone. These phenomena suggest the presence of path dependence in price formation, where investor decisions are influenced not only by current market conditions but also by the trajectory of prices leading up to the present. Path dependence has drawn attention in behavioral finance as a key mechanism behind such anomalies. One plausible driver of path …

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Blessing Agyei Kyem, Neema Jakisa Owor, Andrews Danyo, Joshua Kofi Asamoah, Eugene Denteh, Tanner Muturi, Anthony Dontoh, Yaw Adu-Gyamfi, Armstrong Aboah
https://arxiv.org/abs/2510.11907

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Traffic safety analysis requires complex video understanding to capture fine-grained behavioral patterns and generate comprehensive descriptions for accident prevention. In this work, we present a unique dual-model framework that strategically utilizes the complementary strengths of VideoLLaMA and Qwen2.5-VL through task-specific optimization to address this issue. The core insight behind our approach is that separating training for captioning and visual question answering (VQA) tasks minimizes…

Replaced article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- Agent-Based Modelling for Real-World Stock Markets under Behavioral Economic Principles
He, Zhu, Lu, Xu, Liu, Liu, Lin, Chan, Bian

Modelling Intra-driver Behavioral Adaptation through Risk Sensitivity and Regime Transitions: A Task-difficulty Car-following Model
Mohammad Tamim Kashifi
https://arxiv.org/abs/2510.09886

Modelling Intra-driver Behavioral Adaptation through Risk Sensitivity and Regime Transitions: A Task-difficulty Car-following Model
Over the past decade, there has been a growing trend toward integrating human factors (HF) into traffic flow models to better understand the complexities of human behavior and its impact on traffic dynamics. This research seeks to advance this trend by bridging the gap between traditional car-following models and the inherent variability of human driving behavior. By incorporating these elements, our models provide valuable insights into how driver behavior adaptation and risk-taking influence …

FM-IRL: Flow-Matching for Reward Modeling and Policy Regularization in Reinforcement Learning
Zhenglin Wan, Jingxuan Wu, Xingrui Yu, Chubin Zhang, Mingcong Lei, Bo An, Ivor Tsang
https://arxiv.org/abs/2510.09222

FM-IRL: Flow-Matching for Reward Modeling and Policy Regularization in Reinforcement Learning
Flow Matching (FM) has shown remarkable ability in modeling complex distributions and achieves strong performance in offline imitation learning for cloning expert behaviors. However, despite its behavioral cloning expressiveness, FM-based policies are inherently limited by their lack of environmental interaction and exploration. This leads to poor generalization in unseen scenarios beyond the expert demonstrations, underscoring the necessity of online interaction with environment. Unfortunately…

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…

Retrieving the structure of probabilistic sequences from EEG data during the goalkeeper game
P. R. Cabral-Passos, P. S. Azevedo, V. H. Moraes, B. L. Ramalho, A. Duarte, C. D. Vargas
https://arxiv.org/abs/2510.06344

Retrieving the structure of probabilistic sequences from EEG data during the goalkeeper game
This work draws on the conjecture that fingerprints of stochastic event sequences can be retrieved from electroencephalographic data (EEG) recorded during a behavioral task. To test this, we used the Goalkeeper Game (game.numec.prp.usp.br). Acting as a goalkeeper, the participant predicted each kick in a probabilistic sequence while EEG activity was recorded. At each trial, driven by a context tree, the kicker chose one of three options: left, center, or right. The goalkeeper then predicted the…

Robot Learning: A Tutorial
Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi
https://arxiv.org/abs/2510.12403 https://arxiv.…

Robot Learning: A Tutorial
Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning and Behavioral Cloning to generalist, language-conditione…

Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models
Qiguang Chen, Hanjing Li, Libo Qin, Dengyun Peng, Jinhao Liu, Jiangyi Wang, Chengyue Wu, Xie Chen, Yantao Du, Wanxiang Che
https://arxiv.org/abs/2510.09544

Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models
Recently, Diffusion Large Language Models (DLLMs) have offered high throughput and effective sequential reasoning, making them a competitive alternative to autoregressive LLMs (ALLMs). However, parallel decoding, which enables simultaneous token updates, conflicts with the causal order often required for rigorous reasoning. We first identify this conflict as the core Parallel-Sequential Contradiction (PSC). Behavioral analyses in both simple and complex reasoning tasks show that DLLMs exhibit g…

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…

A Semantic Framework for Patient Digital Twins in Chronic Care
Amal Elgammal, Bernd J. Kr\"amer, Michael P. Papazoglou, Mira Raheem
https://arxiv.org/abs/2510.09134 https:/…

A Semantic Framework for Patient Digital Twins in Chronic Care
Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an ontology-driven in silico patient framework that integrates physiological, psychosocial, behavioral, and genomic information into a coherent…

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…

Beyond Revenue and Welfare: Counterfactual Analysis of Spectrum Auctions with Application to Canada's 3800MHz Allocation
Sara Jalili Shani, Kris Joseph, Michael B. McNally, James R. Wright
https://arxiv.org/abs/2512.08106 https://arxiv.org/pdf/2512.08106 https://arxiv.org/html/2512.08106
arXiv:2512.08106v1 Announce Type: new
Abstract: Spectrum auctions are the primary mechanism through which governments allocate scarce radio frequencies, with outcomes that shape competition, coverage, and innovation in telecommunications markets. While traditional models of spectrum auctions often rely on strong equilibrium assumptions, we take a more parsimonious approach by modeling bidders as myopic and straightforward: in each round, firms simply demand the bundle that maximizes their utility given current prices. Despite its simplicity, this model proves effective in predicting the outcomes of Canada's 2023 auction of 3800 MHz spectrum licenses. Using detailed round-by-round bidding data, we estimate bidders' valuations through a linear programming framework and validate that our model reproduces key features of the observed allocation and price evolution. We then use these estimated valuations to simulate a counterfactual auction under an alternative mechanism that incentivizes deployment in rural and remote regions, aligning with one of the key objectives set out in the Canadian Telecommunications Act. The results show that the proposed mechanism substantially improves population coverage in underserved areas. These findings demonstrate that a behavioral model with minimal assumptions is sufficient to generate reliable counterfactual predictions, making it a practical tool for policymakers to evaluate how alternative auction designs may influence future outcomes. In particular, our study demonstrates a method for counterfactual mechanism design, providing a framework to evaluate how alternative auction rules could advance policy goals such as equitable deployment across Canada.
toXiv_bot_toot

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…

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
Alin Fan, Hanqing Li, Sihan Lu, Jingsong Yuan, Jiandong Zhang
https://arxiv.org/abs/2510.11066

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
Modern industrial recommendation systems improve recommendation performance by integrating multimodal representations from pre-trained models into ID-based Click-Through Rate (CTR) prediction frameworks. However, existing approaches typically adopt modality-centric modeling strategies that process ID-based and multimodal embeddings independently, failing to capture fine-grained interactions between content semantics and behavioral signals. In this paper, we propose Decoupled Multimodal Fusion (…

Modeling information acquisition via f-divergence and duality
Alex Bloedel, Tommaso Denti, Luciano Pomatto
https://arxiv.org/abs/2510.03482 https://arxiv.o…

Modeling information acquisition via f-divergence and duality
We introduce a new cost function over experiments, f-information, based on the theory of multivariate statistical divergences, that generalizes Sims's classic model of rational inattention as well as the class of posterior-separable cost functions. We characterize its behavioral predictions by deriving optimality conditions that extend those of Matejka and McKay (2015) and Caplin, Dean, and Leahy (2019) beyond mutual information. Using these tools, we study the implications of f-information in …

Gauging the Competition: Understanding Social Comparison and Anxiety through Eye-tracking in Virtual Reality Group Interview
Shi-Ting Ni, Kairong Fang, Yuyang Wang, Pan Hui
https://arxiv.org/abs/2510.12590

Gauging the Competition: Understanding Social Comparison and Anxiety through Eye-tracking in Virtual Reality Group Interview
Virtual Reality (VR) is a promising tool for interview training, yet the psychological dynamics of group interviews, such as social comparison, remain underexplored. We investigate this phenomenon by developing an immersive VR group interview system and conducting an eye-tracking study with 73 participants. We manipulated peer performance using ambiguous behavioral cues (e.g., hand-raising) and objective information (public test scores) to measure their effect on participants' attention and sel…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/6]:
- An AI-Based Behavioral Health Safety Filter and Dataset for Identifying Mental Health Crises in T...
Nelson, Wong, Silvestrini, Shin, Robinson, Lee, Yang, Torous, Trister

Dream to Recall: Imagination-Guided Experience Retrieval for Memory-Persistent Vision-and-Language Navigation
Yunzhe Xu, Yiyuan Pan, Zhe Liu
https://arxiv.org/abs/2510.08553 htt…

Dream to Recall: Imagination-Guided Experience Retrieval for Memory-Persistent Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) requires agents to follow natural language instructions through environments, with memory-persistent variants demanding progressive improvement through accumulated experience. Existing approaches for memory-persistent VLN face critical limitations: they lack effective memory access mechanisms, instead relying on entire memory incorporation or fixed-horizon lookup, and predominantly store only environmental observations while neglecting navigation behavioral …

Reliability of Single-Level Equality-Constrained Inverse Optimal Control
Filip Be\v{c}anovi\'c (University of Belgrade - School of Electrical Engineering), Kosta Jovanovi\'c (University of Belgrade - School of Electrical Engineering), Vincent Bonnet (LAAS-CNRS)
https://arxiv.org/abs/2510.08406

Reliability of Single-Level Equality-Constrained Inverse Optimal Control
Inverse optimal control (IOC) allows the retrieval of optimal cost function weights, or behavioral parameters, from human motion. The literature on IOC uses methods that are either based on a slow bilevel process or a fast but noise-sensitive minimization of optimality condition violation. Assuming equality-constrained optimal control models of human motion, this article presents a faster but robust approach to solving IOC using a single-level reformulation of the bilevel method and yields equi…

Replaced article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Behavioral alignment in social networks
Yu Xia, Alex McAvoy, Qi Su
https://…

ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Dakai Zhai, Jiong Gao, Boya Du, Junwei Xu, Qijie Shen, Jialin Zhu, Yuning Jiang
https://arxiv.org/abs/2510.09393

ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems.Existing approaches face three critical limitations: (i) reliance on noisy and unreliable behavioral signals; (ii) insufficient user-level information due to the lack of diverse interaction data; and (iii) a systemic training bias toward high-activity users that overshadows the needs of low-activity users.To address these challenges, we propose ChoirR…

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 …

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
Basil Abdullah AL-Zahrani

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

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- AdaptAuth: Multi-Layered Behavioral and Credential Analysis for a Secure and Adaptive Authenticat...
Tonmoy Ghosh

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Behavioral alignment in social networks
Yu Xia, Alex McAvoy, Qi Su

Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)
Moonkyung Ryu, Chih-Wei Hsu, Yinlam Chow, Mohammad Ghavamzadeh, Craig Boutilier
https://arxiv.org/abs/2510.02331

Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)
While language models (LMs) offer great potential for conversational recommender systems (CRSs), the paucity of public CRS data makes fine-tuning LMs for CRSs challenging. In response, LMs as user simulators qua data generators can be used to train LM-based CRSs, but often lack behavioral consistency, generating utterance sequences inconsistent with those of any real user. To address this, we develop a methodology for generating natural dialogues that are consistent with a user's underlying sta…

Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavior in primates
Tian Sang, Yichun Huang, Fangwei Zhong, Miao Wang, Shiqi Yu, Jiahui Li, Yuanjing Feng, Yizhou Wang, Kwok Sze Chai, Ravi S. Menon, Meiyun Wang, Fang Fang, Zheng Wang
https://arxiv.org/abs/2512.09761 https://arxiv.org/pdf/2512.09761 https://arxiv.org/html/2512.09761
arXiv:2512.09761v1 Announce Type: new
Abstract: Reinforcement learning (RL) enables adaptive behavior across species via reward prediction errors (RPEs), but the neural origins of species-specific adaptability remain unknown. Integrating RL modeling, transcriptomics, and neuroimaging during reversal learning, we discovered convergent RPE signatures - shared monoaminergic/synaptic gene upregulation and neuroanatomical representations, yet humans outperformed macaques behaviorally. Single-trial decoding showed RPEs guided choices similarly in both species, but humans disproportionately recruited dorsal anterior cingulate (dACC) and dorsolateral prefrontal cortex (dlPFC). Cross-species alignment uncovered that macaque prefrontal circuits encode human-like optimal RPEs yet fail to translate them into action. Adaptability scaled not with RPE encoding fidelity, but with the areal extent of dACC/dlPFC recruitment governing RPE-to-action transformation. These findings resolve an evolutionary puzzle: behavioral performance gaps arise from executive cortical readout efficiency, not encoding capacity.
toXiv_bot_toot

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/8]:
- A Human Behavioral Baseline for Collective Governance in Software Projects
Mobina Noori, Mahasweta Chakraborti, Amy X Zhang, Seth Frey

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…

AgentZero : Modeling Fear-Based Behavior
Vrinda Malhotra, Jiaman Li, Nandini Pisupati
https://arxiv.org/abs/2510.05185 https://arxiv.org/pdf/2510.05185

AgentZero++: Modeling Fear-Based Behavior
We present AgentZero++, an agent-based model that integrates cognitive, emotional, and social mechanisms to simulate decentralized collective violence in spatially distributed systems. Building on Epstein's Agent\_Zero framework, we extend the original model with eight behavioral enhancements: age-based impulse control; memory-based risk estimation; affect-cognition coupling; endogenous destructive radius; fight-or-flight dynamics; affective homophily; retaliatory damage; and multi-agent coordi…

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…

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 …

A data fusion approach for mobility hub impact assessment and location selection: integrating hub usage data into a large-scale mode choice model
Xiyuan Ren, Joseph Y. J. Chow
https://arxiv.org/abs/2510.08366

A data fusion approach for mobility hub impact assessment and location selection: integrating hub usage data into a large-scale mode choice model
As cities grapple with traffic congestion and service inequities, mobility hubs offer a scalable solution to align increasing travel demand with sustainability goals. However, evaluating their impacts remains challenging due to the lack of behavioral models that integrate large-scale travel patterns with real-world hub usage. This study presents a novel data fusion approach that incorporates observed mobility hub usage into a mode choice model estimated with synthetic trip data. We identify tri…

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…

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…

Evidence of Cognitive Biases in Capture-the-Flag Cybersecurity Competitions
Carolina Carreira, Anu Aggarwal, Alejandro Cuevas, Maria Jos\'e Ferreira, Hanan Hibshi, Cleotilde Gonzalez
https://arxiv.org/abs/2510.05771

Evidence of Cognitive Biases in Capture-the-Flag Cybersecurity Competitions
Understanding how cognitive biases influence adversarial decision-making is essential for developing effective cyber defenses. Capture-the-Flag (CTF) competitions provide an ecologically valid testbed to study attacker behavior at scale, simulating real-world intrusion scenarios under pressure. We analyze over 500,000 submission logs from picoCTF, a large educational CTF platform, to identify behavioral signatures of cognitive biases with defensive implications. Focusing on availability bias an…

Can Risk-taking AI-Assistants suitably represent entities
Ali Mazyaki, Mohammad Naghizadeh, Samaneh Ranjkhah Zonouzaghi, Amirhossein Farshi Sotoudeh
https://arxiv.org/abs/2510.08114

Can Risk-taking AI-Assistants suitably represent entities
Responsible AI demands systems whose behavioral tendencies can be effectively measured, audited, and adjusted to prevent inadvertently nudging users toward risky decisions or embedding hidden biases in risk aversion. As language models (LMs) are increasingly incorporated into AI-driven decision support systems, understanding their risk behaviors is crucial for their responsible deployment. This study investigates the manipulability of risk aversion (MoRA) in LMs, examining their ability to repl…

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…

Deep Generative Model for Human Mobility Behavior
Ye Hong, Yatao Zhang, Konrad Schindler, Martin Raubal
https://arxiv.org/abs/2510.06473 https://arxiv.org/…

Deep Generative Model for Human Mobility Behavior
Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individual mobility remains challenging because of its complex, context-dependent, and exploratory nature. Here, we present MobilityGen, a deep generative model that produces realistic mobility trajectories spanning days to weeks at large spatial scales. By linking behavioral attributes with environmental context, MobilityGe…

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

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Deep Learning Based Approach to Enhanced Recognition of Emotions and Behavioral Patterns of Autis...
Nelaka K. A. R, Peiris M. K. V, Liyanage R. P. B

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…

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…

A Hybrid CAPTCHA Combining Generative AI with Keystroke Dynamics for Enhanced Bot Detection
Ayda Aghaei Nia
https://arxiv.org/abs/2510.02374 https://arxiv.…

A Hybrid CAPTCHA Combining Generative AI with Keystroke Dynamics for Enhanced Bot Detection
Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs) are a foundational component of web security, yet traditional implementations suffer from a trade-off between usability and resilience against AI-powered bots. This paper introduces a novel hybrid CAPTCHA system that synergizes the cognitive challenges posed by Large Language Models (LLMs) with the behavioral biometric analysis of keystroke dynamics. Our approach generates dynamic, unpredictable questions tha…

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

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

When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?
Xiaoyun Yin, Elmira Zahmat Doost, Shiwen Zhou, Garima Arya Yadav, Jamie C. Gorman
https://arxiv.org/abs/2510.02660

When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?
When researchers claim AI systems possess ToM or mental models, they are fundamentally dis- cussing behavioral predictions and bias corrections rather than genuine mental states. This position paper argues that the current discourse conflates sophisticated pattern matching with authentic cog- nition, missing a crucial distinction between simulation and experience. While recent studies show LLMs achieving human-level performance on ToM laboratory tasks, these results are based only on behavioral…

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…

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

Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study
Hasan Mahmuda, Najmul Islam, Satish Krishnan
https://arxiv.org/abs/2510.02153

Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study
Robo-advisors (RAs) are cost-effective, bias-resistant alternatives to human financial advisors, yet adoption remains limited. While prior research has examined user interactions with RAs, less is known about how individuals interpret RA roles and integrate their advice into decision-making. To address this gap, this study employs a multiphase mixed methods design integrating a behavioral experiment (N = 334), thematic analysis, and follow-up quantitative testing. Findings suggest that people t…

"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…