Tootfinder

The Agent Behavior: Model, Governance and Challenges in the AI Digital Age
Qiang Zhang, Pei Yan, Yijia Xu, Chuanpo Fu, Yong Fang, Yang Liu
https://arxiv.org/abs/2508.14415 https…

The Agent Behavior: Model, Governance and Challenges in the AI Digital Age
Advancements in AI have led to agents in networked environments increasingly mirroring human behavior, thereby blurring the boundary between artificial and human actors in specific contexts. This shift brings about significant challenges in trust, responsibility, ethics, security and etc. The difficulty in supervising of agent behaviors may lead to issues such as data contamination and unclear accountability. To address these challenges, this paper proposes the "Network Behavior Lifecycle" mode…

Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Yixin Chen, Ying Xiong, Shangyu Wu, Yufei Cui, Xue Liu, Nan Guan, Chun Jason Xue
https://arxiv.org/abs/2508.14323

Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Tool-augmented large language models (LLMs) leverage external functions to extend their capabilities, but inaccurate function calls can lead to inefficiencies and increased costs.Existing methods address this challenge by fine-tuning LLMs or using demonstration-based prompting, yet they often suffer from high training overhead and fail to account for inconsistent demonstration samples, which misguide the model's invocation behavior. In this paper, we trained a behavior-aligned retriever (BAR), …

MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation
Chengcheng Guo, Junda She, Kuo Cai, Shiyao Wang, Qigen Hu, Qiang Luo, Kun Gai, Guorui Zhou
https://arxiv.org/abs/2508.14515

MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation
Large-scale industrial recommendation systems typically employ a two-stage paradigm of retrieval and ranking to handle huge amounts of information. Recent research focuses on improving the performance of retrieval model. A promising way is to introduce extensive information about users and items. On one hand, lifelong sequential behavior is valuable. Existing lifelong behavior modeling methods in ranking stage focus on the interaction of lifelong behavior and candidate items from retrieval stag…

Quantum chaos and semiclassical behavior in mushroom billiards I: Spectral statistics
Matic Orel, \v{C}rt Lozej, Marko Robnik, Hua Yan
https://arxiv.org/abs/2507.13823

Quantum chaos and semiclassical behavior in mushroom billiards I: Spectral statistics
We study the aspects of quantum chaos in mushroom billiards introduced by Bunimovich. This family of billiards classically has the property of mixed phase space with precisely one entirely regular and one fully chaotic (ergodic) component, whose size depends on the width w of the stem, and has two limiting geometries, namely the circle (as the integrable system) and stadium (as the fully chaotic system). Therefore, this one-parameter system is ideal to study the semiclassical behavior of the qu…

How Much Can a Behavior-Preserving Changeset Be Decomposed into Refactoring Operations?
Kota Someya, Lei Chen, Michael J. Decker, Shinpei Hayashi
https://arxiv.org/abs/2508.11993

How Much Can a Behavior-Preserving Changeset Be Decomposed into Refactoring Operations?
Developers sometimes mix behavior-preserving modifications, such as refactorings, with behavior-altering modifications, such as feature additions. Several approaches have been proposed to support understanding such modifications by separating them into those two parts. Such refactoring-aware approaches are expected to be particularly effective when the behavior-preserving parts can be decomposed into a sequence of more primitive behavior-preserving operations, such as refactorings, but this has…

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation
Markus Buchholz, Ignacio Carlucho, Michele Grimaldi, Maria Koskinopoulou, Yvan R. Petillot
https://arxiv.org/abs/2507.14099

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation
Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments. Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings. In this paper, we introduce an Adaptive Heuristic Motion Planner framework that integrates a Heuristic Motion Space (HMS) with Bayesian Networks to enhance motion planning for autonomous underwater manipulation. Our appr…

OpenAI and Apollo Research trained o3 and o4-mini versions to not engage in "scheming", or secretly pursuing undesirable goals, reducing "covert actions" ~30X (Radhika Rajkumar/ZDNET)
https://www.zdnet.com/article/ai-models-kn

AI models know when they're being tested - and change their behavior, research shows
OpenAI and Apollo Research tried to stop models from lying - and discovered something else altogether.

Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter
Ashfaq Ali Shafin, Bogdan Carbunar
https://arxiv.org/abs/2507.13636

Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter
This paper investigates inauthentic duplication on social media, where multiple accounts share identical misinformation tweets. Leveraging a dataset of misinformation verified by AltNews, an Indian fact-checking organization, we analyze over 12 million posts from 5,493 accounts known to have duplicated such content. Contrary to common assumptions that bots are primarily responsible for spreading false information, fewer than 1\% of these accounts exhibit bot-like behavior. We present TweeXster,…

Oscillatory Behavior of Linear Nonautonomous Advanced and Delayed Impulsive Differential Equations with Discontinuous Deviating Arguments via Difference Equations
Ricardo Torres Naranjo, Eugenio Trucco Vera
https://arxiv.org/abs/2507.13559

Oscillatory Behavior of Linear Nonautonomous Advanced and Delayed Impulsive Differential Equations with Discontinuous Deviating Arguments via Difference Equations
We establish sufficient conditions to guarantee the oscillatory and non-oscillatory behavior of solutions to nonautonomous advanced and delayed linear differential equations with piecewise constant arguments: \[ x'(t) = a(t)x(t) + b(t)x([t \pm k]), \] where $k \in \mathbb{N}$, $k \geq 1$, in both impulsive and non-impulsive cases (DEPCA and IDEPCA). Due to the hybrid nature of these systems, our approach draws on results from the theory of advanced and delayed linear difference equations. The a…

PBPU Elastomer Network Architecture Determination via Corresponding States Analysis of Mechanical Behavior
Sushanta Das (Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India Defence Research,Development Organization, New Delhi, India), Hari Ramakrishna Sudhakar (Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India), Hemant Nanavati (Department of Chemical Engineering, Indian Institute of T…

PBPU Elastomer Network Architecture Determination via Corresponding States Analysis of Mechanical Behavior
In this work we examine the effect of R=[NCO]/[OH] in the R=<1 regime, on the resultant structural topology of polybutadiene polyurethane (PBPU) elastomer networks based on hydroxy-terminated polybutadiene (HTPB). We employ stress-elongation behavior and its modeling, as a tool. We examine this property via a combination of our model for the finite chain phantom networks incorporating the HTPB structural information, with the slip-tube model from the literature, suitably modified phenomenologic…

Boundary behavior of functions in the Schur-Agler class of the polydisc
Jim Agler, Connor Evans, Zinaida Lykova, N. J. Young
https://arxiv.org/abs/2508.13742 https://

Boundary behavior of functions in the Schur-Agler class of the polydisc
We describe a generalization of the notion of a Hilbert space model of a function $φ$ in the Schur-Agler class of the polydisc. This generalization is well adapted to the investigation of boundary behavior of $φ$ at a mild singularity $τ$ on the $d$-torus. We prove the existence of a generalized model with an enhanced continuity property at such a singularity $τ$. We use this result to prove the directional differentiability of a function $φ$ in the Schur-Agler class at a singular point on…

ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling
Wenji Zhou, Yuhang Zheng, Yinfu Feng, Yunan Ye, Rong Xiao, Long Chen, Xiaosong Yang, Jun Xiao
https://arxiv.org/abs/2508.13567

ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling
Long-term user behavior sequences are a goldmine for businesses to explore users' interests to improve Click-Through Rate. However, it is very challenging to accurately capture users' long-term interests from their long-term behavior sequences and give quick responses from the online serving systems. To meet such requirements, existing methods "inadvertently" destroy two basic requirements in long-term sequence modeling: R1) make full use of the entire sequence to keep the information as much a…

Central Limit Behavior at the Edge of Chaos in the z-Logistic Map
Abbas Ali Saberi, Ugur Tirnakli, Constantino Tsallis
https://arxiv.org/abs/2508.13170 https://

Central Limit Behavior at the Edge of Chaos in the z-Logistic Map
We focus on the FeigenbaumCoulletTresser point of the dissipative one-dimensional z logistic map. We show that sums of iterates converge to q Gaussian distributions, which optimize the nonadditive entropic functional Sq under simple constraints. We derive a closedform prediction for the entropic index, and validate it numerically via data collapse for typical z values. The formula captures how the limiting law depends on the nonlinearity order and implies finite variance for z larger than 2 and…

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning
Yang Wang, Yaxin Zhao, Xinyu Jiao, Sihan Xu, Xiangrui Cai, Ying Zhang, Xiaojie Yuan
https://arxiv.org/abs/2508.11472

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning
Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high false positive rates and miss rates due to the inherent ambiguity between normal and anomalous behaviors. In this work, we instead introduce weak labels of behavior sequences, which have lower annotatio…

Former NFL defensive star Everson Griffen removed from Delta flight after 'unruly behavior'

https://www.cbssports.com/nfl/news/former-

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
Yuqi Luan
https://arxiv.org/abs/2508.14656 https://arxiv.org…

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
This study proposes a behaviorally-informed multi-factor stock selection framework that integrates short-cycle technical alpha signals with deep learning. We design a dual-task multilayer perceptron (MLP) that jointly predicts five-day future returns and directional price movements, thereby capturing nonlinear market behaviors such as volume-price divergence, momentum-driven herding, and bottom reversals. The model is trained on 40 carefully constructed factors derived from price-volume pattern…

The asymptotic behavior of simple eigenvalues of particle-in-well systems
Peter Hintz, Aaron Moser
https://arxiv.org/abs/2508.13545 https://arxiv.org/pdf/2…

The asymptotic behavior of simple eigenvalues of particle-in-well systems
The particle in a well in dimension one is a classical problem in quantum mechanics. We study higher-dimensional analogues of the problem, where the well is a smooth domain in $\mathbb{R}^d$. We show that simple eigenvalues and eigenfunctions of the corresponding Schrödinger operator depend smoothly on the square root $h$ of the inverse depth of the well and provide an explicit first-order expansion of the eigenvalues at $h = 0$. Our proof consists of two steps. In the first step, we constru…

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
Akira Matsui, Takashi Teramoto, Eiji Motohashi, Hiroyuki Tsurumi
https://arxiv.org/abs/2509.14508

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
The digital economy implements complex incentive systems to retain users through point redemption. Understanding user behavior in such complex incentive structures presents a fundamental challenge, especially in estimating the value of these digital assets against traditional money. This study tackles this question by analyzing large-scale, real-world transaction data from a popular personal finance application that captures both monetary spending and point-based transactions across Japan's dee…

Topologically trivial semiconducting behavior and polaronic effects in antiferromagnetic EuZn$_2$As$_2$ and EuCd$_2$Sb$_2$
Divyanshi Sar, Mingda Gong, Tetiana Romanova, Luka Khizanishvili, Hannah Park, Dariusz Kaczorowski, Wei-Cheng Lee, Pegor Aynajian
https://arxiv.org/abs/2508.14205

Topologically trivial semiconducting behavior and polaronic effects in antiferromagnetic EuZn$_2$As$_2$ and EuCd$_2$Sb$_2$
The Eu-based EuA$_2$X$_2$ (A = Zn, Cd, In, Sn; X = P, As, Sb) family of compounds has recently attracted significant attention as a promising platform for exploring magnetic topological materials, with several members either predicted or reported to exhibit nontrivial topological properties. We investigate the previously reported topological semimetals, EuZn$_2$As$_2$ and EuCd$_2$Sb$_2$, using scanning tunneling microscopy and spectroscopy, complemented by various first-principles computational…

Alignment behavior of 2D diopsides (d-silicates) under the influence of an AC electric field
Himakshi Mishra, Bruno Ipaves, Raphael Benjamim de Oliveira, Marcelo Lopes Pereira Junior, Raphael Matozo Tromer, e Douglas Soares Galvao, Chandra Shekar Tiwary
https://arxiv.org/abs/2507.13962

Alignment behavior of 2D diopsides (d-silicates) under the influence of an AC electric field
Controlling the alignment of two dimensional (2D) materials is crucial for optimizing their electronic and mechanical properties in next generation devices. This study explores how electric fields can manipulate the orientation of 2D diopside (CaMgSi2O6) flakes, a flexible silicate material, through a phenomenon called flexoelectricity, where applied voltage generates mechanical strain. We exfoliated diopside crystals into ultrathin flakes, placed them on microelectrodes, and used AC electric f…

Benford behavior resulting from stick and box fragmentation processes
Bruce Fang, Steven J. Miller
https://arxiv.org/abs/2508.12915 https://arxiv.org/pdf/2…

Benford behavior resulting from stick and box fragmentation processes
Benford's law is the statement that in many real world data sets, the probability of having digit $d$ in base $B$, where $1 \leq d \leq B$, as the first digit is \log_{B}\!\left(\frac{d+1}{d}\right). We sometimes refer to this as weak Benford behavior, and we say that a data set exhibits strong Benford behavior in base $B$ if the probability of having significand at most s, where $1 \leq s < B$, is \log_{B}\!\left(s\right). We examine Benford behaviors in two different probabilistic models: sti…

Bounds of Shannon entropy and Extropy and their application in exploring the extreme value behavior of a large set of data
Konstantinos Zografos
https://arxiv.org/abs/2507.13656

Bounds of Shannon entropy and Extropy and their application in exploring the extreme value behavior of a large set of data
This paper derives bounds for two omnipresent information theoretic measures, the Shannon entropy and its complementary dual, the extropy. Based on a large size data set from a logconcave model, the said bounds are obtained for the entropy and the extropy of the distribution of the largest order statistic and the respective normalized sequence, in the extreme value theory setting. A characterization of the exponential distribution is provided as the model that maximizes the Shannon entropy and …

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…

Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
Filippo Lazzati, Alberto Maria Metelli
https://arxiv.org/abs/2509.12010 https://

Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
We study the problem of generalizing an expert agent's behavior, provided through demonstrations, to new environments and/or additional constraints. Inverse Reinforcement Learning (IRL) offers a promising solution by seeking to recover the expert's underlying reward function, which, if used for planning in the new settings, would reproduce the desired behavior. However, IRL is inherently ill-posed: multiple reward functions, forming the so-called feasible set, can explain the same observed beha…

I see so many young kids now just constantly staring and scrolling through reels, tiktok, youtube clips when we are out and about. Everywhere. It's disgusting and extremely sad at the same time. Initially I was wondering how the hell parents are allowing this, but when you notice grown people just doing the same thing. It's insane how that has become a majority, not a minority, behavior.

A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset
Haiyu Yang, Enhong Liu, Jennifer Sun, Sumit Sharma, Meike van Leerdam, Sebastien Franceschini, Puchun Niu, Miel Hostens
https://arxiv.org/abs/2509.12047

A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset
Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motio…

Adaptive Control with Set-Point Tracking and Linear-like Closed-loop Behavior
Mohamad T. Shahab
https://arxiv.org/abs/2508.12225 https://arxiv.org/pdf/2508…

Adaptive Control with Set-Point Tracking and Linear-like Closed-loop Behavior
In this paper, we consider the problem of set-point tracking for a discrete-time plant with unknown plant parameters belonging to a convex and compact uncertainty set. We carry out parameter estimation for an associated auxiliary plant, and a pole-placement-based control law is employed. We prove that this adaptive controller provides desirable linear-like closed-loop behavior which guarantees a bound consisting of: exponential decay with respect to the initial condition, a linear-like convolut…

Assessing Pedestrian Behavior Around Autonomous Cleaning Robots in Public Spaces: Findings from a Field Observation
Maren Raab, Linda Miller, Zhe Zeng, Pascal Jansen, Martin Baumann, Johannes Kraus
https://arxiv.org/abs/2508.13699

Assessing Pedestrian Behavior Around Autonomous Cleaning Robots in Public Spaces: Findings from a Field Observation
As autonomous robots become more common in public spaces, spontaneous encounters with laypersons are more frequent. For this, robots need to be equipped with communication strategies that enhance momentary transparency and reduce the probability of critical situations. Adapting these robotic strategies requires consideration of robot movements, environmental conditions, and user characteristics and states. While numerous studies have investigated the impact of distraction on pedestrians' moveme…

Large Language Models are Highly Aligned with Human Ratings of Emotional Stimuli
Mattson Ogg, Chace Ashcraft, Ritwik Bose, Raphael Norman-Tenazas, Michael Wolmetz
https://arxiv.org/abs/2508.14214

Large Language Models are Highly Aligned with Human Ratings of Emotional Stimuli
Emotions exert an immense influence over human behavior and cognition in both commonplace and high-stress tasks. Discussions of whether or how to integrate large language models (LLMs) into everyday life (e.g., acting as proxies for, or interacting with, human agents), should be informed by an understanding of how these tools evaluate emotionally loaded stimuli or situations. A model's alignment with human behavior in these cases can inform the effectiveness of LLMs for certain roles or interac…

AdapTT: Functoriality for Dependent Type Casts
Arthur Adjedj, Meven Lennon-Bertrand, Thibaut Benjamin, Kenji Maillard
https://arxiv.org/abs/2507.13774 http…

AdapTT: Functoriality for Dependent Type Casts
The ability to cast values between related types is a leitmotiv of many flavors of dependent type theory, such as observational type theories, subtyping, or cast calculi for gradual typing. These casts all exhibit a common structural behavior that boils down to the pervasive functoriality of type formers. We propose and extensively study a type theory, called AdapTT, which makes systematic and precise this idea of functorial type formers, with respect to an abstract notion of adapters relating …

AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test
Sihun Baek, Zhehan Qu, Maria Gorlatova
https://arxiv.org/abs/2509.13468

AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test
Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user behavior in AR environments. To address this gap, we present AR-TMT, an AR adaptation of the Trail Making Test that spatially renders targets for sequential selection on the Magic Leap 2. We implemented distractions in three categories: top-down, bottom-up, and spatial distraction based on Wolfe's Guided Search model, and captured performance, ga…

I have been watching the TV show Blindspot on streaming, and really liking it. Just checked and so glad it was on NBC and not cbs (given cbs’ bad behavior) so I can still watch the rest of it without supporting cbs.
#USpol #tv

freshmen: Zeggelink's freshmen (1999)
Snapshots of friendships among freshmen majoring in Sociology at the University of Groningen, collected five times over the 1998-1999 year. The direction of an edge indicates that student i is friends with student j, and the edge weight gives their friendship level from 1 (best friend) to 5 (troubled relation). Metadata include gender, age, program type, and smoking behavior.
This network has 34 nodes and 1156 edges.
Tags: Social, O…

🦾 Robotic frames offer new insights into honeybee behavior and honey storage
#science

Robotic frames offer new insights into honeybee behavior and honey storage
EPFL researchers are developing robotic beehive frames that help locate honey stores inside of beehives over time, without relying on cameras. The aim is to develop new observation tools to study honeybee behavior that better fit the bees' natural way to occupy space compared to current methods.

A study published in 2021 presented cuttlefish with a new version of the "marshmallow test",
and the results showed there's more going on in their strange little brains than we ever suspected.
Their ability to learn, anticipate future rewards, and adapt their behavior, the researchers said,
may have evolved to give cuttlefish an edge in the cutthroat eat-or-be-eaten marine world they live

Cephalopods Passed a Cognitive Test Intended For Human Children
An eye-opening experiment on cephalopods reinforces why it is so important for us not to underestimate animal intelligence.

Learning from user's behaviour of some well-known congested traffic networks
Isolda Cardoso, Lucas Venturato, Jorgelina Walpen
https://arxiv.org/abs/2508.14804 https://

Learning from user's behaviour of some well-known congested traffic networks
We consider the problem of predicting users' behavior of a congested traffic network under an equilibrium condition, the traffic assignment problem. We propose a two-stage machine learning approach which couples a neural network with a fixed point algorithm, and we evaluate its performance along several classical congested traffic networks.

... an anecdote from IBM's Yorktown Heights Research Center. When a
programmer used his new computer terminal, all was fine when he was sitting
down, but he couldn't log in to the system when he was standing up. That
behavior was 100 percent repeatable: he could always log in when sitting and
never when standing.
Most of us just sit back and marvel at such a story; how could that terminal
know whether the poor guy was sitting or standing? Good debugg…

DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
Zhixin Wang, Tianyi Zhou, Liming Liu, Ao Li, Jiarui Hu, Dian Yang, Jinlong Hou, Siyuan Feng, Yuan Cheng, Yuan Qi
https://arxiv.org/abs/2507.13833

DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
Reinforcement learning (RL) has become the pivotal post-training technique for large language model. Effectively scaling reinforcement learning is now the key to unlocking advanced reasoning capabilities and ensuring safe, goal-aligned behavior in the most powerful LLMs. Mainstream frameworks usually employ a hybrid-controller architecture where a single-controller dispatches the overall execution logic and manages overall data transfer and the multi-controller executes distributed computation.…

Rotating Kinetic Gas Disk Morphology Surrounding a Schwarzschild Black Hole
Carlos Gabarrete, Roger Raudales
https://arxiv.org/abs/2508.14863 https://arxiv…

Rotating Kinetic Gas Disk Morphology Surrounding a Schwarzschild Black Hole
In this work, the behavior of a rotating relativistic kinetic gas surrounding a Schwarzschild black hole is studied. We are interested in the description and the analysis of the morphology of the resulting configurations for a kinetic gas cloud with and without total angular momentum, and we also compare the macroscopic observables with configurations of finite total mass. A collisionless gas in the Schwarzschild spacetime background is analyzed, considering models of the one-particle distribut…

Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing
Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro
https://arxiv.org/abs/2509.14335

Beyond Classification: Evaluating LLMs for Fine-Grained Automatic Malware Behavior Auditing
Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware does but also to substantiate such claims with evidence. This task is challenging, as adversarial intent is often hidden within complex, framework-heavy applications, making manual auditing slow and costly. Large Language Models (LLMs) could help address this gap, but their auditin…

Once-in-a-lifetime encounter models for neutrino media II: Quasi-steady states and miscidynamic flavor evolution
Anson Kost (UNM), Lucas Johns (LANL), Huaiyu Duan (UNM)
https://arxiv.org/abs/2508.14331

Once-in-a-lifetime encounter models for neutrino media II: Quasi-steady states and miscidynamic flavor evolution
We extended the once-in-a-lifetime encounter (OILE) model to stochastic interactions among neutrinos. As in the original OILE model, the new model reproduces the mean-field behavior of a dense neutrino gas for time $t\lesssim (μγ)^{-1}$, where $μ$ measures the strength of the mean-field neutrino self-interaction potential and is proportional to the neutrino density, and the dimensionless "impact parameter" $γ$ is a measure of the change in the flavor quantum state of a neutrino during inter…

Analog computation with transcriptional networks
David Doty, Mina Latifi, David Soloveichick
https://arxiv.org/abs/2508.14017 https://arxiv.org/pdf/2508.14…

Analog computation with transcriptional networks
Transcriptional networks represent one of the most extensively studied types of systems in synthetic biology. Although the completeness of transcriptional networks for digital logic is well-established, *analog* computation plays a crucial role in biological systems and offers significant potential for synthetic biology applications. While transcriptional circuits typically rely on cooperativity and highly non-linear behavior of transcription factors to regulate *production* of proteins, they a…

Multiresolution local smoothness detection in non-uniformly sampled multivariate signals
Sara Avesani, Gianluca Giacchi, Michael Multerer
https://arxiv.org/abs/2507.13480

Multiresolution local smoothness detection in non-uniformly sampled multivariate signals
Inspired by edge detection based on the decay behavior of wavelet coefficients, we introduce a (near) linear-time algorithm for detecting the local regularity in non-uniformly sampled multivariate signals. Our approach quantifies regularity within the framework of microlocal spaces introduced by Jaffard. The central tool in our analysis is the fast samplet transform, a distributional wavelet transform tailored to scattered data. We establish a connection between the decay of samplet coefficient…

Exotic Dark Matter and the DESI Anomaly
Matteo Braglia, Xingang Chen, Abraham Loeb
https://arxiv.org/abs/2507.13925 https://arxiv.org…

Exotic Dark Matter and the DESI Anomaly
Exotic dark matter (EDM) refers to a dark matter species whose equation of state deviates from zero at late times. This behavior enables it to model a variety of non-standard late-time cosmologies, offering alternatives to various dark energy (DE) models, especially when the DE sector violates the null energy condition. In this work, by fitting to CMB, BAO, and Supernovae (SNe) data and comparing models in a Bayesian approach, we show that simple models of exotic dark matter are statistically c…

War is an unconscionable horror. The illusions of "international law" and "rules of war" have lead us to believe that war can be clean, managed, and "civilized."
But wars are fought by humans and humans are messy. Humans are not well suited to following orderly rules. Humans respond to their environment. Humans in extraordinary situations can be extraordinarily vindictive and brutal. Sufficiently traumatized humans can act without a conscience, spreading trauma like an infection. If humans respond to their situation, then there can be no "civilized" war because war is itself an situation outside of the society. It is a place that promotes antisocial behavior and punishes pro-social behavior. War cannot be expected to follow "international law" because it is what fills the void created by the failure of "international law" (so long as we rely on nations).
To call for war is to inflict atrocities on civilians. It is to kill the parents and children who serve, and to destroy the combatants who survive. It is to infect both sides with a trauma that will spread if untreated, when soldiers come home or when they become mercenaries in other wars.
And yet... there are times when the brutality, the incompetence, the evil becomes so unbearable that no other option exists, when taking up arms is simply bringing symmetry to an existing asymmetric conflict. There are times when the worst possible thing is inescapable, though it can never be justified.
In this new era of war, in the scramble of conflict under the collapsing of the (poorly named) "Pax Americana," I hope that we, the people, can understand that war is not a tool to fulfill an objective. It is not part of a larger strategy. It is not an extension of deplomacy.
War is a failure.
While it may be the only way to deal with the irrational - the genocidal, the slaver, the dictator - it is still a failure. It is a failure to build a world in which these people can't control armies and economies, can't turn populations in to cults and bend nations to their will.
And we will continue to have such wars until we unite against those who would use as as pawns, who would control our lives and lead us to our deaths. We will have these wars until we unite, as one world, against those rulers. This is what I mean, and what a lot of other people mean, when we say, "No War, but Class War."

Efficient Bayesian analysis of kilonovae and GRB afterglows with fiesta
Hauke Koehn, Thibeau Wouters, Peter T. H. Pang, Mattia Bulla, Henrik Rose, Hannah Wichern, Tim Dietrich
https://arxiv.org/abs/2507.13807

Efficient Bayesian analysis of kilonovae and GRB afterglows with fiesta
Gamma-ray burst (GRB) afterglows and kilonovae (KNe) are electromagnetic transients that can accompany binary neutron star (BNS) mergers. Therefore, studying their emission processes is of general interest for constraining cosmological parameters or the behavior of ultra-dense matter. One common method to analyze electromagnetic data from BNS mergers is to sample a Bayesian posterior over the parameters of a physical model for the transient. However, sampling the posterior is computationally co…

Fractal Social Dynamics as a Driver of Consensus and Inequality
Airton Deppman
https://arxiv.org/abs/2507.13849 https://arxiv.org/pdf…

Fractal Social Dynamics as a Driver of Consensus and Inequality
Human social behavior is organized in stratified, hierarchical networks, with a support group with about 5 members, expanding proportionally at each layer up to a maximum of approximately 150 frequent interactions per individual. This is known as Social Brain Hypothesis, and its findings are supported by psychological and neurological evidence. The fractal network framework provides valuable insights into social phenomena such as the spread of fake news and the development of technology. This s…

Modeling oxygen-void interactions in uranium nitride
Mohamed AbdulHameed, Anton J. Schneider, Benjamin Beeler, Michael W. D. Cooper
https://arxiv.org/abs/2508.14329 https://

Modeling oxygen-void interactions in uranium nitride
Oxygen impurities in uranium nitride (UN) are reported to influence its swelling behavior under irradiation, yet the underlying mechanism remains unknown. In this work, we develop a first-principles model that quantifies the interaction of oxygen with voids and fission gas bubbles in UN, leading to a reduction in surface energy that can promote swelling. The analysis reveals that segregation of substitutional oxygen at surface nitrogen sites is the primary driver of surface energy reduction, $&…

Variations in Volatile-Driven Activity of Comet C/2017 K2 (PanSTARRS) Revealed by Long-Term Multi-Wavelength Observations
S. Hmiddouch, E. Jehin, M. Lippi, M. Vander Donckt, K. Aravind, D. Hutsem\'ekers, J. Manfroid, A. Jabiri, Y. Moulane, Z. Benkhaldoun
https://arxiv.org/abs/2507.13451

Variations in Volatile-Driven Activity of Comet C/2017 K2 (PanSTARRS) Revealed by Long-Term Multi-Wavelength Observations
Context. A comprehensive study of comets over a wide heliocentric distance range helps us understand the physical processes driving their activity and reveals compositional differences across dynamical groups. C/2017 K2 (PANSTARRS) is a Dynamically New Oort Cloud comet (DNC) that showed activity as far as 23.75 au and displayed a CO-rich coma at 6.72 au, making it a key object to investigate pre- and post-perihelion behavior. Aims. We aim to study the long-term activity evolution and chemical…

Survey-to-Behavior: Downstream Alignment of Human Values in LLMs via Survey Questions
Shangrui Nie, Florian Mai, David Kacz\'er, Charles Welch, Zhixue Zhao, Lucie Flek
https://arxiv.org/abs/2508.11414

Survey-to-Behavior: Downstream Alignment of Human Values in LLMs via Survey Questions
Large language models implicitly encode preferences over human values, yet steering them often requires large training data. In this work, we investigate a simple approach: Can we reliably modify a model's value system in downstream behavior by training it to answer value survey questions accordingly? We first construct value profiles of several open-source LLMs by asking them to rate a series of value-related descriptions spanning 20 distinct human values, which we use as a baseline for subseq…

VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization
Jailing Lin, Shu Jiang, Qingyuan Zeng, Zhenzhong Wang, Min Jiang
https://arxiv.org/abs/2508.13792

VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization
The intrinsic dynamics of an object governs its physical behavior in the real world, playing a critical role in enabling physically plausible interactive simulation with 3D assets. Existing methods have attempted to infer the intrinsic dynamics of objects from visual observations, but generally face two major challenges: one line of work relies on manually defined constitutive priors, making it difficult to generalize to complex scenarios; the other models intrinsic dynamics using neural networ…

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
SM Mazharul Islam, Manfred Huber
https://arxiv.org/abs/2508.13922 https://

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
A policy in deep reinforcement learning (RL), either deterministic or stochastic, is commonly parameterized as a Gaussian distribution alone, limiting the learned behavior to be unimodal. However, the nature of many practical decision-making problems favors a multimodal policy that facilitates robust exploration of the environment and thus to address learning challenges arising from sparse rewards, complex dynamics, or the need for strategic adaptation to varying contexts. This issue is exacerb…

An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents
Junjie Qi, Siqi Mao, Tianyi Tan
https://arxiv.org/abs/2508.14131

An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents
We propose an improved algorithm by identifying and encouraging cooperative behavior in multi-agent environments. First, we analyze the shortcomings of existing algorithms in addressing multi-agent reinforcement learning problems. Then, based on the existing algorithm MADDPG, we introduce a new parameter to increase the reward that an agent can obtain when cooperative behavior among agents is identified. Finally, we compare our improved algorithm with MADDPG in environments from PettingZoo. The…

xAI apologizes for Grok's "horrific behavior" when it wrote antisemitic posts on July 8, and blames "an update to a code path upstream of the Grok bot" (Anthony Ha/TechCrunch)
https://techcrunch.com/2025/07/12/xai-and-grok-apologize-for…

xAI and Grok apologize for ‘horrific behavior’ | TechCrunch
In a series of posts on X, the AI chatbot Grok apologized for what it admitted was “horrific behavior.” The posts appear to be an official statement from xAI, the Elon Musk-led company behind Grok, as opposed to an AI-generated explanation for Grok’s posts. (xAI recently acquired X, where Grok is prominently featured.)

Long time behavior of the Vlasov-Maxwell-Fokker-Planck system with a strong external magnetic field
Anh-Tuan Vu
https://arxiv.org/abs/2508.12231 https://ar…

Long time behavior of the Vlasov-Maxwell-Fokker-Planck system with a strong external magnetic field
The subject matter of this paper concerns the magnetic confinement of plasma. We investigate the long time behavior of the Vlasov-Maxwell-Fokker-Planck system under the effect of a strong external magnetic field. We first formally derive an asymptotically reduced model. In three space dimensions, a constraint occurs along the parallel direction to the magnetic field. To eliminate the corresponding Lagrange multiplier, we then study the Vlasov-Maxwell-Fokker-Planck system in two spatial dimensio…

Impact of quadrature measurement on quantum coherence
Luc\'ia \'Alvarez, Alfredo Luis
https://arxiv.org/abs/2507.13735 https://

Impact of quadrature measurement on quantum coherence
We examine the behavior of quadrature coherence under the measurement of the same field quadrature. This is carried out with the help of a beam splitter, that implies the contribution of the field state impinging at the other input port. We examine the case of Gaussian and number states.

Asymptotic behavior of the extremal position in a multi-type branching random walk with heavy-tailed displacements
Krzysztof Kowalski
https://arxiv.org/abs/2509.14808 https://…

Asymptotic behavior of the extremal position in a multi-type branching random walk with heavy-tailed displacements
We consider a multi-type branching random walk with displacements that have either regularly varying or semi-exponential tails. We investigate the asymptotic behavior of the rightmost particle in irreducible and reducible regimes and identify the correct normalization for different settings.

freshmen: Zeggelink's freshmen (1999)
Snapshots of friendships among freshmen majoring in Sociology at the University of Groningen, collected five times over the 1998-1999 year. The direction of an edge indicates that student i is friends with student j, and the edge weight gives their friendship level from 1 (best friend) to 5 (troubled relation). Metadata include gender, age, program type, and smoking behavior.
This network has 34 nodes and 1156 edges.
Tags: Social, O…

What You See Is What It Does: A Structural Pattern for Legible Software
Eagon Meng, Daniel Jackson
https://arxiv.org/abs/2508.14511 https://arxiv.org/pdf/2…

What You See Is What It Does: A Structural Pattern for Legible Software
The opportunities offered by LLM coders (and their current limitations) demand a reevaluation of how software is structured. Software today is often "illegible" - lacking a direct correspondence between code and observed behavior - and insufficiently modular, leading to a failure of three key requirements of robust coding: incrementality (the ability to deliver small increments by making localized changes), integrity (avoiding breaking prior increments) and transparency (making clear what has c…

Singular Perturbations of Hamilton-Jacobi Equations in the Wasserstein Space
Antonios Zitridis
https://arxiv.org/abs/2508.14333 https://arxiv.org/pdf/2508.…

Singular Perturbations of Hamilton-Jacobi Equations in the Wasserstein Space
We study a singular perturbation problem for second-order Hamilton-Jacobi equations in the Wasserstein space. Specifically, we characterize the behavior of the solutions as the perturbation parameter $\varepsilon$ tends to zero. The notion of solution we adopt is that of viscosity solutions in the sense of test functions on the Wasserstein space. Our proof utilizes the perturbed test function method, appropriately adapted to this setting. Finally, we highlight a connection with the homogenizati…

Thermodynamic constraints and pseudotransition behavior in a one-dimensional water-like system
F. F. Braz, S. M. de Souza, M. L. Lyra, Onofre Rojas
https://arxiv.org/abs/2509.14362

Thermodynamic constraints and pseudotransition behavior in a one-dimensional water-like system
We investigate a one-dimensional water-like lattice model with Van der Waals and hydrogen-bond interactions, allowing for particle number fluctuations through a chemical potential. The model, defined on a chain with periodic boundary conditions, exhibits three ground-state phases: gas, bonded liquid, and dense liquid, separated by sharp phase boundaries in the chemical potential and temperature plane. Using the transfer matrix method, we derive exact analytical results within the grand-canonica…

MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Renjie Li, Ruijie Ye, Mingyang Wu, Hao Frank Yang, Zhiwen Fan, Hezhen Hu, Zhengzhong Tu
https://arxiv.org/abs/2507.12463

MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Humans are integral components of the transportation ecosystem, and understanding their behaviors is crucial to facilitating the development of safe driving systems. Although recent progress has explored various aspects of human behavior$\unicode{x2014}$such as motion, trajectories, and intention$\unicode{x2014}$a comprehensive benchmark for evaluating human behavior understanding in autonomous driving remains unavailable. In this work, we propose $\textbf{MMHU}$, a large-scale benchmark for hu…

Long-lived quasinormal modes in the Euler-Heisenberg electrodynamics
B. C. L\"utf\"uo\u{g}lu
https://arxiv.org/abs/2508.13361 https://arxiv.org/p…

Long-lived quasinormal modes in the Euler-Heisenberg electrodynamics
Using the precise Leaver method and time-domain integration, we analyze the quasinormal modes and late-time behavior of massive neutral and charged scalar fields in the background of a charged, asymptotically flat black hole in the presence of Euler-Heisenberg nonlinear electrodynamics. We show that as the field mass increases, the damping rate decreases significantly, approaching arbitrarily long-lived states known as quasi-resonances. However, these modes cannot be identified in time-domain p…

Integrating Forecasting Models Within Steady-State Analysis and Optimization
Aayushya Agarwal, Larry Pileggi
https://arxiv.org/abs/2507.14117 https://

Integrating Forecasting Models Within Steady-State Analysis and Optimization
Extreme weather variations and the increasing unpredictability of load behavior make it difficult to determine power grid dispatches that are robust to uncertainties. While machine learning (ML) methods have improved the ability to model uncertainty caused by loads and renewables, accurately integrating these forecasts and their sensitivities into steady-state analyses and decision-making strategies remains an open challenge. Toward this goal, we present a generalized methodology that seamlessl…

The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management
Soumyadeep Dhar
https://arxiv.org/abs/2508.13942

The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management
The rise of autonomous, AI-driven agents in economic settings raises critical questions about their emergent strategic behavior. This paper investigates these dynamics in the cooperative context of a multi-echelon supply chain, a system famously prone to instabilities like the bullwhip effect. We conduct computational experiments with generative AI agents, powered by Large Language Models (LLMs), within a controlled supply chain simulation designed to isolate their behavioral tendencies. Our ce…

ALIGN: Word Association Learning for Cross-Cultural Generalization in Large Language Models
Chunhua Liu, Kabir Manandhar Shrestha, Sukai Huang
https://arxiv.org/abs/2508.13426 h…

ALIGN: Word Association Learning for Cross-Cultural Generalization in Large Language Models
As large language models (LLMs) increasingly mediate cross-cultural communication, their behavior still reflects the distributional bias of the languages and viewpoints that are over-represented in their pre-training corpora. Yet, it remains a challenge to model and align culture due to limited cultural knowledge and a lack of exploration into effective learning approaches. We introduce a cost-efficient, cognitively grounded remedy: parameter-efficient fine-tuning on native speakers' free word-…

Patterns, Models, and Challenges in Online Social Media: A Survey
Niccol\`o Di Marco, Anita Bonetti, Edoardo Di Martino, Edoardo Loru, Jacopo Nudo, Mario Edoardo Pandolfo, Giulio Pecile, Emanuele Sangiorgio, Irene Scalco, Simon Zollo, Matteo Cinelli, Fabiana Zollo, Walter Quattrociocchi
https://arxiv.org/abs/2507.13379

Patterns, Models, and Challenges in Online Social Media: A Survey
The rise of digital platforms has enabled the large scale observation of individual and collective behavior through high resolution interaction data. This development has opened new analytical pathways for investigating how information circulates, how opinions evolve, and how coordination emerges in online environments. Yet despite a growing body of research, the field remains fragmented and marked by methodological heterogeneity, limited model validation, and weak integration across domains. T…

Anomalous-magnetic-moment-enhanced Casimir effect
Daisuke Fujii, Katsumasa Nakayama, Kei Suzuki
https://arxiv.org/abs/2507.14015 https://

Anomalous-magnetic-moment-enhanced Casimir effect
We theoretically investigate the impact of the anomalous magnetic moment (AMM) of Dirac fermions on the fermionic Casimir effect under magnetic fields. We formulate it as an extension of the well-known Lifshitz formula. From our formula, we find that the AMM increases the fermionic Casimir energy. In particular, when the AMM is large enough, the Casimir energy is significantly enhanced by the gapless behavior of the lowest Landau level. We also quantitatively estimate the Casimir energy from el…

PHASE: Passive Human Activity Simulation Evaluation
Steven Lamp, Jason D. Hiser, Anh Nguyen-Tuong, Jack W. Davidson
https://arxiv.org/abs/2507.13505 https:…

PHASE: Passive Human Activity Simulation Evaluation
Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas. This paper presents PHASE (Passive Human Activity Simulation Evaluation), a machine learning framework that analyzes Zeek connection logs and distinguishes human from non-human activity with over 90\% accuracy. PHASE operates entirely passively, relying on standard …

Competing Length Scales and Symmetry Frustration Govern Non-Universal Melting in 2D Core-softened Colloidal Crystals
Thiago Puccinelli, Alexandre V. Ilha, Jos\'e Rafael Bordin
https://arxiv.org/abs/2507.13573

Competing Length Scales and Symmetry Frustration Govern Non-Universal Melting in 2D Core-softened Colloidal Crystals
We investigate the melting behavior of two-dimensional colloidal crystals stabilized by a core-softened potential featuring two competing interaction length scales. Using molecular dynamics simulations, we analyze three polymorphic solid phases -- low-density triangular, stripe, and kagome -- and uncover distinct melting pathways. The triangular and kagome crystals undergo abrupt first-order transitions, driven by the interplay between energetic frustration and structural reorganization. In par…

Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters
Yingfan Zhou, Philip Sanderink, Sigurd Jager Lemming, Cheng Fang
https://arxiv.org/abs/2508.13303

Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters
High-fidelity personalized human musculoskeletal models are crucial for simulating realistic behavior of physically coupled human-robot interactive systems and verifying their safety-critical applications in simulations before actual deployment, such as human-robot co-transportation and rehabilitation through robotic exoskeletons. Identifying subject-specific Hill-type muscle model parameters and bone dynamic parameters is essential for a personalized musculoskeletal model, but very challenging…

Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
Aaron Fl\"ototto, Benjamin Spetzler, Rose von Stackelberg, Martin Ziegler, Erich Runge, Christian Dre{\ss}ler
https://arxiv.org/abs/2508.13790

Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
The formation of extended sulfur vacancies in MoS2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale molecular dynamics simulations using machine learning interatomic potentials (MLIPs) reveal key mechanisms of cooperative vacancy transport, including incorporation of vacancies into clusters of arbitrary size. The simulations provide a coherent atomistic explanation for irradiation-induced vacancy patterns observed e…

Ideal Social Gas
VS Morales-Salgado
https://arxiv.org/abs/2508.13447 https://arxiv.org/pdf/2508.13447

Ideal Social Gas
This work continues the one commenced in a previous one, where the key idea is that individual stances on a social matter can be modeled as positions of particles in physics. Here, we explore the aggregation of individual behavior as a microscopic model of social phenomena to obtain quantities characterizing a society as a whole, similar to the resulting thermodynamical quantities at the macroscopic scale. We follow the theoretical framework of statistical mechanics with a Boltzmann distributio…

A Taxonomy of Prompt Defects in LLM Systems
Haoye Tian, Chong Wang, BoYang Yang, Lyuye Zhang, Yang Liu
https://arxiv.org/abs/2509.14404 https://arxiv.org/p…

A Taxonomy of Prompt Defects in LLM Systems
Large Language Models (LLMs) have become key components of modern software, with prompts acting as their de-facto programming interface. However, prompt design remains largely empirical and small mistakes can cascade into unreliable, insecure, or inefficient behavior. This paper presents the first systematic survey and taxonomy of prompt defects, recurring ways that prompts fail to elicit their intended behavior from LLMs. We organize defects along six dimensions: (1) Specification and Intent, …

Asymptotic freedom of dephased charging
Chayan Purkait, B. Prasanna Venkatesh, Gentaro Watanabe
https://arxiv.org/abs/2508.13497 https://arxiv.org/pdf/2508…

Asymptotic freedom of dephased charging
Quantum batteries, small-scale energy storage devices based on quantum systems, offer the potential for enhanced charging performance through quantum effects such as coherence and collectivity. In this work, we study the collective charging of a quantum battery consisting of N qubits, coupled to a driven qubit charger in a star configuration, with controlled pure dephasing acting on the charger. We investigate how an "asymptotic freedom"-like behavior, in which all the energy deposited into the…

RewardRank: Optimizing True Learning-to-Rank Utility
Gaurav Bhatt, Kiran Koshy Thekumparampil, Tanmay Gangwani, Tesi Xiao, Leonid Sigal
https://arxiv.org/abs/2508.14180 https://…

RewardRank: Optimizing True Learning-to-Rank Utility
Traditional ranking systems rely on proxy loss functions that assume simplistic user behavior, such as users preferring a rank list where items are sorted by hand-crafted relevance. However, real-world user interactions are influenced by complex behavioral biases, including position bias, brand affinity, decoy effects, and similarity aversion, which these objectives fail to capture. As a result, models trained on such losses often misalign with actual user utility, such as the probability of an…

ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
Rehan Raza, Guanjin Wang, Kevin Wong, Hamid Laga, Marco Fisichella
https://arxiv.org/abs/2508.13672

ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
Explainable Artificial Intelligence (XAI) methods, such as Local Interpretable Model-Agnostic Explanations (LIME), have advanced the interpretability of black-box machine learning models by approximating their behavior locally using interpretable surrogate models. However, LIME's inherent randomness in perturbation and sampling can lead to locality and instability issues, especially in scenarios with limited training data. In such cases, data scarcity can result in the generation of unrealistic…

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Kaiwei Zhang, Qi Jia, Zijian Chen, Wei Sun, Xiangyang Zhu, Chunyi Li, Dandan Zhu, Guangtao Zhai
https://arxiv.org/abs/2508.13743

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Large language models (LLMs), while increasingly used in domains requiring factual rigor, often display a troubling behavior: sycophancy, the tendency to align with user beliefs regardless of correctness. This tendency is reinforced by preference-based alignment techniques that optimize for user satisfaction but can undermine truthfulness. While relatively benign in casual dialogue, sycophancy poses serious risks in high-stakes settings such as scientific question answering (QA), where model ou…

The Social Context of Human-Robot Interactions
Sydney Thompson, Kate Candon, Marynel V\'azquez
https://arxiv.org/abs/2508.13982 https://arxiv.org/pdf/2…

The Social Context of Human-Robot Interactions
The Human-Robot Interaction (HRI) community often highlights the social context of an interaction as a key consideration when designing, implementing, and evaluating robot behavior. Unfortunately, researchers use the term "social context" in varied ways. This can lead to miscommunication, making it challenging to draw connections between related work on understanding and modeling the social contexts of human-robot interactions. To address this gap, we survey the HRI literature for existing defi…

Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
Jaeung Lee, Suhyeon Yu, Yurim Jang, Simon S. Woo, Jaemin Jo
https://arxiv.org/abs/2508.12730

Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
Machine Unlearning (MU) aims to remove target training data from a trained model so that the removed data no longer influences the model's behavior, fulfilling "right to be forgotten" obligations under data privacy laws. Yet, we observe that researchers in this rapidly emerging field face challenges in analyzing and understanding the behavior of different MU methods, especially in terms of three fundamental principles in MU: accuracy, efficiency, and privacy. Consequently, they often rely on ag…

Asymptotic Behavior of Homogeneous Complex Monge-Ampere Equations on ALE Kahler manifolds
Qi Yao
https://arxiv.org/abs/2509.13609 https://arxiv.org/pdf/250…

Asymptotic Behavior of Homogeneous Complex Monge-Ampere Equations on ALE Kahler manifolds
This paper is a sequel to the author's earlier work and investigates the homogeneous complex Monge--Ampere equation (HCMA) on the product space $X \times D$, where $X$ is an asymptotically locally Euclidean (ALE) Kahler manifold and $D subset C$ is the unit disc. We establish precise asymptotic behavior of the solution to the HCMA equation, showing that the decay rate of the solution matches that of the prescribed boundary data and that uniform control in weighted Holder norms can be achieved. …

Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy
Jordan Peper, Yan Miao, Sayan Mitra, Ivan Ruchkin
https://arxiv.org/abs/2508.14181 https://

Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy
Precise and comprehensive situational awareness is a critical capability of modern autonomous systems. Deep neural networks that perceive task-critical details from rich sensory signals have become ubiquitous; however, their black-box behavior and sensitivity to environmental uncertainty and distribution shifts make them challenging to verify formally. Abstraction-based verification techniques for vision-based autonomy produce safety guarantees contingent on rigid assumptions, such as bounded e…

Phase Transitions of Electromagnetically Charged Black Holes in Lovelock Gravity with Nonconstant Curvature Horizons
N. Farhangkhah, S. Hajkhalili
https://arxiv.org/abs/2508.13221

Phase Transitions of Electromagnetically Charged Black Holes in Lovelock Gravity with Nonconstant Curvature Horizons
We present the most general class of charged black hole solutions in third-order Lovelock gravity within even-dimensional spacetimes in the presence of an electromagnetic field. These solutions feature nonconstant-curvature horizons that affect geometry when n>=8. The near-origin behavior of the metric reveals a timelike singularity for electrically charged cases, in contrast to the spacelike singularity found in the uncharged case. We investigate thermodynamic stability in both the grand canon…

Towards LLM-generated explanations for Component-based Knowledge Graph Question Answering Systems
Dennis Schiese, Aleksandr Perevalov, Andreas Both
https://arxiv.org/abs/2508.14553

Towards LLM-generated explanations for Component-based Knowledge Graph Question Answering Systems
Over time, software systems have reached a level of complexity that makes it difficult for their developers and users to explain particular decisions made by them. In this paper, we focus on the explainability of component-based systems for Question Answering (QA). These components often conduct processes driven by AI methods, in which behavior and decisions cannot be clearly explained or justified, s.t., even for QA experts interpreting the executed process and its results is hard. To address …

Less is more: removing a single bond increases the toughness of elastic networks
Antoine Sanner, Luca Michel, Alessandra Lingua, David S. Kammer
https://arxiv.org/abs/2507.13806

Less is more: removing a single bond increases the toughness of elastic networks
We investigate how the removal of a single bond affects the fracture behavior of triangular spring networks, whereby we systematically vary the position of the removed bond. Our simulations show that removing the bond has two contrasting effects on the fracture energy for initiation of crack propagation and on the fracture energy for failure of the entire network. A single missing bond can either lower or raise the initiation fracture energy, depending on its placement relative to the crack tip…

Physics-Informed ML Exploration of Structure-Transport Relationships in Hard Carbon
Nikhil Rampal, Stephen E. Weitzner, Fredrick Omenya, Marissa Wood, David M. Reed, Xiaolin Li, Jonathan R. I. Lee, Liwen F. Wan
https://arxiv.org/abs/2508.14849

Physics-Informed ML Exploration of Structure-Transport Relationships in Hard Carbon
Sodium-ion batteries are a cost-effective and sustainable alternative to lithium-ion systems for large-scale energy storage. Hard carbon (HC) anodes, composed of disordered graphitic and amorphous domains, offer high capacity but exhibit complex, poorly understood ion transport behavior. In particular, the relationship between local microstructure and sodium mobility remains unresolved, hindering rational performance optimization. Here, we introduce a data-driven framework that combines machine…

ISCA: A Framework for Interview-Style Conversational Agents
Charles Welch, Allison Lahnala, Vasudha Varadarajan, Lucie Flek, Rada Mihalcea, J. Lomax Boyd, Jo\~ao Sedoc
https://arxiv.org/abs/2508.14344 …

ISCA: A Framework for Interview-Style Conversational Agents
We present a low-compute non-generative system for implementing interview-style conversational agents which can be used to facilitate qualitative data collection through controlled interactions and quantitative analysis. Use cases include applications to tracking attitude formation or behavior change, where control or standardization over the conversational flow is desired. We show how our system can be easily adjusted through an online administrative panel to create new interviews, making the …

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Qingyi Chen, Ahmed H. Qureshi
https://arxiv.org/abs/2507.13940

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. To address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi…

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
https://arxiv.org/abs/2507.13525

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input. Compared to traditional methods such as collaborative filtering, LLM-based recommendation offers advantages in handling cold-start, cross-domain, and zero-shot scenarios, as well as supporting flexible input formats and generating explanations of user behavior. In this paper, we focus on a single-user setting, where no information from other users is used. This setting is practi…

SaMOSA: Sandbox for Malware Orchestration and Side-Channel Analysis
Meet Udeshi, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
https://arxiv.org/abs/2508.14261

SaMOSA: Sandbox for Malware Orchestration and Side-Channel Analysis
Cyber-attacks on operational technology (OT) and cyber-physical systems (CPS) have increased tremendously in recent years with the proliferation of malware targeting Linux-based embedded devices of OT and CPS systems. Comprehensive malware detection requires dynamic analysis of execution behavior in addition to static analysis of binaries. Safe execution of malware in a manner that captures relevant behaviors via side-channels requires a sandbox environment. Existing Linux sandboxes are built f…

A porous medium equation with spatially inhomogeneous absorption. Part II: Large time behavior
Razvan Gabriel Iagar, Diana-Rodica Munteanu
https://arxiv.org/abs/2508.11046 https…

A porous medium equation with spatially inhomogeneous absorption. Part II: Large time behavior
We study the large time behavior of solutions to the Cauchy problem for the quasilinear absorption-diffusion equation $$ \partial_tu=Δu^m-|x|^σu^p, \quad (x,t)\in\real^N\times(0,\infty), $$ with exponents $p>m>1$ and $σ>0$ and with initial conditions either satisfying $$ u_0\in L^{\infty}(\real^N)\cap C(\real^N), \quad \lim\limits_{|x|\to\infty}|x|^θu_0(x)=A\in(0,\infty) $$ for some $θ\geq0$. A number of different asymptotic profiles are identified, and unifor…

InTraVisTo: Inside Transformer Visualisation Tool
Nicol\`o Brunello, Davide Rigamonti, Andrea Sassella, Vincenzo Scotti, Mark James Carman
https://arxiv.org/abs/2507.13858

InTraVisTo: Inside Transformer Visualisation Tool
The reasoning capabilities of Large Language Models (LLMs) have increased greatly over the last few years, as have their size and complexity. Nonetheless, the use of LLMs in production remains challenging due to their unpredictable nature and discrepancies that can exist between their desired behavior and their actual model output. In this paper, we introduce a new tool, InTraVisTo (Inside Transformer Visualisation Tool), designed to enable researchers to investigate and trace the computational…

MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence
Chao Tang, Anxing Xiao, Yuhong Deng, Tianrun Hu, Wenlong Dong, Hanbo Zhang, David Hsu, Hong Zhang
https://arxiv.org/abs/2508.13534

MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence
Imitating tool manipulation from human videos offers an intuitive approach to teaching robots, while also providing a promising and scalable alternative to labor-intensive teleoperation data collection for visuomotor policy learning. While humans can mimic tool manipulation behavior by observing others perform a task just once and effortlessly transfer the skill to diverse tools for functionally equivalent tasks, current robots struggle to achieve this level of generalization. A key challenge l…

A first-principles theoretical study on two-dimensional MX and MX$_2$ metal halides: bandgap engineering, magnetism, and catalytic descriptors
Yu-Hsiu Lin, Daniel Maldonado-Lopez, Jose L. Mendoza-Cortes
https://arxiv.org/abs/2508.13955

A first-principles theoretical study on two-dimensional MX and MX$_2$ metal halides: bandgap engineering, magnetism, and catalytic descriptors
Metal halides, particularly MX and MX$_2$ compounds (where M represents metal elements and X = F, Cl, Br, I), have attracted significant interest due to their diverse electronic and optoelectronic properties. However, a comprehensive understanding of their structural and electronic behavior, particularly the evolution of these properties from bulk to low-dimensional forms, remains limited. To address this gap, we performed first-principles calculations to develop a database of 60 MX and MX$_2$ …

Knowledge Graph Tokenization for Behavior-Aware Generative Next POI Recommendation
Ke Sun, Mayi Xu
https://arxiv.org/abs/2509.12350 https://arxiv.org/pdf/2…

Knowledge Graph Tokenization for Behavior-Aware Generative Next POI Recommendation
Generative paradigm, especially powered by Large Language Models (LLMs), has emerged as a new solution to the next point-of-interest (POI) recommendation. Pioneering studies usually adopt a two-stage pipeline, starting with a tokenizer converting POIs into discrete identifiers that can be processed by LLMs, followed by POI behavior prediction tasks to instruction-tune LLM for next POI recommendation. Despite of remarkable progress, they still face two limitations: (1) existing tokenizers strugg…

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Pranay Dugar, Mohitvishnu S. Gadde, Jonah Siekmann, Yesh Godse, Aayam Shrestha, Alan Fern
https://arxiv.org/abs/2508.14098

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical, these transitions must be fast, robust, and energy efficient. While learning-based locomotion has made significant progress, most existing methods optimize for velocity-tracking rather than direct pose reaching, resulting in inefficient, marching-style behavior when applied to short-range tasks. In this work, we develop a reinforcement learning approach…

Pulled fronts are not (just) pulled
Montie Avery, Matt Holzer, Arnd Scheel
https://arxiv.org/abs/2508.14864 https://arxiv.org/pdf/2508.14864

Pulled fronts are not (just) pulled
Front propagation into unstable states is often determined by the linearization, that is, propagation speeds agree with predictions from the linearized equation at the unstable state. The leading edge behavior is then a Gaussian tail propagating with the linear spreading speed. Fronts following this leading edge are commonly referred to as pulled fronts, alluding to the idea that they are ``pulled'' by this leading-edge Gaussian tail. We describe here a class of examples that exhibits how these…

The Hardy spaces $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$ for Fourier integral operators for $p<1$
Naijia Liu, Jan Rozendaal, Liang Song
https://arxiv.org/abs/2508.13243 https…

The Hardy spaces $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$ for Fourier integral operators for $p<1$
We introduce the Hardy spaces $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$ for Fourier integral operators for $0

Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Shunlin Lu, Kangning Yin, Xiaojie Niu, Minyue Dai, Jingbo Wang, Jiangmiao Pang
https://arxiv.org/abs/2509.13780 http…

Behavior Foundation Model for Humanoid Robots
Whole-body control (WBC) of humanoid robots has witnessed remarkable progress in skill versatility, enabling a wide range of applications such as locomotion, teleoperation, and motion tracking. Despite these achievements, existing WBC frameworks remain largely task-specific, relying heavily on labor-intensive reward engineering and demonstrating limited generalization across tasks and skills. These limitations hinder their response to arbitrary control modes and restrict their deployment in com…

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta, Jovin D'sa, Faizan M. Tariq, David Isele, Sangjae Bae
https://arxiv.org/abs/2507.11940

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV's actions. To address this, we propose …