Tootfinder

Cosmology-informed Neural Networks to infer dark energy equation-of-state
Anshul Verma, Shashwat Sourav, Pavan K. Aluri, David F. Mota
https://arxiv.org/abs/2508.12032 https://

Cosmology-informed Neural Networks to infer dark energy equation-of-state
We present a framework that combines physics-informed neural networks (PINNs) with Markov Chain Monte Carlo (MCMC) inference to constrain dynamical dark energy models using the Pantheon+ Type Ia supernova compilation. First, we train a physics-informed neural network to learn the solution of the Friedmann equation and accurately reproduce the matter density term x_m(z) = Omega_m,0 (1+z)^3 across a range of Omega_m,0. For each of five two-parameter equation-of-state (EoS) forms: Chevallier-Polar…

Applied causality to infer protein dynamics and kinetics
Akashnathan Aranganathan, Eric R. Beyerle
https://arxiv.org/abs/2508.12060 https://arxiv.org/pdf/2…

Applied causality to infer protein dynamics and kinetics
The use of generative machine learning models, trained on the experimentally resolved structures deposited in the protein data bank, is an attractive approach to sampling conformational ensembles of proteins. Unfortunately, since the machine-learned model utilized to generate these ensembles is not tied to an equation of motion, such as a molecular dynamics integrator or other causal generator of the dynamics, there is no timescale or causal information encoded in them. As such, with this work,…

The same special effect as Sutekh's dust of death. Tempted to infer an in-story relationship. #BBC3 #DoctorWho

RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization
Bingsheng Peng, Shutao Zhang, Xi Zheng, Ye Xue, Xinyu Qin, Tsung-Hui Chang
https://arxiv.org/abs/2509.13686

RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization
Accurate localized wireless channel modeling is a cornerstone of cellular network optimization, enabling reliable prediction of network performance during parameter tuning. Localized statistical channel modeling (LSCM) is the state-of-the-art channel modeling framework tailored for cellular network optimization. However, traditional LSCM methods, which infer the channel's Angular Power Spectrum (APS) from Reference Signal Received Power (RSRP) measurements, suffer from critical limitations: the…

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Cesar Alan Contreras, Manolis Chiou, Alireza Rastegarpanah, Michal Szulik, Rustam Stolkin
https://arxiv.org/abs/2508.11093

Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
Human-robot collaboration requires robots to quickly infer user intent, provide transparent reasoning, and assist users in achieving their goals. Our recent work introduced GUIDER, our framework for inferring navigation and manipulation intents. We propose augmenting GUIDER with a vision-language model (VLM) and a text-only language model (LLM) to form a semantic prior that filters objects and locations based on the mission prompt. A vision pipeline (YOLO for object detection and the Segment An…

Quantum keystroke logging
En-Jui Chang
https://arxiv.org/abs/2509.13659 https://arxiv.org/pdf/2509.13659

Quantum keystroke logging
We propose a \textit{quantum keystroke logging} scheme in the context of GKP-based quantum communication. The central idea is that, if a communication provider controls the preparation of encoded states, it may be able to infer logical operations applied by users prior to transmission. We show that phase estimation can be adapted to this setting despite two obstacles: GKP codewords are not eigenstates of the logical operators, and realistic communication involves one-shot operations rather than…

Mitigating Jailbreaks with Intent-Aware LLMs
Wei Jie Yeo, Ranjan Satapathy, Erik Cambria
https://arxiv.org/abs/2508.12072 https://arxiv.org/pdf/2508.12072

Mitigating Jailbreaks with Intent-Aware LLMs
Despite extensive safety-tuning, large language models (LLMs) remain vulnerable to jailbreak attacks via adversarially crafted instructions, reflecting a persistent trade-off between safety and task performance. In this work, we propose Intent-FT, a simple and lightweight fine-tuning approach that explicitly trains LLMs to infer the underlying intent of an instruction before responding. By fine-tuning on a targeted set of adversarial instructions, Intent-FT enables LLMs to generalize intent ded…

SALT4Decompile: Inferring Source-level Abstract Logic Tree for LLM-Based Binary Decompilation
Yongpan Wang, Xin Xu, Xiaojie Zhu, Xiaodong Gu, Beijun Shen
https://arxiv.org/abs/2509.14646

SALT4Decompile: Inferring Source-level Abstract Logic Tree for LLM-Based Binary Decompilation
Decompilation is widely used in reverse engineering to recover high-level language code from binary executables. While recent approaches leveraging Large Language Models (LLMs) have shown promising progress, they typically treat assembly code as a linear sequence of instructions, overlooking arbitrary jump patterns and isolated data segments inherent to binary files. This limitation significantly hinders their ability to correctly infer source code semantics from assembly code. To address this …

VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars
Sayed Shafaat Mahmud, Sayantan Auddy, Neal Turner, Jeffrey S. Bary
https://arxiv.org/abs/2509.12324

VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars
We present \textbf{VADER} (Variational Autoencoder for Disks Embedded with Rings), for inferring both planet mass and global disk properties from high-resolution ALMA dust continuum images of protoplanetary disks (PPDs). VADER, a probabilistic deep learning model, enables uncertainty-aware inference of planet masses, $α$-viscosity, dust-to-gas ratio, Stokes number, flaring index, and the number of planets directly from protoplanetary disk images. VADER is trained on over 100{,}000 synthetic im…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Seongeun Ryu, Yunyong Ko, Sang-Wook Kim
https://arxiv.org/abs/2508.13064 https:/…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users' interest persistence, and (C2) modeling the varying lifetime of news across topics and us…

Control of a commercial vehicle by a tetraplegic human using a bimanual brain-computer interface
Xinyun Zou, Jorge Gamez, Meghna Menon, Phillip Ring, Chadwick Boulay, Likhith Chitneni, Jackson Brennecke, Shana R. Melby, Gracy Kureel, Kelsie Pejsa, Emily R. Rosario, Ausaf A. Bari, Aniruddh Ravindran, Tyson Aflalo, Spencer S. Kellis, Dimitar Filev, Florian Solzbacher, Richard A. Andersen

Control of a commercial vehicle by a tetraplegic human using a bimanual brain-computer interface
Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a bimanual BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted with intracortical BCI electrodes in the posterior parietal cortex (PPC) and the hand knob…

Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
Weihao Yan, Christoph Brune, Mengwu Guo
https://arxiv.org/abs/2509.14054 https://

Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
Inferring parameters of high-dimensional partial differential equations (PDEs) poses significant computational and inferential challenges, primarily due to the curse of dimensionality and the inherent limitations of traditional numerical methods. This paper introduces a novel two-stage Bayesian framework that synergistically integrates training, physics-based deep kernel learning (DKL) with Hamiltonian Monte Carlo (HMC) to robustly infer unknown PDE parameters and quantify their uncertainties f…

The velocity field of the Scorpius-Centaurus OB association
S. Hutschenreuter (University of Vienna, Department of Astrophysics), J. Alves (University of Vienna, Department of Astrophysics), L. Posch (University of Vienna, Department of Astrophysics), J. Gro{\ss}schedl (Astronomical Institute of the Czech Academy of Sciences), M. Piecka (University of Vienna, Department of Astrophysics), N. Miret-Roig (Departament de F\'isica Qu\`antica i Astrof\'isica), S. Ratzenb\"ock (C…

The velocity field of the Scorpius-Centaurus OB association
We present a non-parametric reconstruction of the three-dimensional velocity field of the Scorpius-Centaurus OB association (Sco- Cen). Using Gaia DR3 astrometry and radial velocities, we infer the velocity field using information field theory on a 70 x 70 x 50 grid at 3 pc resolution. Our model suggests the existence of a primary stellar velocity field with a secondary field that accounts for an additional young kinematic component in Upper Scorpius and Lupus. We find clear tracers of a feedba…

Deciphering the global production network from cross-border firm transactions
Neave O'Clery, Ben Radcliffe-Brown, Thomas Spencer, Daniel Tarling-Hunter
https://arxiv.org/abs/2508.12315

Deciphering the global production network from cross-border firm transactions
Critical for policy-making and business operations, the study of global supply chains has been severely hampered by a lack of detailed data. Here we harness global firm-level transaction data covering 20m global firms, and 1 billion cross-border transactions, to infer key inputs for over 1200 products. Transforming this data to a directed network, we find that products are clustered into three large groups including textiles, chemicals and food, and machinery and metals. European industrial nat…

Learning to Restore Heisenberg Limit in Noisy Quantum Sensing via Quantum Digital Twin
Hang Xu, Tailong Xiao, Jingzheng Huang, Jianping Fan, Guihua Zeng
https://arxiv.org/abs/2508.11198

Learning to Restore Heisenberg Limit in Noisy Quantum Sensing via Quantum Digital Twin
Quantum sensors leverage nonclassical resources to achieve sensing precision at the Heisenberg limit, surpassing the standard quantum limit attainable through classical strategies. However, a critical issue is that the environmental noise induces rapid decoherence, fundamentally limiting the realizability of the Heisenberg limit. In this Letter, we propose a quantum digital twin protocol to overcome this issue. The protocol first establishes observable-constrained state reconstruction to infer …

Inhomogeneous continuous-time Markov chains to infer flexible time-varying evolutionary rates
Pratyusa Datta, Philippe Lemey, Marc A. Suchard
https://arxiv.org/abs/2510.11982 ht…

Inhomogeneous continuous-time Markov chains to infer flexible time-varying evolutionary rates
Reconstructing evolutionary histories and estimating the rate of evolution from molecular sequence data is of central importance in evolutionary biology and infectious disease research. We introduce a flexible Bayesian phylogenetic inference framework that accommodates changing evolutionary rates over time by modeling sequence character substitution processes as inhomogeneous continuous-time Markov chains (ICTMCs) acting along the unknown phylogeny, where the rate remains as an unknown, positiv…

Enhancing Situational Awareness in Wearable Audio Devices Using a Lightweight Sound Event Localization and Detection System
Jun-Wei Yeow, Ee-Leng Tan, Santi Peksi, Zhen-Ting Ong, Woon-Seng Gan
https://arxiv.org/abs/2509.14650

Enhancing Situational Awareness in Wearable Audio Devices Using a Lightweight Sound Event Localization and Detection System
Wearable audio devices with active noise control (ANC) enhance listening comfort but often at the expense of situational awareness. However, this auditory isolation may mask crucial environmental cues, posing significant safety risks. To address this, we propose an environmental intelligence framework that combines Acoustic Scene Classification (ASC) with Sound Event Localization and Detection (SELD). Our system first employs a lightweight ASC model to infer the current environment. The scene p…

End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier, Hieu Le, Juerg Schwitter, Pascal Fua
https://arxiv.org/abs/2509.12090

End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Reconstructing cardiac motion from cine CMR sequences is critical for diagnosis, prediction, and intervention. Existing methods rely on complete CMR stacks to infer full heart motion, limiting their utility in intra-procedural scenarios where only sparse observations are available. We present TetHeart, the first end-to-end framework that unifies full 4D multi-structure heart mesh recovery from both offline full-stack acquisitions and intra-procedural sparse-slice observations. Our method levera…

Field-level inference in cosmology
Florent Leclercq
https://arxiv.org/abs/2509.13435 https://arxiv.org/pdf/2509.13435…

Field-level inference in cosmology
These lecture notes delve into field-level inference, a framework offering a robust way to extract more information and avoid biases compared to traditional methods for cosmological data analysis. The core idea is to analyse uncompressed maps to infer underlying physical fields and cosmological parameters. We introduce Bayesian hierarchical field-level models and discuss sampling techniques for exploring complex, high-dimensional posterior distributions. We review the framework that underpins f…

Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference
Synthia Wang, Sai Teja Peddinti, Nina Taft, Nick Feamster
https://arxiv.org/abs/2509.12152 https://…

Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference
Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a …

Volatile-bearing mineral atmospheres of hot rocky exoplanets as probes of interior state and composition
Fabian L. Seidler, Paolo A. Sossi, Dan J. Bower, Brice-Olivier Demory
https://arxiv.org/abs/2509.13610

Volatile-bearing mineral atmospheres of hot rocky exoplanets as probes of interior state and composition
The atmospheres of hot rocky exoplanets (HREs), should they persist, are products of interactions with underlying magma oceans. Spectra collected by the James Webb Space Telescope (JWST) hint at a CO/CO$_2$-rich atmosphere on the HRE 55 Cancri e, indicative of such a process. Here, we aim to identify diagnostic features that can be used to infer the composition and geochemical state of HREs. We construct a coupled atmosphere-interior model that computes the equilibrium gas speciation in the atm…

Crosslisted article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Yo...
Sayed Shafaat Mahmud, Sayantan Auddy, Neal Turner, Jeffrey S. Bary

Bayesian Inference of Gravity through Realistic 3D Modeling of Wide Binary Orbits: General Algorithm and a Pilot Study with HARPS Radial Velocities
Kyu-Hyun Chae
https://arxiv.org/abs/2508.11996

Bayesian Inference of Gravity through Realistic 3D Modeling of Wide Binary Orbits: General Algorithm and a Pilot Study with HARPS Radial Velocities
When 3D relative displacement $\mathbf{r}$ and velocity $\mathbf{v}$ between the pair in a gravitationally-bound system are precisely measured, the six measured quantities at one phase can allow elliptical orbit solutions at a given gravitational parameter $G$. Due to degeneracies between orbital-geometric parameters and $G$, individual Bayesian inferences and their statistical consolidation are needed to infer $G$ as recently suggested by a Bayesian 3D modeling algorithm. Here I present a full…

INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
Harshvardhan C. Takawale, Nirupam Roy, Phil Brown
https://arxiv.org/abs/2510.07442 https://

INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
Accurate modeling of spatial acoustics is critical for immersive and intelligible audio in confined, resonant environments such as car cabins. Current tuning methods are manual, hardware-intensive, and static, failing to account for frequency selective behaviors and dynamic changes like passenger presence or seat adjustments. To address this issue, we propose INFER: Implicit Neural Frequency Response fields, a frequency-domain neural framework that is jointly conditioned on source and receiver …

Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?
Cedric Richter, Heike Wehrheim
https://arxiv.org/abs/2510.12702 http…

Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?
Automatic software verifiers have become increasingly effective at the task of checking software against (formal) specifications. Yet, their adoption in practice has been hampered by the lack of such specifications in real world code. Large Language Models (LLMs) have shown promise in inferring formal postconditions from natural language hints embedded in code such as function names, comments or documentation. Using the generated postconditions as specifications in a subsequent verification, ho…

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Mathijs van Noort, Femke Ongenae, Pieter Bonte
https://arxiv.org/abs/2509.10146 https://

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Temporal reasoning in dynamic, data-intensive environments increasingly demands expressive yet tractable logical frameworks. Traditional approaches often rely on negation to express absence or contradiction. In such contexts, Negation-as-Failure is commonly used to infer negative information from the lack of positive evidence. However, open and distributed systems such as IoT networks or the Semantic Web Negation-as-Failure semantics become unreliable due to incomplete and asynchronous data. Th…

An Introduction to Dust Evolution and Vertical Transport in Protoplanetary Disks
Marion Villenave
https://arxiv.org/abs/2509.10614 https://arxiv.org/pdf/25…

An Introduction to Dust Evolution and Vertical Transport in Protoplanetary Disks
This tutorial is an introduction to observational studies of dust transport and evolution in protoplanetary disks. Spatially resolved observations of disks at multiple wavelengths can allow to infer the distribution of various dust grains and gas species. Combining these observations offers a more complete understanding of dust structure and properties across different disk locations. For example, by better characterizing the disk vertical structure, observations help to constrain the level of …

Redefining Website Fingerprinting Attacks With Multiagent LLMs
Chuxu Song, Dheekshith Dev Manohar Mekala, Hao Wang, Richard Martin
https://arxiv.org/abs/2509.12462 https://

Redefining Website Fingerprinting Attacks With Multiagent LLMs
Website Fingerprinting (WFP) uses deep learning models to classify encrypted network traffic to infer visited websites. While historically effective, prior methods fail to generalize to modern web environments. Single-page applications (SPAs) eliminate the paradigm of websites as sets of discrete pages, undermining page-based classification, and traffic from scripted browsers lacks the behavioral richness seen in real user sessions. Our study reveals that users exhibit highly diverse behaviors …

Constraining Cosmology with Double-Source-Plane Strong Gravitational Lenses From the AGEL Survey
Duncan J. Bowden, Nandini Sahu, Anowar J. Shajib, Kim-Vy Tran, Tania M. Barone, Keerthi Vasan G. C., Daniel J. Ballard, Thomas E. Collett, Faith Dalessandro, Giovanni Ferrami, Karl Glazebrook, William J. Gottemoller, Leena Iwamoto, Tucker Jones, Glenn G. Kacprzak, Geraint F. Lewis, Haven McIntosh-Lombardo, Hannah Skobe, Sherry H. Suyu, Sarah M. Sweet

Constraining Cosmology with Double-Source-Plane Strong Gravitational Lenses From the AGEL Survey
Double-source-plane strong gravitational lenses (DSPLs), with two sources at different redshifts, are independent cosmological probes of the dark energy equation of state parameter $w$ and the matter density parameter $Ω_{\rm m}$. We present the lens model for the DSPL AGEL035346$-$170639 and infer cosmological constraints from this system for flat $Λ$CDM and flat $w$CDM cosmologies. From the joint posterior of $w$ and $Ω_{\rm m}$ in the flat $w$CDM cosmology, we extract the following median…

Quantifying Mental States in Work Environment: Mathematical Perspectives
Aymen Balti, Assane Wade, Abdelatif Oujbara, M. A., Aziz-Alaoui, Hicham Bellarabi, Frederic Dutertre, Benjamin Ambrosio
https://arxiv.org/abs/2509.12162

Quantifying Mental States in Work Environment: Mathematical Perspectives
This article presents a study involving 87 participants exposed to a stressful scenario in a virtual reality (VR) environment. An algorithm was developed to assign a positive or negative valence based on questionnaire responses. EEG signals were recorded, and a k-nearest neighbors (KNN) algorithm was trained to infer emotional valence from these signals. Our objective is to further develop mathematical models capable of describing the dynamic evolution of emotional and mental states.

Measuring the Two-Dimensional Thermal Structures of Protoplanetary Disks
Anna J. Fehr, Sean M. Andrews
https://arxiv.org/abs/2509.15196 https://arxiv.org/p…

Measuring the Two-Dimensional Thermal Structures of Protoplanetary Disks
We present a flexible, annulus-by-annulus method to constrain the 2-D thermal structure of a protoplanetary disk from optically thick spectral line emission. Using synthetic disk models with a known temperature and density structure, we extracted the vertical emission surfaces and brightness temperatures in radial annuli for multiple CO isotopologue transitions and used them to infer the vertical temperature profiles. This approach reliably recovers the injected temperature structure despite no…

Efficient Methods for Accurate Sparse Trajectory Recovery and Map Matching
Wei Tian, Jieming Shi, Man Lung Yiu
https://arxiv.org/abs/2508.10460 https://arx…

Efficient Methods for Accurate Sparse Trajectory Recovery and Map Matching
Real-world trajectories are often sparse with low-sampling rates (i.e., long intervals between consecutive GPS points) and misaligned with road networks, yet many applications demand high-quality data for optimal performance. To improve data quality with sparse trajectories as input, we systematically study two related research problems: trajectory recovery on road network, which aims to infer missing points to recover high-sampling trajectories, and map matching, which aims to map GPS points t…

An Explorative Study on Distributed Computing Techniques in Training and Inference of Large Language Models
Sheikh Azizul Hakim, Saem Hasan
https://arxiv.org/abs/2510.11211 http…

An Explorative Study on Distributed Computing Techniques in Training and Inference of Large Language Models
Large language models (LLM) are advanced AI systems trained on extensive textual data, leveraging deep learning techniques to understand and generate human-like language. Today's LLMs with billions of parameters are so huge that hardly any single computing node can train, fine-tune, or infer from them. Therefore, several distributed computing techniques are being introduced in the literature to properly utilize LLMs. We have explored the application of distributed computing techniques in LLMs f…

Membership Inference Attacks with False Discovery Rate Control
Chenxu Zhao, Wei Qian, Aobo Chen, Mengdi Huai
https://arxiv.org/abs/2508.07066 https://arxiv…

Membership Inference Attacks with False Discovery Rate Control
Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have been proposed. Despite the significance and popularity of MIAs, existing works on MIAs are limited in providing guarantees on the false discovery rate (FDR), which refers to the expected proportion of false discoveries among the identified p…

Membership Inference Attacks on Recommender System: A Survey
Jiajie He, Yuechun Gu, Keke Chen, Xintong Chen
https://arxiv.org/abs/2509.11080 https://arxiv.…

Membership Inference Attacks on Recommender System: A Survey
Recommender systems (RecSys) have been widely applied to various applications, including E-commerce, finance, healthcare, social media and have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. However, recent studies have shown that RecSys are vulnerable to membership inference attacks (MIAs), which aim to infer whether user interaction record was used to train a target model or not. MIAs on RecSys models can dir…

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
https://arxiv.org/abs/2510.11600

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted pressure probes to infer the wake state, and is trained entirely offline on a dataset built with open-loop actuations, thus avoiding the complexities of online learning. Sparsification …

Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Wenli Shi, Clemence Grislain, Olivier Sigaud, Mohamed Chetouani
https://arxiv.org/abs/2510.12370 https://…

Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Legibility of robot motion is critical in human-robot interaction, as it allows humans to quickly infer a robot's intended goal. Although traditional trajectory generation methods typically prioritize efficiency, they often fail to make the robot's intentions clear to humans. Meanwhile, existing approaches to legible motion usually produce only a single "most legible" trajectory, overlooking the need to modulate intent expressiveness in different contexts. In this work, we propose a novel motio…

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li
https://arxiv.org/abs/2509.06531

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Link prediction in knowledge graphs requires integrating structural information and semantic context to infer missing entities. While large language models offer strong generative reasoning capabilities, their limited exploitation of structural signals often results in structural sparsity and semantic ambiguity, especially under incomplete or zero-shot settings. To address these challenges, we propose SLiNT (Structure-aware Language model with Injection and coNtrastive Training), a modular fram…

Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing
Kaixun Yang, Yizhou Fan, Luzhen Tang, Mladen Rakovi\'c, Xinyu Li, Dragan Ga\v{s}evi\'c, Guanliang Chen
https://arxiv.org/abs/2508.10310

Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing
The integration of Generative AI (GenAI) into education is reshaping how students learn, making self-regulated learning (SRL) - the ability to plan, monitor, and adapt one's learning - more important than ever. To support learners in these new contexts, it is essential to understand how SRL unfolds during interaction with GenAI tools. Learning analytics offers powerful techniques for analyzing digital trace data to infer SRL behaviors. However, existing approaches often assume SRL processes are…

Cosmic Ray Spatial Distribution and the Galactic/Extragalactic Transition
Paolo Lipari, Silvia Vernetto
https://arxiv.org/abs/2509.09028 https://arxiv.org/…

Cosmic Ray Spatial Distribution and the Galactic/Extragalactic Transition
Determining the spatial distribution of Galactic cosmic rays (CRs) is fundamental to understand how these particles propagate in interstellar space and to infer their source spectra. The most sensitive method of studying this problem is observing the gamma--ray and neutrino diffuse fluxes produced in the inelastic interactions of CR protons and nuclei with interstellar gas that encode the energy and spatial distributions of the interacting particles. Most theoretical models assume that the spat…

When Algorithms Infer Gender: Revisiting Computational Phenotyping with Electronic Health Records Data
Jessica Gronsbell, Hilary Thurston, Lillian Dong, Vanessa Ferguson, Diksha Sen Chaudhury, Braden O'Neill, Katrina S. Sha, Rebecca Bonneville
https://arxiv.org/abs/2508.14150

When Algorithms Infer Gender: Revisiting Computational Phenotyping with Electronic Health Records Data
Computational phenotyping has emerged as a practical solution to the incomplete collection of data on gender in electronic health records (EHRs). This approach relies on algorithms to infer a patient's gender using the available data in their health record, such as diagnosis codes, medication histories, and information in clinical notes. Although intended to improve the visibility of trans and gender-expansive populations in EHR-based biomedical research, computational phenotyping raises signif…

A Pseudo-Fermion Propagator Approach to the Fermion Sign Problem
Yunuo Xiong, Hongwei Xiong
https://arxiv.org/abs/2508.09557 https://arxiv.org/pdf/2508.095…

A Pseudo-Fermion Propagator Approach to the Fermion Sign Problem
In this work, within the framework of path integral Monte Carlo, we construct a pseudo-fermion propagator by replacing the original fermionic determinant with its absolute value. This modified propagator defines an auxiliary system free from the fermion sign problem, enabling efficient simulations of fermionic systems. We found that by shifting the pseudo-fermion energy based on the energy of a non-interacting fermion system, we can efficiently and reliably infer the energy of fermionic systems…

I suspect we’re doing something similar to the tank classifier when we ascribe intelligence to AIs: there are patterns such as (for example) grammatical correctness which we •associate• with this abstract thing called “intelligence,” and we thus mis-infer the existence of everything else we associate with the notion of “intelligence” when we see (for example) correct grammar.
Or should we just call the machine intelligent because we classify it as intelligence because our brains, which we assume are intelligent, think it fits the pattern of intelligence? And now you see what the OP means about “begging the question of intelligence.”

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Fr\'ed\'eric Zheng, Yassir Jedra, Alexandre Proutiere
https://arxiv.org/abs/2510.08117 https://

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
We address the problem of estimating a high-dimensional matrix from linear measurements, with a focus on designing optimal rank-adaptive algorithms. These algorithms infer the matrix by estimating its singular values and the corresponding singular vectors up to an effective rank, adaptively determined based on the data. We establish instance-specific lower bounds for the sample complexity of such algorithms, uncovering fundamental trade-offs in selecting the effective rank: balancing the precis…

Using Aggregate Relational Data to Infer Social Networks
Xunkang Tian
https://arxiv.org/abs/2509.01503 https://arxiv.org/pdf/2509.01503

Using Aggregate Relational Data to Infer Social Networks
This study introduces a novel approach for inferring social network structures using Aggregate Relational Data (ARD), addressing the challenge of limited detailed network data availability. By integrating ARD with variational approximation methods, we provide a computationally efficient and cost-effective solution for network analysis. Our methodology demonstrates the potential of ARD to offer insightful approximations of network dynamics, as evidenced by Monte Carlo Simulations. This paper not…

Tight correlation of star formation with [Ci] and CO lines across cosmic time
Theodoros Topkaras, Thomas G. Bisbas, Zhi-Yu Zhang, V. Ossenkopf-Okada
https://arxiv.org/abs/2508.09951

Tight correlation of star formation with [Ci] and CO lines across cosmic time
Context. Cold molecular gas tracers, such as CI and CO lines, have been widely used to infer specific characteristics of the ISM and to derive star-formation relations among galaxies. Aims. However, there is still a lack of systematic studies of the star-formation scaling relation of CO and [CI] lines across cosmic time on multiple physical scales. Methods. We used observations of the ground state transitions of [CI], CO, and [CII], for 885 sources collected from the literature, to infer possib…

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
Ananda Prakash Verma
https://arxiv.org/abs/2508.07224 https://arxiv.org/pdf/2508.07…

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
We present EDGE, a general-purpose, misconception-aware adaptive learning framework composed of four stages: Evaluate (ability and state estimation), Diagnose (posterior infer-ence of misconceptions), Generate (counterfactual item synthesis), and Exercise (index-based retrieval scheduling). EDGE unifies psychometrics (IRT/Bayesian state space models), cog-nitive diagnostics (misconception discovery from distractor patterns and response latencies), contrastive item generation (minimal perturbati…

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Wenxin Mao, Zhitao Wang Long Wang, Sirong Chen, Cuiyun Gao, Luyang Cao, Ziming Liu, Qiming Zhang, Jun Zhou, Zhi Jin
https://arxiv.org/abs/2508.03379

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to capture complex requirements like intricate system behaviors, conditional logic, and architectural constraints; implicit data dependencies in service-oriented architectures are difficult to infer and handle correctly. To bridge this gap, we propose a novel step-by-step code generation framework named UML2Dep by leveragi…

DCHO: A Decomposition-Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applications
Weibin Li, Wendu Li, Quanying Liu
https://arxiv.org/abs/2509.09696 …

DCHO: A Decomposition-Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applications
Higher-order brain connectivity (HOBC), which captures interactions among three or more brain regions, provides richer organizational information than traditional pairwise functional connectivity (FC). Recent studies have begun to infer latent HOBC from noninvasive imaging data, but they mainly focus on static analyses, limiting their applicability in dynamic prediction tasks. To address this gap, we propose DCHO, a unified approach for modeling and forecasting the temporal evolution of HOBC ba…

Bayesian Multivariable Bidirectional Mendelian Randomization
Bitan Sarkar, Yuchao Jiang, Yang Ni
https://arxiv.org/abs/2510.09991 https://arxiv.org/pdf/251…

Bayesian Multivariable Bidirectional Mendelian Randomization
Mendelian randomization (MR) is a pivotal tool in genetic epidemiology, leveraging genetic variants as instrumental variables to infer causal relationships between modifiable exposures and health outcomes. Traditional MR methods, while powerful, often rest on stringent assumptions such as the absence of feedback loops, which are frequently violated in complex biological systems. In addition, many popular MR approaches focus on only two variables (i.e., one exposure and one outcome) whereas our …

Flux tube profile from Holography: finite size and strong coupling corrections
Tommaso Canneti
https://arxiv.org/abs/2510.02442 https://arxiv.org/pdf/2510.…

Flux tube profile from Holography: finite size and strong coupling corrections
We use the holographic correspondence as a tool to infer the classical flux tube profile connecting a static quark-antiquark pair in a $2+1$-dimensional strongly-coupled large $N$ QCD-like theory. Our final prediction extends already known results in the literature in several ways. First, it is an analytical function of both the space-like boundary coordinates; in other words, we keep track of what happens both along and transversely to the inter-quark axis. Then, we take into account the finit…

Search for gravitational waves from eccentric binary black holes with an effective-one-body template
Yi-Fan Wang, Alexander H. Nitz
https://arxiv.org/abs/2508.05018 https://

Search for gravitational waves from eccentric binary black holes with an effective-one-body template
As gravitational wave astronomy has entered an era of routine detections, it becomes increasingly important to precisely measure the physical parameters of individual events and infer population properties. Eccentricity is a key observable, suggesting that binaries form in a dense stellar environment through dynamical encounters. This work performs the first matched-filtering search for gravitational waves from eccentric binary black holes (BBHs) covering the mass range $[5, 200]~M_\odot$ and e…

SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
Song Gao, Rui Meng, Xiaodong Xu, Haixiao Gao, Yiming Liu, Chenyuan Feng, Ping Zhang, Tony Q. S. Quek, Dusit Niyato
https://arxiv.org/abs/2509.04803

SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
Semantic communication (SemCom), as a novel paradigm for future communication systems, has recently attracted much attention due to its superiority in communication efficiency. However, similar to traditional communication, it also suffers from eavesdropping threats. Intelligent eavesdroppers could launch advanced semantic analysis techniques to infer secret semantic information. Therefore, some researchers have designed Semantic Steganography Communication (SemSteCom) scheme to confuse semanti…

What Do Agents Think Others Would Do? Level-2 Inverse Games for Inferring Agents' Estimates of Others' Objectives
Hamzah I. Khan, Jingqi Li, David Fridovich-Keil
https://arxiv.org/abs/2508.03824

What Do Agents Think Others Would Do? Level-2 Inverse Games for Inferring Agents' Estimates of Others' Objectives
Effectively interpreting strategic interactions among multiple agents requires us to infer each agent's objective from limited information. Existing inverse game-theoretic approaches frame this challenge in terms of a "level-1" inference problem, in which we take the perspective of a third-party observer and assume that individual agents share complete knowledge of one another's objectives. However, this assumption breaks down in decentralized, real-world decision scenarios like urban driving a…

Strategic Concealment of Environment Representations in Competitive Games
Yue Guan, Dipankar Maity, Panagiotis Tsiotras
https://arxiv.org/abs/2509.05503 https://

Strategic Concealment of Environment Representations in Competitive Games
This paper investigates the strategic concealment of map abstractions used by the players in competitive games. We consider a defense scenario in which one player (the Defender) seeks to infer and exploit the abstraction used by the other player (the Attacker). The interaction between the two players is modeled as a Bayesian game: the Defender selects a barrier configuration, i.e., a placement of obstacles that can obstruct the Attacker's movement, based on its belief about the Attacker's abstr…

HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
Shuwei Chen, Jiajun Cui, Zhengqi Xu, Fan Zhang, Jiangke Fan, Teng Zhang, Xingxing Wang
https://arxiv.org/abs/2510.11100

HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
Click-through rate (CTR) prediction, which models behavior sequence and non-sequential features (e.g., user/item profiles or cross features) to infer user interest, underpins industrial recommender systems. However, most methods face three forms of heterogeneity that degrade predictive performance: (i) Feature Heterogeneity persists when limited sequence side features provide less granular interest representation compared to extensive non-sequential features, thereby impairing sequence modeling…

Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
Kexin Chu, Zecheng Lin, Dawei Xiang, Zixu Shen, Jianchang Su, Cheng Chu, Yiwei Yang, Wenhui Zhang, Wenfei Wu, Wei Zhang
https://arxiv.org/abs/2508.08438

Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference
Global KV-cache sharing has emerged as a key optimization for accelerating large language model (LLM) inference. However, it exposes a new class of timing side-channel attacks, enabling adversaries to infer sensitive user inputs via shared cache entries. Existing defenses, such as per-user isolation, eliminate leakage but degrade performance by up to 38.9% in time-to-first-token (TTFT), making them impractical for high-throughput deployment. To address this gap, we introduce SafeKV (Secure and …

The Surface Orientation Ambiguity for Single Molecules at Dielectric Interfaces
E. Dey, M. Elorza, F. W. Foss, J. J. Gomez Cadenas, B. J. P. Jones
https://arxiv.org/abs/2508.03903

The Surface Orientation Ambiguity for Single Molecules at Dielectric Interfaces
Fluorescent molecules emit light in a dipole radiation pattern that can be used to infer their orientation through defocused fluorescence microscopy. Proper measurement of the orientation requires mathematical modeling of the radiation pattern expected for a dipole in the geometry of interest, and subsequent comparison against experimental data. We point out an ambiguity in common calculations of these patterns that appears to compromise orientation measurements for molecules that are especiall…

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins, Zhaoming Xie
https://arxiv.org/abs/2510.08754

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and pred…

A deep learning framework for predicting functional visual performance in bionic eye users https://www.biorxiv.org/content/10.1101/2025.06.23.660990v1

Short GRB 090510: a magnetized neutron star binary merger leading to a black hole
J. A. Rueda, R. Ruffini, Yu Wang
https://arxiv.org/abs/2509.08172 https://

Short GRB 090510: a magnetized neutron star binary merger leading to a black hole
We model the short gamma-ray bursts (GRB) 090510 as the product of a magnetized neutron star (NS) binary merger. Accounting for the NS critical mass constraint given by the mass of PSR J0952--0607, we infer that GRB 090510 was a highly-magnetized NS-NS merger that left as remnant a Kerr black hole (BH) of $2.4 M_\odot$ with a low-mass accretion disk. The gamma-ray precursor is powered by the magnetic energy released during the merger of the NSs. The prompt emission originates at the transparenc…

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping
Suyuchen Wang, Tianyu Zhang, Ahmed Masry, Christopher Pal, Spandana Gella, Bang Liu, Perouz Taslakian
https://arxiv.org/abs/2510.03230

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping
GUI grounding, the task of mapping natural-language instructions to pixel coordinates, is crucial for autonomous agents, yet remains difficult for current VLMs. The core bottleneck is reliable patch-to-pixel mapping, which breaks when extrapolating to high-resolution displays unseen during training. Current approaches generate coordinates as text tokens directly from visual features, forcing the model to infer complex position-to-pixel mappings implicitly; as a result, accuracy degrades and fai…

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars
Rodrigo C\'ordova Rosado, Andy D. Goulding, Jenny E. Greene, Nickolas Kokron, Andrina Nicola, Michael A. Strauss, Ryan C. Hickox
https://arxiv.org/abs/2510.11780

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars
Understanding the dark matter (DM) halo environment in which galaxies that host active galactic nuclei (AGN) reside is a window into the nature of supermassive black hole (SMBH) accretion. We apply halo occupation distribution (HOD) modeling tools to interpret the angular cross-correlation functions between $1.5\times10^6$ luminous red galaxies (LRGs) and our $\sim28,500$ Hyper Suprime-Cam + Wide-field Infrared Survey Explorer-selected (and $L_{6 μm}$-limited) AGN to infer the halo properties …

Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translation
Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Xiaoyu Chen, Zhanglin Wu, Huan Yang, Hengchao Shang, Zongyao Li, Zhiqiang Rao, Jinlong Yang, Hao Yang
https://arxiv.org/abs/2509.03809

Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translation
Large language models (LLMs) have ushered in a new era for document-level machine translation (\textit{doc}-mt), yet their whole-document outputs challenge existing evaluation methods that assume sentence-by-sentence alignment. We introduce \textit{\textbf{Align-then-Slide}}, a complete evaluation framework for ultra-long doc-mt. In the Align stage, we automatically infer sentence-level source-target correspondences and rebuild the target to match the source sentence number, resolving omissions…

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
Matthieu Bou, Nyal Patel, Arjun Jagota, Satyapriya Krishna, Sonali Parbhoo
https://arxiv.org/abs/2510.06096

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a grand challenge. While Inverse Reinforcement Learning (IRL) can infer reward functions from behaviour, existing approaches either produce a single, overconfident reward estimate or fail to address the fundamental ambiguity of the task (non-identifiability). This paper introduces a principled auditing framework that re-frames reward inference from a simple e…

A Learning-Based Framework for Collision-Free Motion Planning
Mateus Salom\~ao, Tiany\"u Ren, Alexander K\"onig
https://arxiv.org/abs/2508.07502 https://

A Learning-Based Framework for Collision-Free Motion Planning
This paper presents a learning-based extension to a Circular Field (CF)-based motion planner for efficient, collision-free trajectory generation in cluttered environments. The proposed approach overcomes the limitations of hand-tuned force field parameters by employing a deep neural network trained to infer optimal planner gains from a single depth image of the scene. The pipeline incorporates a CUDA-accelerated perception module, a predictive agent-based planning strategy, and a dataset genera…

Label Inference Attacks against Federated Unlearning
Wei Wang, Xiangyun Tang, Yajie Wang, Yijing Lin, Tao Zhang, Meng Shen, Dusit Niyato, Liehuang Zhu
https://arxiv.org/abs/2508.06789

Label Inference Attacks against Federated Unlearning
Federated Unlearning (FU) has emerged as a promising solution to respond to the right to be forgotten of clients, by allowing clients to erase their data from global models without compromising model performance. Unfortunately, researchers find that the parameter variations of models induced by FU expose clients' data information, enabling attackers to infer the label of unlearning data, while label inference attacks against FU remain unexplored. In this paper, we introduce and analyze a new pr…

Trustworthy scientific inference for inverse problems with generative models
James Carzon, Luca Masserano, Joshua D. Ingram, Alex Shen, Antonio Carlos Herling Ribeiro Junior, Tommaso Dorigo, Michele Doro, Joshua S. Speagle, Rafael Izbicki, Ann B. Lee
https://arxiv.org/abs/2508.02602

Trustworthy scientific inference for inverse problems with generative models
Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to ``inverse problems'' to infer hidden parameters from observed data. While these methods can handle intractable models and large-scale studies, they can also produce biased or overconfident conclusions. We present a solution with Frequentist-Bayes (FreB), a mathemati…

Physical interpretation of the oscillation spectrum on the RGB and AGB
G. Dr\'eau, Y. Lebreton, B. Mosser, D. Stello
https://arxiv.org/abs/2510.04955 https://

Physical interpretation of the oscillation spectrum on the RGB and AGB
The high-frequency resolution of the four-year $\textit{Kepler}$ time series allows detailed study of seismic modes in luminous giants. Seismic observables help infer interior structures via comparisons with stellar models. We aim to investigate differences between H-shell (Red-Giant Branch; RGB) and He-burning (red clump and Asymptotic-Giant Branch; AGB) stars in the He-II ionisation zone and the sensitivity of seismic parameters to input physics in stellar models. We used a grid of stellar mo…

Bilinear relational structure fixes reversal curse and enables consistent model editing
Dong-Kyum Kim, Minsung Kim, Jea Kwon, Nakyeong Yang, Meeyoung Cha
https://arxiv.org/abs/2509.21993

Bilinear relational structure fixes reversal curse and enables consistent model editing
The reversal curse -- a language model's (LM) inability to infer an unseen fact ``B is A'' from a learned fact ``A is B'' -- is widely considered a fundamental limitation. We show that this is not an inherent failure but an artifact of how models encode knowledge. By training LMs from scratch on a synthetic dataset of relational knowledge graphs, we demonstrate that bilinear relational structure emerges in their hidden representations. This structure substantially alleviates the reversal curse,…

Cosmology Likelihood for Observables in \Euclid (CLOE). 1. Theoretical recipe
Collaboration, Cardone, Joudaki, Blot, Bonici, Camera, Ca\~nas-Herrera, Carrilho, Casas, Davini, Di Domizio, Farrens, Goh, Beauchamps, Ili\'c, Keil, Le Brun, Martinelli, Moretti, Pettorino, Pezzotta, S\'anchez, Sakr, Sciotti, Tanidis, Tutusaus, Ajani, Crocce, Giocoli, Legrand, Lembo, Lesci, Girones, Nouri-Zonoz, Pamuk, Tsedrik, Bel, Carbone, Duncan, Kilbinger, Lacasa, Lattanzi, Sapone, Sellentin, Tayl…

Cosmology Likelihood for Observables in \Euclid (CLOE). 1. Theoretical recipe
As the statistical precision of cosmological measurements increases, the accuracy of the theoretical description of these measurements needs to increase correspondingly in order to infer the underlying cosmology that governs the Universe. To this end, we have created the Cosmology Likelihood for Observables in Euclid (CLOE), which is a novel cosmological parameter inference pipeline developed within the Euclid Consortium to translate measurements and covariances into cosmological parameter cons…

Constraining Active Galactic Nucleus Jets with Spectrum and Core Shift: The Case of M87
Kouichi Hirotani, Hsien Shang, Ruben Krasnopolsky, Satoki Matsushita, Britton Jeter, Keiichi Asada
https://arxiv.org/abs/2508.06158

Constraining Active Galactic Nucleus Jets with Spectrum and Core Shift: The Case of M87
We analytically model stationary and axisymmetric active galactic nucleus jets, assuming energy conservation along each magnetic flux tube. Using very-long-baseline interferometry (VLBI) observations and published general relativistic magnetohydrodynamic simulations, we constrain the evolution of the bulk Lorentz factor, the magnetization parameter, and the magnetic field strength along the jet. We then infer the electron density, emission coefficient, and absorption coefficient at each point, …

Strata-Sword: A Hierarchical Safety Evaluation towards LLMs based on Reasoning Complexity of Jailbreak Instructions
Shiji Zhao, Ranjie Duan, Jiexi Liu, Xiaojun Jia, Fengxiang Wang, Cheng Wei, Ruoxi Cheng, Yong Xie, Chang Liu, Qing Guo, Jialing Tao, Hui Xue, Xingxing Wei
https://arxiv.org/abs/2509.01444…

Strata-Sword: A Hierarchical Safety Evaluation towards LLMs based on Reasoning Complexity of Jailbreak Instructions
Large language models (LLMs) have gained widespread recognition for their superior comprehension and have been deployed across numerous domains. Building on Chain-of-Thought (CoT) ideology, Large Reasoning models (LRMs) further exhibit strong reasoning skills, enabling them to infer user intent more accurately and respond appropriately. However, both LLMs and LRMs face the potential safety risks under jailbreak attacks, which raise concerns about their safety capabilities. Current safety evalua…

Nonmodal growth and optimal perturbations in magnetohydrodynamic shear flows
Adrian E. Fraser, Alexis K. Kaminski, Jeffrey S. Oishi
https://arxiv.org/abs/2510.03141 https://

Nonmodal growth and optimal perturbations in magnetohydrodynamic shear flows
In astrophysical shear flows, the Kelvin-Helmholtz (KH) instability is generally suppressed by magnetic tension provided a sufficiently strong streamwise magnetic field. This is often used to infer upper (or lower) bounds on field strengths in systems where shear-driven fluctuations are (or are not) observed, on the basis that fluctuations cannot grow in the absence of linear instability. On the contrary, by calculating the maximum growth that small-amplitude perturbations can achieve in finite…

Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks
Luke Snow, Vikram Krishnamurthy
https://arxiv.org/abs/2508.01445 https://

Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks
Suppose there is an adversarial UAV network being tracked by a radar. How can the radar determine whether the UAVs are coordinating, in some well-defined sense? How can the radar infer the objectives of the individual UAVs and the network as a whole? We present an abstract interpretation of such a strategic interaction, allowing us to conceptualize coordination as a linearly constrained multi-objective optimization problem. Then, we present some tools from microeconomic theory that allow us to …

Likelihood-based inference for the Gompertz model with Poisson errors
Paolo Onorati, Sofia Ruiz-Suarez, Radu Craiu
https://arxiv.org/abs/2510.06787 https://

Likelihood-based inference for the Gompertz model with Poisson errors
Population dynamics models play an important role in a number of fields, such as actuarial science, demography, and ecology. Statistical inference for these models can be difficult when, in addition to the process' inherent stochasticity, one also needs to account for sampling error. Ignoring the latter can lead to biases in the estimation, which in turn can produce erroneous conclusions about the system's behavior. The Gompertz model is widely used to infer population size dynamics, but a full…

LLMs and their Limited Theory of Mind: Evaluating Mental State Annotations in Situated Dialogue
Katharine Kowalyshyn, Matthias Scheutz
https://arxiv.org/abs/2509.02292 https://

LLMs and their Limited Theory of Mind: Evaluating Mental State Annotations in Situated Dialogue
What if large language models could not only infer human mindsets but also expose every blind spot in team dialogue such as discrepancies in the team members' joint understanding? We present a novel, two-step framework that leverages large language models (LLMs) both as human-style annotators of team dialogues to track the team's shared mental models (SMMs) and as automated discrepancy detectors among individuals' mental states. In the first step, an LLM generates annotations by identifying SMM…

Generation and annotation of item usage scenarios in e-commerce using large language models
Madoka Hagiri, Kazushi Okamoto, Koki Karube, Kei Harada, Atsushi Shibata
https://arxiv.org/abs/2510.07885

Generation and annotation of item usage scenarios in e-commerce using large language models
Complementary recommendations suggest combinations of useful items that play important roles in e-commerce. However, complementary relationships are often subjective and vary among individuals, making them difficult to infer from historical data. Unlike conventional history-based methods that rely on statistical co-occurrence, we focus on the underlying usage context that motivates item combinations. We hypothesized that people select complementary items by imagining specific usage scenarios an…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/11]:
- Learning to Infer Unseen Single-/Multi-Attribute-Object Compositions with Graph Networks
Hui Chen, Jingjing Jiang, Nanning Zheng

RUBIES spectroscopically confirms the high number density of quiescent galaxies from $\mathbf{2<z<5}$
Yunchong Zhang, Anna de Graaff, David J. Setton, Sedona H. Price, Rachel Bezanson, Claudia del P. Lagos, Sam E. Cutler, Ian McConachie, Nikko J. Cleri, Olivia R. Cooper, Rashmi Gottumukkala, Jenny E. Greene, Michaela Hirschmann, Gourav Khullar, Ivo Labbe, Joel Leja, Michael V. Maseda, Jorryt Matthee, Tim B. Miller, Themiya Nanayakkara, Katherine A. Suess, Bingjie Wang, Katherine …

RUBIES spectroscopically confirms the high number density of quiescent galaxies from $\mathbf{2
We present the number density of massive ($ \mathrm{ log (M_{*}/M_{\odot}) > 10.3} $) quiescent galaxies at $2