Tootfinder

Trump's tactics against the media resemble approaches used by authoritarian leaders in other countries, such as Hungary and Serbia, to curtail critical coverage (Associated Press)
https://apnews.com/article/trump-dictators-medi…

Trump's attacks on media echo authoritarians who silence dissent
Since taking office in January, President Donald Trump has waged an aggressive campaign against the media unlike any in modern U.S. history, making moves similar to those of authoritarian leaders that he has often praised. On Wednesday, Trump cheered ABC’s suspension of Jimmy Kimmel’s late-night show after the comedian made remarks about the assassination of conservative activist Charlie Kirk that criticized the president’s MAGA movement. It was the latest in a string of attacks against n…

Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology
Mingzhe Hu, Zach Eidex, Shansong Wang, Mojtaba Safari, Qiang Li, Xiaofeng Yang
https://arxiv.org/abs/2508.13192

Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology
Radiology, radiation oncology, and medical physics require decision-making that integrates medical images, textual reports, and quantitative data under high-stakes conditions. With the introduction of GPT-5, it is critical to assess whether recent advances in large multimodal models translate into measurable gains in these safety-critical domains. We present a targeted zero-shot evaluation of GPT-5 and its smaller variants (GPT-5-mini, GPT-5-nano) against GPT-4o across three representative task…

Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning
Beinuo Yang, Qishen Zhou, Junyi Li, Xingchen Su, Simon Hu
https://arxiv.org/abs/2508.14410 h…

Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning
Optimization Modeling (OM) is essential for solving complex decision-making problems. However, the process remains time-consuming and error-prone, heavily relying on domain experts. While Large Language Models (LLMs) show promise in addressing these challenges through their natural language understanding and reasoning capabilities, current approaches face three critical limitations: high benchmark labeling error rates reaching up to 42\%, narrow evaluation scope that only considers optimal valu…

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…

DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality
Qitong Chu, Yufeng Yue, Danya Yao, Huaxin Pei
https://arxiv.org/abs/2508.11514

DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality
The growing deployment of decision-making agents in dynamic environments increases the demand for safety verification. While critical testing scenario generation has emerged as an appealing verification methodology, effectively balancing diversity and criticality remains a key challenge for existing methods, particularly due to local optima entrapment in high-dimensional scenario spaces. To address this limitation, we propose a dual-space guided testing framework that coordinates scenario param…

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…

NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
Kai Zhang, Eric Lucet, Julien Alexandre Dit Sandretto, Shoubin Chen, David Filait
https://arxiv.org/abs/2509.12723

NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the correspo…

Security Analysis of Web Applications Based on Gruyere
Yonghao Ni, Zhongwen Li, Xiaoqi Li
https://arxiv.org/abs/2509.14706 https://arxiv.org/pdf/2509.14706…

Security Analysis of Web Applications Based on Gruyere
With the rapid development of Internet technologies, web systems have become essential infrastructures for modern information exchange and business operations. However, alongside their expansion, numerous security vulnerabilities have emerged, making web security a critical research focus within the broader field of cybersecurity. These issues are closely related to data protection, privacy preservation, and business continuity, and systematic research on web security is crucial for mitigating …

AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling
Qijun Zhang, Yao Lu, Mengming Li, Zhiyao Xie
https://arxiv.org/abs/2508.12294 https://

AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling
Power efficiency is a critical design objective in modern CPU design. Architects need a fast yet accurate architecture-level power evaluation tool to perform early-stage power estimation. However, traditional analytical architecture-level power models are inaccurate. The recently proposed machine learning (ML)-based architecture-level power model requires sufficient data from known configurations for training, making it unrealistic. In this work, we propose AutoPower targeting fully automated…

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn, Rolf Findeisen
https://arxiv.org/abs/2508.12738

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hierarchical Bayesian optimization (BO) framework that is tailored to efficient controller parameter learning in sequential decision-making and control scenarios for distinct tasks. Instead of treating the closed-loop cost as a black-box, our method exploits structural knowledge of the underlying problem, consisting of a…

AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Yifan Liu, Wenkuan Zhao, Shanshan Zhong, Jinghui Qin, Mingfu Liang, Zhongzhan Huang, Wushao Wen
https://arxiv.org/abs/2509.14171

AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). Among the essential capabilities required for AGI, creativity has emerged as a critical trait for MLLMs, with association serving as its foundation. Association reflects a model' s ability to think creatively, making it vital to evaluate and understand. While several frameworks have been proposed to assess associative abi…

Consistency analysis and nuclear data validation for two series of beryllium reflector critical benchmark experiments
Shengli Chen, Tianxiang Wang
https://arxiv.org/abs/2507.12757

Consistency analysis and nuclear data validation for two series of beryllium reflector critical benchmark experiments
Neutron-induced nuclear reaction data on beryllium playing a crucial role in nuclear application. However, discrepancies have been observed in two closely related series of beryllium-reflector fast-spectrum critical benchmark experiments, HMF-058 and HMF-066, which are widely used in current nuclear data validation. In this work, we address these inconsistencies by improving the secondary angular distributions of the (n,n) and (n,2n) reactions of beryllium, thereby making the theoretical calcul…

Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review
Emmanuel Adjei Domfeh, Christopher L. Dancy
https://arxiv.org/abs/2509.12034 https://…

Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review
In high-stakes disaster scenarios, timely and informed decision-making is critical yet often challenged by uncertainty, dynamic environments, and limited resources. This paper presents a systematic review of Human-AI collaboration patterns that support decision-making across all disaster management phases. Drawing from 51 peer-reviewed studies, we identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Trainin…

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…

Confirmation Bias as a Cognitive Resource in LLM-Supported Deliberation
Sander de Jong, Rune M{\o}berg Jacobsen, Niels van Berkel
https://arxiv.org/abs/2509.14824 https://

Confirmation Bias as a Cognitive Resource in LLM-Supported Deliberation
Large language models (LLMs) are increasingly used in group decision-making, but their influence risks fostering conformity and reducing epistemic vigilance. Drawing on the Argumentative Theory of Reasoning, we argue that confirmation bias, often seen as detrimental, can be harnessed as a resource when paired with critical evaluation. We propose a three-step process in which individuals first generate ideas independently, then use LLMs to refine and articulate them, and finally engage with LLMs…

The collapse of a critical Atlantic current can no longer be considered a low-likelihood event, a study has concluded, making deep cuts to fossil fuel emissions even more urgent to avoid the catastrophic impact.
https://www.theguardian.co…

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
Kevin T Webster
https://arxiv.org/abs/2507.11548 https:/…

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
The increasing use of generative AI for resume screening is predicated on the assumption that it offers an unbiased alternative to biased human decision-making. However, this belief fails to address a critical question: are these AI systems fundamentally competent at the evaluative tasks they are meant to perform? This study investigates the question of competence through a two-part audit of eight major AI platforms. Experiment 1 confirmed complex, contextual racial and gender biases, with some…

Log Gaussian Cox Process Background Modeling in High Energy Physics
Yuval Frid, Liron Barak, Pavani Jairam, Michael Kagan, Rachel Jordan Hyneman
https://arxiv.org/abs/2508.11740

Log Gaussian Cox Process Background Modeling in High Energy Physics
Background modeling is one of the most critical components in high energy physics data analyses, and for smooth backgrounds it is often performed by fitting using an analytic functional form. In this paper a novel method based on Log Gaussian Cox Processes (LGCP) is introduced to model smooth backgrounds while making minimal assumptions on the underlying shape. In LGCP, samples are assumed to be drawn from a non-homogeneous Poisson process, with an intensity function drawn from a Gaussian proce…

DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model
Jingkai Xu, De Cheng, Xiangqian Zhao, Jungang Yang, Zilong Wang, Xinyang Jiang, Xufang Luo, Lili Chen, Xiaoli Ning, Chengxu Li, Xinzhu Zhou, Xuejiao Song, Ang Li, Qingyue Xia, Zhou Zhuang, Hongfei Ouyang, Ke Xue, Yujun Sheng, Rusong Meng, Feng Xu, Xi Yang, Weimin Ma, Yusheng Lee, Dongsheng Li, Xinbo Gao, Jianming Liang, Lili Qiu, Nannan Wang, Xianbo Zuo, Cui Yong

DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model
Skin diseases impose a substantial burden on global healthcare systems, driven by their high prevalence (affecting up to 70% of the population), complex diagnostic processes, and a critical shortage of dermatologists in resource-limited areas. While artificial intelligence(AI) tools have demonstrated promise in dermatological image analysis, current models face limitations-they often rely on large, manually labeled datasets and are built for narrow, specific tasks, making them less effective in…

Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems
Florian Bayer, Maximilian Russo, Christian Rathgeb
https://arxiv.org/abs/2508.11419

Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems
Biometric recognition is widely used, making the privacy and security of extracted templates a critical concern. Biometric Template Protection schemes, especially those utilizing Homomorphic Encryption, introduce significant computational challenges due to increased workload. Recent advances in deep neural networks have enabled state-of-the-art feature extraction for face, fingerprint, and iris modalities. The ubiquity and affordability of biometric sensors further facilitate multi-modal fusion…

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Kasper Lien Oftebro, Anh Nguyen-Duc, Kai-Kristian Kemell
https://arxiv.org/abs/2507.10753

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Effective backlog management is critical for ensuring that development teams remain aligned with evolving requirements and stakeholder expectations. However, as product backlogs consistently grow in scale and complexity, they tend to become cluttered with redundant, outdated, or poorly defined tasks, complicating prioritization and decision making processes. This study investigates whether a generative-AI (GenAI) assistant can automate backlog grooming in Agile software projects without sacrifi…

Ukraine hits Russian electronic warfare facility making Shahed, Iskander components, General Staff says: https://benborges.xyz/2025/07/05/ukraine-hits-russian-electronic-warfare.html

The collapse of a critical Atlantic current can no longer be considered a low-likelihood event, a study has concluded,
making deep cuts to fossil fuel emissions even more urgent to avoid the catastrophic impact.
The Atlantic meridional overturning circulation ( #Amoc ) is a major part of the global climate system.
It brings sun-warmed tropical water to Europe and the Arctic, where it cools a…

Privacy-Driven Network Data for Smart Cities
T\^ania Carvalho, Jos\'e Barata, Henish Balu, Filipa Moreira, Jo\~ao Bastos, Lu\'is Antunes
https://arxiv.org/abs/2509.12403

Privacy-Driven Network Data for Smart Cities
A smart city is essential for sustainable urban development. In addition to citizen engagement, a smart city enables connected infrastructure, data-driven decision making and smart mobility. For most of these features, network data plays a critical role, particularly from public Wi-Fi infrastructures, where cities can benefit from optimized services such as public transport management and the safety and efficiency of large events. One of the biggest concerns in developing a smart city is using …

Weekend Reads
* PQC for the RPKI https://labs.ripe.net/author/dirk/pqc-for-the-rpki/
* Big tech in Taiwan https://

PQC for the RPKI
Future capabilities of quantum attackers will present a host of new vulnerabilities for RPKI. A research student from SIDN Labs presents the first work on post-quantum cryptography for the RPKI, establishing the foundation for making this critical Internet infrastructure quantum-safe.

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Chan Young Park, Jillian Fisher, Marius Memmel, Dipika Khullar, Andy Yun, Abhishek Gupta, Yejin Choi
https://arxiv.org/abs/2507.08224

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic execution. Vision-language models (VLMs) offer a path toward more perceptually grounded plans, but current methods either rely on expensive, large-scale models or are constrained to narrow simulation settings. We introduce SelfReVision, a lightweight and scalable self-improvement framework for vision-language procedural …

VisMoDAl: Visual Analytics for Evaluating and Improving Corruption Robustness of Vision-Language Models
Huanchen Wang, Wencheng Zhang, Zhiqiang Wang, Zhicong Lu, Yuxin Ma
https://arxiv.org/abs/2509.14571

VisMoDAl: Visual Analytics for Evaluating and Improving Corruption Robustness of Vision-Language Models
Vision-language (VL) models have shown transformative potential across various critical domains due to their capability to comprehend multi-modal information. However, their performance frequently degrades under distribution shifts, making it crucial to assess and improve robustness against real-world data corruption encountered in practical applications. While advancements in VL benchmark datasets and data augmentation (DA) have contributed to robustness evaluation and improvement, there remai…

Science submits CE mark application for PRIMA retinal implant – A critical step towards making it available to patients in Europe #AMD

Science Submits CE Mark Application for PRIMA Retinal Implant – A Critical Step Towards Making It Available To Patients | Science Corporation
Science Corporation is a clinical-stage medical technology company.

Causality and Decision-making: A Logical Framework for Systems and Security Modelling
Pinaki Chakraborty, Tristan Caulfield, David Pym
https://arxiv.org/abs/2508.01758 https://

Causality and Decision-making: A Logical Framework for Systems and Security Modelling
Causal reasoning is essential for understanding decision-making about the behaviour of complex `ecosystems' of systems that underpin modern society, with security -- including issues around correctness, safety, resilience, etc. -- typically providing critical examples. We present a theory of strategic reasoning about system modelling based on minimal structural assumptions and employing the methods of transition systems, supported by a modal logic of system states in the tradition of van Benthe…

Kevin: Multi-Turn RL for Generating CUDA Kernels
Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti
https://arxiv.org/abs/2507.11948 https:/…

Kevin: Multi-Turn RL for Generating CUDA Kernels
Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiable rewards like correctness and speedup, making it a natural environment to apply Reinforcement Learning (RL). To explicitly incorporate the iterative nature of this process into training, we develop a flexible multi-turn RL recipe that addresses unique challenges encountered in…

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Chandana Cheerla
https://arxiv.org/abs/2507.12425 https://

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Organizations increasingly rely on proprietary enterprise data, including HR records, structured reports, and tabular documents, for critical decision-making. While Large Language Models (LLMs) have strong generative capabilities, they are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats. Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data.…

LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan, Burak Kantarci, Emil Janulewicz
https://arxiv.org/abs/2507.10903

LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Effective management of Service Function Chains (SFCs) and optimal Virtual Network Function (VNF) placement are critical challenges in modern Software-Defined Networking (SDN) and Network Function Virtualization (NFV) environments. Although Deep Reinforcement Learning (DRL) is widely adopted for dynamic network decision-making, its inherent dependency on structured data and fixed action rules often limits adaptability and responsiveness, particularly under unpredictable network conditions. This…

QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
Fei Xiong, Xiang Zhang, Aosong Feng, Siqi Sun, Chenyu You
https://arxiv.org/abs/2509.09995 https://

QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
Recent advances in Large Language Models (LLMs) have demonstrated impressive capabilities in financial reasoning and market understanding. Multi-agent LLM frameworks such as TradingAgent and FINMEM augment these models to long-horizon investment tasks, leveraging fundamental and sentiment-based inputs for strategic decision-making. However, such systems are ill-suited for the high-speed, precision-critical demands of High-Frequency Trading (HFT). HFT requires rapid, risk-aware decisions based o…

Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers
Guoyue Xu, Renzheng Zhang, Tengfei Luo
https://arxiv.org/abs/2509.05351 ht…

Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers
To overcome the inherent inefficiencies of traditional trial-and-error materials discovery, the scientific community is increasingly developing autonomous laboratories that integrate data-driven decision-making into closed-loop experimental workflows. In this work, we realize this concept for thermoresponsive polymers by developing a low-cost, "frugal twin" platform for the optimization of the lower critical solution temperature (LCST) of poly(N-isopropylacrylamide) (PNIPAM). Our system integra…

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
Kelen C. Teixeira Vivaldini, Robert P\v{e}ni\v{c}ka, Martin Saska
https://arxiv.org/abs/2508.09304 https://

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
One of the most critical features for the successful operation of autonomous UAVs is the ability to make decisions based on the information acquired from their surroundings. Each UAV must be able to make decisions during the flight in order to deal with uncertainties in its system and the environment, and to further act upon the information being received. Such decisions influence the future behavior of the UAV, which is expressed as the path plan. Thus, decision-making in path planning is an e…

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Jeremias Ferrao, Matthijs van der Lende, Ilija Lichkovski, Clement Neo
https://arxiv.org/abs/2509.12934

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Aligning large language models is critical for their usability and safety. However, the prevailing approach of Reinforcement Learning from Human Feedback (RLHF) induces diffuse, opaque parameter changes, making it difficult to discern what the model has internalized. Hence, we introduce Feature Steering with Reinforcement Learning (FSRL), a transparent alignment framework that trains a lightweight adapter to steer behavior by modulating interpretable features from a Sparse Autoencoder (SAE). Fi…

SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations
Amit Jaspal, Kapil Dalwani, Ajantha Ramineni
https://arxiv.org/abs/2508.07241 https://

SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations
Most industry scale recommender systems face critical cold start challenges new items lack interaction history, making it difficult to distribute them in a personalized manner. Standard collaborative filtering models underperform due to sparse engagement signals, while content only approaches lack user specific relevance. We propose SocRipple, a novel two stage retrieval framework tailored for coldstart item distribution in social graph based platforms. Stage 1 leverages the creators social con…

PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era
Xu Zheng, Chenfei Liao, Ziqiao Weng, Kaiyu Lei, Zihao Dongfang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Lu Qi, Li Chen, Danda Pani Paudel, Kailun Yang, Linfeng Zhang, Luc Van Gool, Xuming Hu
https://arxiv.org/abs/2509.12989

PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era
Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vi…

Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction
Yi Lu, Aifan Ling, Chaoqun Wang, Yaxin Xu
https://arxiv.org/abs/2509.10802 https:/…

Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction
In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrat…

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos
https://arxiv.org/abs/2507.12418

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
The Number Theoretic Transform (NTT) is a fundamental operation in privacy-preserving technologies, particularly within fully homomorphic encryption (FHE). The efficiency of NTT computation directly impacts the overall performance of FHE, making hardware acceleration a critical technology that will enable realistic FHE applications. Custom accelerators, in FPGAs or ASICs, offer significant performance advantages due to their ability to exploit massive parallelism and specialized optimizations. …

A test statistic, $h^*$, for outlier analysis
Johan F. Hoorn, Johnny K. W. Ho
https://arxiv.org/abs/2508.06792 https://arxiv.org/pdf/2508.06792

A test statistic, $h^*$, for outlier analysis
Outlier analysis is a critical tool across diverse domains, from clinical decision-making to cybersecurity and talent identification. Traditional statistical outlier detection methods, such as Grubb's test and Dixon's Q, are predicated on the assumption of normality and often fail to reckon the meaningfulness of exceptional values within non-normal datasets. In this paper, we introduce the h* statistic, a novel parametric, frequentist approach for evaluating global outliers without the normalit…

Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
Jianfeng Zhu, Julina Maharjan, Xinyu Li, Karin G. Coifman, Ruoming Jin
https://arxiv.org/abs/2509.13244

Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
Large Language Models (LLMs) are increasingly deployed in roles requiring nuanced psychological understanding, such as emotional support agents, counselors, and decision-making assistants. However, their ability to interpret human personality traits, a critical aspect of such applications, remains unexplored, particularly in ecologically valid conversational settings. While prior work has simulated LLM "personas" using discrete Big Five labels on social media data, the alignment of LLMs with co…

ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva, Shalmali Joshi, No\'emie Elhadad
https://arxiv.org/abs/2509.07108

ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictive performance. However, most of these models do not provide interpretable insights into the association between exposures and the modeled outcomes, a critical requirement for decision-making in clinical practice. To address this limitation, we propose Additive Deep Hazard Analysis Mixtures (ADHAM), an interpretable ad…

Might I suggest that the D-party form a shadow government with shadow agencies to replicate at least the policy making aspects of the gov't that FFOTUS is tearing down.
For example, begin with a medical advisory panel to give us trustworthy policy regarding vaccinations and drugs.
Etc.
It need not be large or expensive, just cherry pick, at least at the start, the most critical places to give the public information that it most needs.

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
https://arxiv.org/abs/2508.10212

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Underground mining operations rely on distributed sensor networks to collect critical data daily, including mine temperature, toxic gas concentrations, and miner movements for hazard detection and operational decision-making. However, transmitting raw sensor data to a central server for training deep learning models introduces significant privacy risks, potentially exposing sensitive mine-specific information. Federated Learning (FL) offers a transformative solution by enabling collaborative mo…

As if overburdened CISOs don't have enough challenges, they also face a central problem in their organizations: their peers don't really know what they do.
Check out my latest CSO piece on this stumbling block and how CISOs can communicate their missions to colleagues.
Thanks to Andy Ellis, Bethany De Lude, Susan Chiang, and Omar Khawaja for their insights.

The CISO’s challenge: Getting colleagues to understand what you do
CISOs often operate with significant responsibility but limited formal authority, making it critical to articulate their role clearly. Experts offer strategies for CISOs to communicate their mission to colleagues and customers.

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach
Lucas Erlacher, Samuel Ruip\'erez-Campillo, Holger Michel, Sven Wellmann, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt
https://arxiv.org/abs/2507.11561

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach
Pulmonary hypertension (PH) in newborns is a critical condition characterized by elevated pressure in the pulmonary arteries, leading to right ventricular strain and heart failure. While right heart catheterization (RHC) is the diagnostic gold standard, echocardiography is preferred due to its non-invasive nature, safety, and accessibility. However, its accuracy highly depends on the operator, making PH assessment subjective. While automated detection methods have been explored, most models foc…

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
N. Jean, G. Le Pera
https://arxiv.org/abs/2509.02388 https://arxiv.org…

Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems
Explainability in AI and ML models is critical for fostering trust, ensuring accountability, and enabling informed decision making in high stakes domains. Yet this objective is often unmet in practice. This paper proposes a general purpose framework that bridges state of the art explainability techniques with Malle's five category model of behavior explanation: Knowledge Structures, Simulation/Projection, Covariation, Direct Recall, and Rationalization. The framework is designed to be applicabl…

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Hamid Rezatofighi, Adel N. Toosi
https://arxiv.org/abs/2507.06011

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Edge computing enables data processing closer to the source, significantly reducing latency an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strateg…

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Phuc Hao Do, Tran Duc Le
https://arxiv.org/abs/2508.04288 https://

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free sim…

Building causation links in stochastic nonlinear systems from data
Sergio Chibbaro, Cyril Furtlehner, Th\'eo Marchetta, Andrei-Tiberiu Pantea, Davide Rossetti
https://arxiv.org/abs/2509.07701

Building causation links in stochastic nonlinear systems from data
Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offerin…

An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Zhihao Zhang, Chengyang Peng, Minghao Zhu, Ekim Yurtsever, Keith A. Redmill
https://arxiv.org/abs/2509.13132

An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Autonomous driving in dense, dynamic environments requires decision-making systems that can exploit both spatial structure and long-horizon temporal dependencies while remaining robust to uncertainty. This work presents a novel framework that integrates multi-channel bird's-eye-view occupancy grids with transformer-based sequence modeling for tactical driving in complex roundabout scenarios. To address the imbalance between frequent low-risk states and rare safety-critical decisions, we propose…

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Asim H. Gazi, Bhanu T. Gullapalli, Daiqi Gao, Benjamin M. Marlin, Vivek Shetty, Susan A. Murphy
https://arxiv.org/abs/2507.10798

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior…

Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
https://arxiv.org/abs/2509.02025

Curiosity-Driven Testing for Sequential Decision-Making Process
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios rema…

Safety-Critical Multi-Agent MCTS for Mixed Traffic Coordination at Unsignalized Roundabout
Zhihao Lin, Shuo Liu, Zhen Tian, Dezong Zhao, Jianglin Lan
https://arxiv.org/abs/2509.01856

Safety-Critical Multi-Agent MCTS for Mixed Traffic Coordination at Unsignalized Roundabout
Decision-making at unsignalized roundabouts poses substantial challenges for autonomous vehicles (AVs), particularly in mixed traffic environments where AVs must coordinate safely with human-driven vehicles (HDVs). This paper presents a safety-critical multi-agent Monte Carlo Tree Search (MCTS) framework that integrates both deterministic and probabilistic prediction models to facilitate cooperative decision-making in complex roundabout scenarios. The proposed framework introduces three key inn…

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Richa Dasila, Vatsala Upadhyay, Samo Bobek, Abhishek Vaish
https://arxiv.org/abs/2508.10652 …

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their decision-making processes difficult to understand, even for their creators. This research addresses these challenges by integrating Explainable AI (XAI) techniques to enhance the interpretability and tr…

The Arctic island of Svalbard is so reliably frigid that humanity bet its future on the place.
Since 2008, the "Svalbard Global Seed Vault" — set deep in frozen soil known as permafrost — has accepted nearly 1.4 million samples of more than 6,000 species of critical crops.
⭐️ But, the island is warming six to seven times faster than the rest of the planet, making even winters freakishly hot -- at least by Arctic standards.
Indeed, in 2017, an access tunnel to the…

Troubling scenes from an Arctic in full-tilt crisis
The heat that hit Svalbard in February was so intense that scientists could dig into the ground with spoons, "like it was soft ice cream."

The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety?
Manuel Herrador
https://arxiv.org/abs/2508.09762 https://

The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety?
As Large Language Models (LLMs) become increasingly autonomous and integrated into critical societal functions, the focus of AI safety must evolve from mitigating harmful content to evaluating underlying behavioral alignment. Current safety benchmarks do not systematically probe a model's decision-making in scenarios where its own instrumental goals - such as self-preservation, resource acquisition, or goal completion - conflict with human safety. This represents a critical gap in our ability t…

Camel: Energy-Aware LLM Inference on Resource-Constrained Devices
Hao Xu, Long Peng, Shezheng Song, Xiaodong Liu, Ma Jun, Shasha Li, Jie Yu, Xiaoguang Mao
https://arxiv.org/abs/2508.09173

Camel: Energy-Aware LLM Inference on Resource-Constrained Devices
Most Large Language Models (LLMs) are currently deployed in the cloud, with users relying on internet connectivity for access. However, this paradigm faces challenges such as network latency, privacy concerns, and bandwidth limits. Thus, deploying LLMs on edge devices has become an important research focus. In edge inference, request latency is critical as high latency can impair real-time tasks. At the same time, edge devices usually have limited battery capacity, making energy consumption ano…

Deep Context-Conditioned Anomaly Detection for Tabular Data
Spencer King, Zhilu Zhang, Ruofan Yu, Baris Coskun, Wei Ding, Qian Cui
https://arxiv.org/abs/2509.09030 https://

Deep Context-Conditioned Anomaly Detection for Tabular Data
Anomaly detection is critical in domains such as cybersecurity and finance, especially when working with large-scale tabular data. Yet, unsupervised anomaly detection -- where no labeled anomalies are available -- remains a significant challenge. Although various deep learning methods have been proposed to model a dataset's joint distribution, real-world tabular data often contain heterogeneous contexts (e.g., different users), making globally rare events normal under certain contexts. Conseque…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2507.03021 htt…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Cyber warfare has become a critical dimension of modern conflict, driven by society's increasing dependence on interconnected digital and physical infrastructure. Effective cyber defense often requires decision-making at different echelons, where the tactical layer focuses on detailed actions such as techniques, tactics, and procedures, while the strategic layer addresses long-term objectives and coordinated planning. Modeling these interactions at different echelons remains challenging due to …

Loss Functions for Detecting Outliers in Panel Data
Charles D. Coleman, Thomas Bryan
https://arxiv.org/abs/2509.07014 https://arxiv.org/pdf/2509.07014

Loss Functions for Detecting Outliers in Panel Data
The detection of outliers is of critical importance in the assurance of data quality. Outliers may exist in observed data or in data derived from these observed data, such as estimates and forecasts. An outlier may indicate a problem with its data generation process or may simply be a true statement about the world. Without making any distributional assumptions, this paper proposes the use of loss functions to detect these outliers in panel data. An unsigned loss function is derived axiomatical…

Influence of Stakeholder Involvement in M&E on the Performance of Donor-Funded Projects in Informal Settlements in Kisumu Central Sub-County, Kisumu County, Kenya
Brighton Savayi Amuni, Stella Silas Karimi, Felistus Mwikali Patrick
https://arxiv.org/abs/2509.05369

Influence of Stakeholder Involvement in M&E on the Performance of Donor-Funded Projects in Informal Settlements in Kisumu Central Sub-County, Kisumu County, Kenya
Stakeholder engagement and participatory approaches influence the effectiveness of donor-funded projects. Participatory Monitoring and Evaluation (PM&E) methodologies ensure that local communities play an active role in decision-making, leading to more sustainable outcomes. Given the complex socio-political landscape of Kisumu Central Sub-County, there is a critical need for inclusive and context-responsive project monitoring strategies. Initiatives that have integrated local leaders, youth gro…

Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
Zhiyi Hu, Siyuan Shen, Tommaso Bonato, Sylvain Jeaugey, Cedell Alexander, Eric Spada, Jeff Hammond, Torsten Hoefler
https://arxiv.org/abs/2507.04786

Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
The NVIDIA Collective Communication Library (NCCL) is a critical software layer enabling high-performance collectives on large-scale GPU clusters. Despite being open source with a documented API, its internal design remains largely opaque. The orchestration of communication channels, selection of protocols, and handling of memory movement across devices and nodes are not well understood, making it difficult to analyze performance or identify bottlenecks. This paper presents a comprehensive anal…

Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems
Maria J. P. Peixoto, Akriti Pandey, Ahsan Zaman, Peter R. Lewis
https://arxiv.org/abs/2508.10806 …

Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems
As AI systems are increasingly deployed to support decision-making in critical domains, explainability has become a means to enhance the understandability of these outputs and enable users to make more informed and conscious choices. However, despite growing interest in the usability of eXplainable AI (XAI), the accessibility of these methods, particularly for users with vision impairments, remains underexplored. This paper investigates accessibility gaps in XAI through a two-pronged approach. …

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Zhonghao Yan, Muxi Diao, Yuxuan Yang, Jiayuan Xu, Kaizhou Zhang, Ruoyan Jing, Lele Yang, Yanxi Liu, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2508.08177

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit spatial hints, making them ill-equipped to handle the implicit queries common in clinical practice. This work makes three core contributions. We first define Unified Medical Reasoning Grounding (UMRG…

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis
Xi Long, Christy Boscardin, Lauren A. Maggio, Joseph A. Costello, Ralph Gonzales, Rasmyah Hammoudeh, Ki Lai, Yoon Soo Park, Brian C. Gin
https://arxiv.org/abs/2508.09458

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis
Knowledge syntheses (literature reviews) are essential to health professions education (HPE), consolidating findings to advance theory and practice. However, they are labor-intensive, especially during data extraction. Artificial Intelligence (AI)-assisted extraction promises efficiency but raises concerns about accuracy, making it critical to distinguish AI 'hallucinations' (fabricated content) from legitimate interpretive differences. We developed an extraction platform using large language m…

From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Jinghui Zhang, Kaiyang Wan, Longwei Xu, Ao Li, Zongfang Liu, Xiuying Chen
https://arxiv.org/abs/2508.00497

From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Public response prediction is critical for understanding how individuals or groups might react to specific events, policies, or social phenomena, making it highly valuable for crisis management, policy-making, and social media analysis. However, existing works face notable limitations. First, they lack micro-level personalization, producing generic responses that ignore individual user preferences. Moreover, they overlook macro-level sentiment distribution and only deal with individual-level se…

MAESTRO: Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization
Hong Liu
https://arxiv.org/abs/2509.08578 https://arxiv.org/pdf/2509.08578

MAESTRO: Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization
Timely and robust influenza incidence forecasting is critical for public health decision-making. To address this, we present MAESTRO, a Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization. MAESTRO achieves robustness by adaptively fusing multi-modal inputs-including surveillance, web search trends, and meteorological data-and leveraging a comprehensive spectro-temporal architecture. The model first decomposes time series into seasonal and trend components. These are then proc…

Explainable Information Retrieval in the Audit Domain
Alexander Frummet, Emanuel Slany, Jonas Amling, Moritz Lang, Stephan Scheele
https://arxiv.org/abs/2507.03479

Explainable Information Retrieval in the Audit Domain
Conversational agents such as Microsoft Copilot and Google Gemini assist users with complex search tasks but often generate misleading or fabricated references. This undermines trust, particularly in high-stakes domains such as medicine and finance. Explainable information retrieval (XIR) aims to address this by making search results more transparent and interpretable. While most XIR research is domain-agnostic, this paper focuses on auditing -- a critical yet underexplored area. We argue that …

Learning From Software Failures: A Case Study at a National Space Research Center
Dharun Anandayuvaraj, Zain Hammadeh, Andreas Lund, Alexandra Holloway, James C. Davis
https://arxiv.org/abs/2509.06301 …

Learning From Software Failures: A Case Study at a National Space Research Center
Software failures can have significant consequences, making learning from failures a critical aspect of software engineering. While software organizations are recommended to conduct postmortems, the effectiveness and adoption of these practices vary widely. Understanding how engineers gather, document, share, and apply lessons from failures is essential for improving reliability and preventing recurrence. High-reliability organizations (HROs) often develop software systems where failures carry …

Structured AI Decision-Making in Disaster Management
Julian Gerald Dcruz, Argyrios Zolotas, Niall Ross Greenwood, Miguel Arana-Catania
https://arxiv.org/abs/2509.01576 https://

Structured AI Decision-Making in Disaster Management
With artificial intelligence (AI) being applied to bring autonomy to decision-making in safety-critical domains such as the ones typified in the aerospace and emergency-response services, there has been a call to address the ethical implications of structuring those decisions, so they remain reliable and justifiable when human lives are at stake. This paper contributes to addressing the challenge of decision-making by proposing a structured decision-making framework as a foundational step towar…

Generative Diffusion Contrastive Network for Multi-View Clustering
Jian Zhu, Xin Zou, Xi Wang, Ning Zhang, Bian Wu, Yao Yang, Ying Zhou, Lingfang Zeng, Chang Tang, Cheng Luo
https://arxiv.org/abs/2509.09527

Generative Diffusion Contrastive Network for Multi-View Clustering
In recent years, Multi-View Clustering (MVC) has been significantly advanced under the influence of deep learning. By integrating heterogeneous data from multiple views, MVC enhances clustering analysis, making multi-view fusion critical to clustering performance. However, there is a problem of low-quality data in multi-view fusion. This problem primarily arises from two reasons: 1) Certain views are contaminated by noisy data. 2) Some views suffer from missing data. This paper proposes a novel…

PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501

PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Existing tool-augmented agentic systems are limited in the real world by (i) black-box reasoning steps that undermine trust of decision-making and pose safety risks, (ii) poor multimodal integration, which is inherently critical for healthcare tasks, and (iii) rigid and computationally inefficient agentic pipelines. We introduce PASS (Probabilistic Agentic Supernet Sampling), the first multimodal framework to address these challenges in the context of Chest X-Ray (CXR) reasoning. PASS adaptivel…

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
Saideep Nannapaneni, Joseph Sakaya, Kyle Caron, Pedro HM Albuquerque, Zaid Tashman
https://arxiv.org/abs/2507.15909

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as it predicts the change in an outcome (usually a key performance indicator) due to a treatment (also called an intervention). To facilitate robust decision making using causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum Likelihood Estimation (…

OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Yuecheng Liu, Dafeng Chi, Shiguang Wu, Zhanguang Zhang, Yuzheng Zhuang, Bowen Yang, He Zhu, Lingfeng Zhang, Pengwei Xie, David Gamaliel Arcos Bravo, Yingxue Zhang, Jianye Hao, Xingyue Quan
https://arxiv.org/abs/2509.09332

OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning
Recent advances in multimodal large language models (MLLMs) have opened new opportunities for embodied intelligence, enabling multimodal understanding, reasoning, and interaction, as well as continuous spatial decision-making. Nevertheless, current MLLM-based embodied systems face two critical limitations. First, Geometric Adaptability Gap: models trained solely on 2D inputs or with hard-coded 3D geometry injection suffer from either insufficient spatial information or restricted 2D generalizat…

Measuring Epistemic Humility in Multimodal Large Language Models
Bingkui Tong, Jiaer Xia, Sifeng Shang, Kaiyang Zhou
https://arxiv.org/abs/2509.09658 https://

Measuring Epistemic Humility in Multimodal Large Language Models
Hallucinations in multimodal large language models (MLLMs) -- where the model generates content inconsistent with the input image -- pose significant risks in real-world applications, from misinformation in visual question answering to unsafe errors in decision-making. Existing benchmarks primarily test recognition accuracy, i.e., evaluating whether models can select the correct answer among distractors. This overlooks an equally critical capability for trustworthy AI: recognizing when none of …

Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models
Meidan Ding, Jipeng Zhang, Wenxuan Wang, Cheng-Yi Li, Wei-Chieh Fang, Hsin-Yu Wu, Haiqin Zhong, Wenting Chen, Linlin Shen
https://arxiv.org/abs/2508.21430

Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models
Multimodal large language models (MLLMs) hold significant potential in medical applications, including disease diagnosis and clinical decision-making. However, these tasks require highly accurate, context-sensitive, and professionally aligned responses, making reliable reward models and judges critical. Despite their importance, medical reward models (MRMs) and judges remain underexplored, with no dedicated benchmarks addressing clinical requirements. Existing benchmarks focus on general MLLM c…

Leveraging large language models for SQL behavior-based database intrusion detection
Meital Shlezinger, Shay Akirav, Lei Zhou, Liang Guo, Avi Kessel, Guoliang Li
https://arxiv.org/abs/2508.05690

Leveraging large language models for SQL behavior-based database intrusion detection
Database systems are extensively used to store critical data across various domains. However, the frequency of abnormal database access behaviors, such as database intrusion by internal and external attacks, continues to rise. Internal masqueraders often have greater organizational knowledge, making it easier to mimic employee behavior effectively. In contrast, external masqueraders may behave differently due to their lack of familiarity with the organization. Current approaches lack the granul…

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
S M Asiful Islam Saky, Ugyen Tshering
https://arxiv.org/abs/2509.07795 https:…

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images
Optical Coherence Tomography (OCT) is essential for diagnosing conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Accurate retinal layer segmentation enables quantitative biomarkers critical for clinical decision-making, but manual segmentation is time-consuming and variable, while conventional deep learning models often lack interpretability. This work proposes an improved SegNet-based deep learning framework for automated and interpretable retinal layer s…

Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
Yuki Takemoto
https://arxiv.org/abs/2509.04921 https://

Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
Time series forecasting plays a critical role in decision-making processes across diverse fields including meteorology, traffic, electricity, economics, finance, and so on. Especially, predicting returns on financial instruments is a challenging problem. Some researchers have proposed time series foundation models applicable to various forecasting tasks. Simultaneously, based on the recognition that real-world time series exhibit chaotic properties, methods have been developed to artificially g…

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
Ray Wai Man Kong
https://arxiv.org/abs/2508.06518 https://arxiv.org/pd…

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanc…

Why it is worth making an effort with GenAI
Yvonne Rogers
https://arxiv.org/abs/2509.00852 https://arxiv.org/pdf/2509.00852

Why it is worth making an effort with GenAI
Students routinely use ChatGPT and the like now to help them with their homework, such as writing an essay. It takes less effort to complete and is easier to do than by hand. It can even produce as good if not better output than the student's own work. However, there is a growing concern that over-reliance on using GenAI in this way will stifle the development of learning writing and critical thinking skills. How might this trend be reversed? What if students were required to make more effort w…

FairLangProc: A Python package for fairness in NLP
Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Rosa E. Lillo
https://arxiv.org/abs/2508.03677 https://

FairLangProc: A Python package for fairness in NLP
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizational justice or healthcare. This, in turn, poses questions about the fairness of these models in critical settings, which leads to the developement of different procedures to address bias in Natural Language Processing. Although many datasets, metrics and algorithms have been proposed to measure and mitigate harmful …

LLMs in Cybersecurity: Friend or Foe in the Human Decision Loop?
Irdin Pekaric, Philipp Zech, Tom Mattson
https://arxiv.org/abs/2509.06595 https://arxiv.or…

LLMs in Cybersecurity: Friend or Foe in the Human Decision Loop?
Large Language Models (LLMs) are transforming human decision-making by acting as cognitive collaborators. Yet, this promise comes with a paradox: while LLMs can improve accuracy, they may also erode independent reasoning, promote over-reliance and homogenize decisions. In this paper, we investigate how LLMs shape human judgment in security-critical contexts. Through two exploratory focus groups (unaided and LLM-supported), we assess decision accuracy, behavioral resilience and reliance dynamics…

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models
Xingyu Xiao, Hongxu Zhu, Jingang Liang, Jiejuan Tong, Haitao Wang
https://arxiv.org/abs/2507.01017

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models
Human error remains a dominant risk driver in safety-critical sectors such as nuclear power, aviation, and healthcare, where seemingly minor mistakes can cascade into catastrophic outcomes. Although decades of research have produced a rich repertoire of mitigation techniques, persistent limitations: scarce high-quality data, algorithmic opacity, and residual reliance on expert judgment, continue to constrain progress. This review synthesizes recent advances at the intersection of risk-informed …

Temporal Counterfactual Explanations of Behaviour Tree Decisions
Tamlin Love, Antonio Andriella, Guillem Aleny\`a
https://arxiv.org/abs/2509.07674 https://…

Temporal Counterfactual Explanations of Behaviour Tree Decisions
Explainability is a critical tool in helping stakeholders understand robots. In particular, the ability for robots to explain why they have made a particular decision or behaved in a certain way is useful in this regard. Behaviour trees are a popular framework for controlling the decision-making of robots and other software systems, and thus a natural question to ask is whether or not a system driven by a behaviour tree is capable of answering "why" questions. While explainability for behaviour…

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Bui Duc Manh, Soumyaratna Debnath, Zetong Zhang, Shriram Damodaran, Arvind Kumar, Yueyi Zhang, Lu Mi, Erik Cambria, Lin Wang
https://arxiv.org/abs/2509.09154

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advan…

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation
Fatimaelzahraa Ahmed, Muraam Abdel-Ghani, Muhammad Arsalan, Mahmoud Ali, Abdulaziz Al-Ali, Shidin Balakrishnan
https://arxiv.org/abs/2507.04304

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation
Holistic surgical scene segmentation in robot-assisted surgery (RAS) enables surgical residents to identify various anatomical tissues, articulated tools, and critical structures, such as veins and vessels. Given the firm intraoperative time constraints, it is challenging for surgeons to provide detailed real-time explanations of the operative field for trainees. This challenge is compounded by the scarcity of expert surgeons relative to trainees, making the unambiguous delineation of go- and n…

Invariant Features for Global Crop Type Classification
Xin-Yi Tong, Sherrie Wang
https://arxiv.org/abs/2509.03497 https://arxiv.org/pdf/2509.03497

Invariant Features for Global Crop Type Classification
Accurately obtaining crop type and its spatial distribution at a global scale is critical for food security, agricultural policy-making, and sustainable development. Remote sensing offers an efficient solution for large-scale crop classification, but the limited availability of reliable ground samples in many regions constrains applicability across geographic areas. To address performance declines under geospatial shifts, this study identifies remote sensing features that are invariant to geogr…

Supply Chain Optimization via Generative Simulation and Iterative Decision Policies
Haoyue Bai, Haoyu Wang, Nanxu Gong, Xinyuan Wang, Wangyang Ying, Haifeng Chen, Yanjie Fu
https://arxiv.org/abs/2507.07355

Supply Chain Optimization via Generative Simulation and Iterative Decision Policies
High responsiveness and economic efficiency are critical objectives in supply chain transportation, both of which are influenced by strategic decisions on shipping mode. An integrated framework combining an efficient simulator with an intelligent decision-making algorithm can provide an observable, low-risk environment for transportation strategy design. An ideal simulation-decision framework must (1) generalize effectively across various settings, (2) reflect fine-grained transportation dynami…

Energy-Efficient Path Planning with Multi-Location Object Pickup for Mobile Robots on Uneven Terrain
Faiza Babakano, Ahmed Fahmin, Bojie Shen, Muhammad Aamir Cheema, Isma Farah Siddiqui
https://arxiv.org/abs/2509.06061

Energy-Efficient Path Planning with Multi-Location Object Pickup for Mobile Robots on Uneven Terrain
Autonomous Mobile Robots (AMRs) operate on battery power, making energy efficiency a critical consideration, particularly in outdoor environments where terrain variations affect energy consumption. While prior research has primarily focused on computing energy-efficient paths from a source to a destination, these approaches often overlook practical scenarios where a robot needs to pick up an object en route - an action that can significantly impact energy consumption due to changes in payload. …

Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning
Zhimeng Luo, Abhibha Gupta, Adam Frisch, Daqing He
https://arxiv.org/abs/2509.01885 htt…

Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning
The extraction of critical patient information from Electronic Health Records (EHRs) poses significant challenges due to the complexity and unstructured nature of the data. Traditional machine learning approaches often fail to capture pertinent details efficiently, making it difficult for clinicians to utilize these tools effectively in patient care. This paper introduces a novel approach to extracting the OPQRST assessment from EHRs by leveraging the capabilities of Large Language Models (LLMs…

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Hyeongchan Ham, Heejin Ahn
https://arxiv.org/abs/2507.05710

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we present a distributionally robust optimization (DRO) framework that accounts for both aleatoric and epistemic perception uncertainties using evidential deep learning (EDL). Our approach introduces a novel…

Towards explainable decision support using hybrid neural models for logistic terminal automation
Riccardo DElia, Alberto Termine, Francesco Flammini
https://arxiv.org/abs/2509.07577

Towards explainable decision support using hybrid neural models for logistic terminal automation
The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, these gains are often offset by the loss of explainability and causal reliability $-$ key requirements in critical decision-making systems. This paper presents a novel framework for interpretable-by-design neural system dynamics modeling that synergizes DL with techniques from Concept-Based Interpretability, Mechanisti…

Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving V2X Communication
Abdullah Al Mamun, Kyle Yates, Antsa Rakotondrafara, Mashrur Chowdhury, Ryann Cartor, Shuhong Gao
https://arxiv.org/abs/2508.02461

Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving V2X Communication
Intelligent Transportation Systems (ITS) fundamentally rely on vehicle-generated data for applications such as congestion monitoring and route optimization, making the preservation of user privacy a critical challenge. Homomorphic Encryption (HE) offers a promising solution by enabling computation on encrypted data without revealing underlying content. This study presents the first real-world experimental evaluation of three post-quantum secure HE schemes, i.e., Brakerski-Fan-Vercauteren (BFV),…

An Appraisal-Based Approach to Human-Centred Explanations
Rukshani Somarathna, Madhawa Perera, Tom Gedeon, Matt Adcock
https://arxiv.org/abs/2508.01388 https://

An Appraisal-Based Approach to Human-Centred Explanations
Explainability remains a critical challenge in artificial intelligence (AI) systems, particularly in high stakes domains such as healthcare, finance, and decision support, where users must understand and trust automated reasoning. Traditional explainability methods such as feature importance and post-hoc justifications often fail to capture the cognitive processes that underlie human decision making, leading to either too technical or insufficiently meaningful explanations. We propose a novel a…

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Yuan Sui, Yanming Zhang, Yi Liao, Yu Gu, Guohua Tang, Zhongqian Sun, Wei Yang, Bryan Hooi
https://arxiv.org/abs/2509.04791

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Large language models (LLMs) excel at processing information reactively but lack the ability to systemically explore hypothetical futures. They cannot ask, "what if we take this action? how will it affect the final outcome" and forecast its potential consequences before acting. This critical gap limits their utility in dynamic, high-stakes scenarios like strategic planning, risk assessment, and real-time decision making. To bridge this gap, we propose WiA-LLM, a new paradigm that equips LLMs wi…

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Weiyu Luo, Chenfeng Xiong
https://arxiv.org/abs/2508.02734 …

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Location-Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult. We raise a research problem: Can we use activity sequences derived from high-quality LBS data to recover incomplete activity sequences at the individual level? This study proposes a new solution, the Variable Selection Network-fused Insertion Transformer (VSNIT), integrating the In…

Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
Tonghe Li, Jixin Liu, Weili Zeng, Hao Jiang
https://arxiv.org/abs/2509.03550 https://

Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method
In the context of continuously rising global air traffic, efficient and safe Conflict Detection and Resolution (CD&R) is paramount for air traffic management. Although Deep Reinforcement Learning (DRL) offers a promising pathway for CD&R automation, existing approaches commonly suffer from a "unimodal bias" in their policies. This leads to a critical lack of decision-making flexibility when confronted with complex and dynamic constraints, often resulting in "decision deadlocks." To overcome thi…