Tootfinder

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…

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…

Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation
Mubaris Nadeem, Madjid Fathi
https://arxiv.org/abs/2510.09223 https…

Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation
In the field of medicine and healthcare, the utilization of medical expertise, based on medical knowledge combined with patients' health information is a life-critical challenge for patients and health professionals. The within-laying complexity and variety form the need for a united approach to gather, analyze, and utilize existing knowledge of medical treatments, and medical operations to provide the ability to present knowledge for the means of accurate patient-driven decision-making. One wa…

CDE: Concept-Driven Exploration for Reinforcement Learning
Le Mao, Andrew H. Liu, Renos Zabounidis, Zachary Kingston, Joseph Campbell
https://arxiv.org/abs/2510.08851 https://…

CDE: Concept-Driven Exploration for Reinforcement Learning
Intelligent exploration remains a critical challenge in reinforcement learning (RL), especially in visual control tasks. Unlike low-dimensional state-based RL, visual RL must extract task-relevant structure from raw pixels, making exploration inefficient. We propose Concept-Driven Exploration (CDE), which leverages a pre-trained vision-language model (VLM) to generate object-centric visual concepts from textual task descriptions as weak, potentially noisy supervisory signals. Rather than direct…

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…

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…

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…

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…

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…

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 …

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…

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…

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…

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…

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…

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…

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…

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…

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 …

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…

Red Lines and Grey Zones in the Fog of War: Benchmarking Legal Risk, Moral Harm, and Regional Bias in Large Language Model Military Decision-Making
Toby Drinkall
https://arxiv.org/abs/2510.03514

Red Lines and Grey Zones in the Fog of War: Benchmarking Legal Risk, Moral Harm, and Regional Bias in Large Language Model Military Decision-Making
As military organisations consider integrating large language models (LLMs) into command and control (C2) systems for planning and decision support, understanding their behavioural tendencies is critical. This study develops a benchmarking framework for evaluating aspects of legal and moral risk in targeting behaviour by comparing LLMs acting as agents in multi-turn simulated conflict. We introduce four metrics grounded in International Humanitarian Law (IHL) and military doctrine: Civilian Tar…

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…

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…

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 …

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…

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.

Beyond Canonical Rounds: Communication Abstractions for Optimal Byzantine Resilience
Hagit Attiya, Itay Flam, Jennifer L. Welch
https://arxiv.org/abs/2510.04310 https://

Beyond Canonical Rounds: Communication Abstractions for Optimal Byzantine Resilience
We study communication abstractions for asynchronous Byzantine fault tolerance with optimal failure resilience, where $n > 3f$. Two classic patterns -- canonical asynchronous rounds and communication-closed layers -- have long been considered as general frameworks for designing distributed algorithms, making asynchronous executions appear synchronous and enabling modular reasoning. We show that these patterns are inherently limited in the critical resilience regime $3f < n \le 5f$. Several ke…

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…

AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
Xiaochong Lan, Jie Feng, Yinxing Liu, Xinlei Shi, Yong Li
https://arxiv.org/abs/2510.08081

AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
Ranking online reviews by their intrinsic quality is a critical task for e-commerce platforms and information services, impacting user experience and business outcomes. However, quality is a domain-dependent and dynamic concept, making its assessment a formidable challenge. Traditional methods relying on hand-crafted features are unscalable across domains and fail to adapt to evolving content patterns, while modern deep learning approaches often produce black-box models that lack interpretabili…

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…

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 former Clemson University professor is claiming his firing over a post about the killing of conservative activist Charlie Kirk was unconstitutional, according to a lawsuit filed in federal court Friday.
Joshua Bregy, an assistant professor in the Department of Environmental Engineering and Earth Sciences,
was one of three employees at Clemson fired for making social media posts critical of Kirk after the activist was shot and killed Sept. 10 during a speaking event at a Utah co…

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…

Why Sean McVay felt 'sick' over failed fourth-down play call in Rams' overtime loss to 49ers

https://www.cbssports.com/nfl/news/why-sea

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.

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…

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…

TeV Emission from PSR B1055-52 with HESS: Evidence for a Pulsar Halo
Tina Wach (for the H.E.S.S. Collaboration), Alison M. W. Mitchell (for the H.E.S.S. Collaboration)
https://arxiv.org/abs/2510.02802 …

TeV Emission from PSR B1055-52 with HESS: Evidence for a Pulsar Halo
Pulsar halos are a recently identified class of TeV $γ$-ray sources, offering valuable insights into the evolution of pulsar systems at the highest energies. However, only a handful of such sources have been detected so far, making each new identification critical for understanding the properties of the population as a whole. We report the first detection of extended very-high-energy (VHE) $γ$-ray emission around PSR~B1055$-$52 using observations from the H.E.S.S. array. This middle-aged puls…

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…

Towards Deterministic Sub-0.5 us Response on Linux through Interrupt Isolation
Zhouyi Zhou, Zhili Liu, Shancong Zhang, Jiemin Li, Dengke Du, Mengke Sun, Zhiqiang Wang, Hongyan Liu, Guogai Xu
https://arxiv.org/abs/2509.03855

Towards Deterministic Sub-0.5 us Response on Linux through Interrupt Isolation
Real-time responsiveness in Linux is often constrained by interrupt contention and timer handling overhead, making it challenging to achieve sub-microsecond latency. This work introduces an interrupt isolation approach that centralizes and minimizes timer interrupt interference across CPU cores. By enabling a dedicated API to selectively invoke timer handling routines and suppress non-critical inter-processor interrupts, our design significantly reduces jitter and response latency. Experiments …

'I'm not afraid to make tough and critical decisions': John Spytek handles his first roster reduction as general manager https://www.raiders.com/news/john-spytek-handles-his-first-roster-reduction-as-general-manager

Observation of universal non-Gaussian statistics of the order parameter across a continuous phase transition
Maxime Allemand, G\'eraud Dupuy, Paul Paquiez, Nicolas Dupuis, Adam Ran\c{c}on, Tommaso Roscilde, Thomas Chalopin, David Cl\'ement
https://arxiv.org/abs/2508.21623

Observation of universal non-Gaussian statistics of the order parameter across a continuous phase transition
Second-order phase transitions are characterised by critical scaling and universality. The singular behaviour of thermodynamic quantities at the transition, in particular, is determined by critical exponents of the universality class of the transition. However, critical properties are also characterised by the probability distribution of order parameter across the transition, where non-Gaussian statistics are expected, but remain largely unexplored. Here, making use of single-atom-resolved dete…

Czech cyber agency warns against using services and products that send data to China https://therecord.media/czech-nukib-warns-against-products-sending-data-china

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…

A Schema.org Mapping for Brazilian Legal Norms: Toward Interoperable Legal Graphs and Open Government Data
Hudson de Martim
https://arxiv.org/abs/2508.00827 https://

A Schema.org Mapping for Brazilian Legal Norms: Toward Interoperable Legal Graphs and Open Government Data
Open Government Data (OGD) initiatives aim to enhance transparency and public participation by making government data openly accessible. However, structuring legal norms for machine readability remains a critical challenge for advancing Legal Tech applications such as Legal Knowledge Graphs (LKGs). Focusing on the Normas.leg.br portal initiative by the Brazilian National Congress, we propose a unified mapping of Brazilian legislation to the schema.org/Legislation vocabulary via JSON-LD and Link…

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…

FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data
Ziting Wang, Shize Zhang, Haitao Yuan, Jinwei Zhu, Shifu Li, Wei Dong, Gao Cong
https://arxiv.org/abs/2509.02473

FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data
The growing demand for data-driven decision-making has created an urgent need for data agents that can integrate structured and unstructured data for analysis. While data agents show promise for enabling users to perform complex analytics tasks, this field still suffers from three critical limitations: first, comprehensive data agent benchmarks remain absent due to the difficulty of designing test cases that evaluate agents' abilities across multi-source analytical tasks; second, constructing r…

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…

Time asymptotics, time regularity and separation rates for Navier-Stokes flows in supercritical solution classes
Zachary Bradshaw, Joshua Hudson
https://arxiv.org/abs/2508.00714

Time asymptotics, time regularity and separation rates for Navier-Stokes flows in supercritical solution classes
This paper extends the weak solution theory for the 3D Navier-Stokes equations of Barker, Seregin and Sverak from a critical setting to a supercritical setting making sure to include a useful a priori energy bound as well as a statement about stability under weak-star convergence. Two applications of the a priori bound are then explored. The first provides a spatially local, short-time asymptotic expansion in the time variable starting at $t=0$ which, as a corollary, provides an upper bound on …

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…

A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory
Qianshan Wei, Tengchao Yang, Yaochen Wang, Xinfeng Li, Lijun Li, Zhenfei Yin, Yi Zhan, Thorsten Holz, Zhiqiang Lin, XiaoFeng Wang
https://arxiv.org/abs/2510.02373

A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory
Large Language Model (LLM) agents use memory to learn from past interactions, enabling autonomous planning and decision-making in complex environments. However, this reliance on memory introduces a critical security risk: an adversary can inject seemingly harmless records into an agent's memory to manipulate its future behavior. This vulnerability is characterized by two core aspects: First, the malicious effect of injected records is only activated within a specific context, making them hard t…

Graph Conditioned Diffusion for Controllable Histopathology Image Generation
Sarah Cechnicka, Matthew Baugh, Weitong Zhang, Mischa Dombrowski, Zhe Li, Johannes C. Paetzold, Candice Roufosse, Bernhard Kainz
https://arxiv.org/abs/2510.07129

Graph Conditioned Diffusion for Controllable Histopathology Image Generation
Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature inherent structure such as consistent spatial arrangement, shape or texture, all of which are critical for diagnosis. However, existing DPMs operate in noisy latent spaces that lack semantic structure and strong priors, making it difficult to ensure meaningf…

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…

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…

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."

LLMs as Policy-Agnostic Teammates: A Case Study in Human Proxy Design for Heterogeneous Agent Teams
Aju Ani Justus, Chris Baber
https://arxiv.org/abs/2510.06151 https://

LLMs as Policy-Agnostic Teammates: A Case Study in Human Proxy Design for Heterogeneous Agent Teams
A critical challenge in modelling Heterogeneous-Agent Teams is training agents to collaborate with teammates whose policies are inaccessible or non-stationary, such as humans. Traditional approaches rely on expensive human-in-the-loop data, which limits scalability. We propose using Large Language Models (LLMs) as policy-agnostic human proxies to generate synthetic data that mimics human decision-making. To evaluate this, we conduct three experiments in a grid-world capture game inspired by Sta…

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
Imad Aouali, Otmane Sakhi
https://arxiv.org/abs/2509.03456 https://ar…

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits. Recent advances in OPL primarily optimize OPE estimators with improved statistical properties, assuming that better estimators inherently yield superior policies. Although theoretically justified, we argue this estimator-centric approach neglects a critical practical obstacle: challenging optimization landscapes. In this paper, we provide theoretical insights and extensi…

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. …

Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake
Farshad Amani, Faezeh Ardali, Amin Kargarian
https://arxiv.org/abs/2509.04308 https://

Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake
Post-disaster crew dispatch is a critical but computationally intensive task. Traditional mixed-integer linear programming methods often require minutes to several hours to compute solutions, leading to delays that hinder timely decision-making in highly dynamic restoration environments. To address this challenge, we propose a novel learning-based framework that integrates transformer architectures with deep reinforcement learning (DRL) to deliver near real-time decision support without comprom…

Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection
Sumin Seo, In Kyu Lee, Hyun-Woo Kim, Jaesik Min, Chung-Hwan Jung
https://arxiv.org/abs/2508.00438 h…

Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection
Coronary stenosis is a major risk factor for ischemic heart events leading to increased mortality, and medical treatments for this condition require meticulous, labor-intensive analysis. Coronary angiography provides critical visual cues for assessing stenosis, supporting clinicians in making informed decisions for diagnosis and treatment. Recent advances in deep learning have shown great potential for automated localization and severity measurement of stenosis. In real-world scenarios, however…

'I'm not afraid to make tough and critical decisions': John Spytek handles his first roster reduction as general manager https://www.raiders.com/news/john-spytek-handles-his-first-roster-reduction-as-general-manager

Algorithm Adaptation Bias in Recommendation System Online Experiments
Chen Zheng, Zhenyu Zhao
https://arxiv.org/abs/2509.00199 https://arxiv.org/pdf/2509.0…

Algorithm Adaptation Bias in Recommendation System Online Experiments
Online experiments (A/B tests) are widely regarded as the gold standard for evaluating recommender system variants and guiding launch decisions. However, a variety of biases can distort the results of the experiment and mislead decision-making. An underexplored but critical bias is algorithm adaptation effect. This bias arises from the flywheel dynamics among production models, user data, and training pipelines: new models are evaluated on user data whose distributions are shaped by the incumbe…

TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Cheng Xin, Fan Xu, Xin Ding, Jie Gao, Jiaxin Ding
https://arxiv.org/abs/2510.05102 https:/…

TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Graph Neural Networks (GNNs) have shown remarkable success across various scientific fields, yet their adoption in critical decision-making is often hindered by a lack of interpretability. Recently, intrinsically interpretable GNNs have been studied to provide insights into model predictions by identifying rationale substructures in graphs. However, existing methods face challenges when the underlying rationale subgraphs are complex and varied. In this work, we propose TopInG: Topologically Int…

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…

RumorSphere: A Framework for Million-scale Agent-based Dynamic Simulation of Rumor Propagation
Yijun Liu, Wu Liu, Xiaoyan Gu, Weiping Wang, Jiebo Luo, Yongdong Zhang
https://arxiv.org/abs/2509.02172

RumorSphere: A Framework for Million-scale Agent-based Dynamic Simulation of Rumor Propagation
Rumor propagation modeling is critical for understanding the dynamics of misinformation spread. Previous models are either overly simplistic or static, making them ineffective for simulating real-world rumor dynamics. In this paper, leveraging the impressive human behavior imitation capabilities of large language models (LLMs), we present a novel dynamic and hierarchical social network simulation framework, which supports simulations with millions of agents. This simulator is used to explore th…

AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documents
Hossein Sholehrasa, Amirhossein Ghanaatian, Doina Caragea, Lisa A. Tell, Jim E. Riviere, Majid Jaberi-Douraki
https://arxiv.org/abs/2510.00039

AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documents
Pharmacokinetics (PK) plays a critical role in drug development and regulatory decision-making for human and veterinary medicine, directly affecting public health through drug safety and efficacy assessments. However, PK data are often embedded in complex, heterogeneous tables with variable structures and inconsistent terminologies, posing significant challenges for automated PK data retrieval and standardization. AutoPK, a novel two-stage framework for accurate and scalable extraction of PK da…

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…

Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy
Himanshu Tripathi, Subash Neupane, Shahram Rahimi, Noorbakhsh Amiri Golilarz, Sudip Mittal, Mohammad Sepehrifar
https://arxiv.org/abs/2508.00916

Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy
This paper addresses the critical challenge of estimating the reliability of an Electric Vehicle (EV) charging systems when facing risks such as overheating, unpredictable, weather, and cyberattacks. Traditional methods for predicting failures often rely on past data or limiting assumptions, making them ineffective for new or less common threats that results in failure. To solve this issue, we utilize the Principle of Maximum Entropy (PME), a statistical tool that estimates risks even with limi…

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),…

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 (…

AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
Jiannan Xu, Gujie Li, Jane Yi Jiang
https://arxiv.org/abs/2509.00462 https://ar…

AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
As generative artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved on both sides of decision-making processes, ranging from hiring to content moderation. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias -- the tendency of LLMs to favor their own generated content -- but its real-world implications …

StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation
Mingyu Liu, Jiuhe Shu, Hui Chen, Zeju Li, Canyu Zhao, Jiange Yang, Shenyuan Gao, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.05057

StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation
A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that learns a highly compressed two-token state representation using a lightweight encoder and a pre-trained Diffusion Transformer (DiT) decoder…

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…

MAGIC-MASK: Multi-Agent Guided Inter-Agent Collaboration with Mask-Based Explainability for Reinforcement Learning
Maisha Maliha, Dean Hougen
https://arxiv.org/abs/2510.00274 ht…

MAGIC-MASK: Multi-Agent Guided Inter-Agent Collaboration with Mask-Based Explainability for Reinforcement Learning
Understanding the decision-making process of Deep Reinforcement Learning agents remains a key challenge for deploying these systems in safety-critical and multi-agent environments. While prior explainability methods like StateMask, have advanced the identification of critical states, they remain limited by computational cost, exploration coverage, and lack of adaptation to multi-agent settings. To overcome these limitations, we propose a mathematically grounded framework, MAGIC-MASK (Multi-Agen…

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…

KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning
Xilin Dang, Kexin Chen, Xiaorui Su, Ayush Noori, I\~naki Arango, Lucas Vittor, Xinyi Long, Yuyang Du, Marinka Zitnik, Pheng Ann Heng
https://arxiv.org/abs/2509.24816

KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning
In clinical practice, physicians refrain from making decisions when patient information is insufficient. This behavior, known as abstention, is a critical safety mechanism preventing potentially harmful misdiagnoses. Recent investigations have reported the application of large language models (LLMs) in medical scenarios. However, existing LLMs struggle with the abstentions, frequently providing overconfident responses despite incomplete information. This limitation stems from conventional abste…

Learning Stability Certificate for Robotics in Real-World Environments
Zhe Shen
https://arxiv.org/abs/2510.03123 https://arxiv.org/pdf/2510.03123

Learning Stability Certificate for Robotics in Real-World Environments
Stability certificates play a critical role in ensuring the safety and reliability of robotic systems. However, deriving these certificates for complex, unknown systems has traditionally required explicit knowledge of system dynamics, often making it a daunting task. This work introduces a novel framework that learns a Lyapunov function directly from trajectory data, enabling the certification of stability for autonomous systems without needing detailed system models. By parameterizing the Lyap…

ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks
Zhaorun Chen, Xun Liu, Mintong Kang, Jiawei Zhang, Minzhou Pan, Shuang Yang, Bo Li
https://arxiv.org/abs/2510.02677

ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks
As vision-language models (VLMs) gain prominence, their multimodal interfaces also introduce new safety vulnerabilities, making the safety evaluation challenging and critical. Existing red-teaming efforts are either restricted to a narrow set of adversarial patterns or depend heavily on manual engineering, lacking scalable exploration of emerging real-world VLM vulnerabilities. To bridge this gap, we propose ARMs, an adaptive red-teaming agent that systematically conducts comprehensive risk ass…

Rapid Modeling Architecture for Lightweight Simulator to Accelerate and Improve Decision Making for Industrial Systems
Takumi Kato, Zhi Li Hu
https://arxiv.org/abs/2507.17990 ht…

Rapid Modeling Architecture for Lightweight Simulator to Accelerate and Improve Decision Making for Industrial Systems
Designing industrial systems, such as building, improving, and automating distribution centers and manufacturing plants, involves critical decision-making with limited information in the early phases. The lack of information leads to less accurate designs of the systems, which are often difficult to resolve later. It is effective to use simulators to model the designed system and find out the issues early. However, the modeling time required by conventional simulators is too long to allow for r…

Morae: Proactively Pausing UI Agents for User Choices
Yi-Hao Peng, Dingzeyu Li, Jeffrey P. Bigham, Amy Pavel
https://arxiv.org/abs/2508.21456 https://arxiv…

Morae: Proactively Pausing UI Agents for User Choices
User interface (UI) agents promise to make inaccessible or complex UIs easier to access for blind and low-vision (BLV) users. However, current UI agents typically perform tasks end-to-end without involving users in critical choices or making them aware of important contextual information, thus reducing user agency. For example, in our field study, a BLV participant asked to buy the cheapest available sparkling water, and the agent automatically chose one from several equally priced options, wit…

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
Aadarsh Anantha Ramakrishnan, Shubham Agarwal, Selvanayagam S, Kunwar Singh
https://arxiv.org/abs/2510.01967

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals - making them unsuitable for secure and …

Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database
Diego Russo, Gian Marco Orlando, Valerio La Gatta, Vincenzo Moscato
https://arxiv.org/abs/2507.23669

Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database
Artificial Intelligence (AI) systems are transforming critical sectors such as healthcare, finance, and transportation, enhancing operational efficiency and decision-making processes. However, their deployment in high-stakes domains has exposed vulnerabilities that can result in significant societal harm. To systematically study and mitigate these risk, initiatives like the AI Incident Database (AIID) have emerged, cataloging over 3,000 real-world AI failure reports. Currently, associating a ne…

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…

ControlMed: Adding Reasoning Control to Medical Language Model
Sung-Min Lee, Siyoon Lee, Juyeon Kim, Kyungmin Roh
https://arxiv.org/abs/2507.22545 https://…

ControlMed: Adding Reasoning Control to Medical Language Model
Reasoning Large Language Models (LLMs) with enhanced accuracy and explainability are increasingly being adopted in the medical domain, as the life-critical nature of clinical decision-making demands reliable support. Despite these advancements, existing reasoning LLMs often generate unnecessarily lengthy reasoning processes, leading to significant computational overhead and response latency. These limitations hinder their practical deployment in real-world clinical environments. To address thes…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
Raju Ningappa Mulawade, Christoph Garth, Alexander Wiebel
https://arxiv.org/abs/2507.22020 https://…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
We propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this method, we propose a novel point-shifting mechanism to introduce perturbations in point cloud data. Recently, AI has seen an exponential growth. Hence, it is important to understand the decision-making process of AI algorithms when they are applied in critical areas. Our work focuses on explaining AI algorithms that class…

Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov
https://arxiv.org/abs/2509.26610

Uncertainty Quantification for Regression using Proper Scoring Rules
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantification (UQ) theory has advanced significantly, building on a firm basis of learning with proper scoring rules. However, these advances were focused on classification, while extending these ideas to regression remains challenging. In this work, we introduce a unified UQ framework for regression based on proper scoring r…

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…

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment
Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha
https://arxiv.org/abs/2507.18631

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment
With rapid advancement and increasing accessibility of LLMs, fine-tuning aligned models has become a critical step for adapting them to real-world applications, which makes the safety of this fine-tuning process more important than ever. However, recent studies have highlighted a critical challenge: even when fine-tuning with seemingly benign downstream datasets, the safety of aligned LLMs can be compromised, making them more susceptible to malicious instructions. In this paper, we show that fi…

fev-bench: A Realistic Benchmark for Time Series Forecasting
Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen, Lorenzo Stella, Nick Erickson, Pablo Guerron, Michael Bohlke-Schneider, Yuyang Wang
https://arxiv.org/abs/2509.26468

fev-bench: A Realistic Benchmark for Time Series Forecasting
Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly given the recent rise of pretrained models. Existing benchmarks often have narrow domain coverage or overlook important real-world settings, such as tasks with covariates. Additionally, their aggregation procedures often lack statistical rigor, making it unclear whether observed performance differences reflect true improvements or random variation. Many benchmarks also fail to…

Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety
Wenxiao Zhang, Xiangrui Kong, Conan Dewitt, Thomas Br\"aunl, Jin B. Hong
https://arxiv.org/abs/2509.02163

Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safety
Integrating large language models (LLMs) into robotic systems has revolutionised embodied artificial intelligence, enabling advanced decision-making and adaptability. However, ensuring reliability, encompassing both security against adversarial attacks and safety in complex environments, remains a critical challenge. To address this, we propose a unified framework that mitigates prompt injection attacks while enforcing operational safety through robust validation mechanisms. Our approach combin…

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…

FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
Yuzhen Long, Songze Li
https://arxiv.org/abs/2509.24408 https://arx…

FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models (LLMs), where specialized agents collaborate to perceive, reason, and plan. A key component of these systems is the shared function library, a collection of software tools that agents use to process sensor data and navigate complex driving environments. Despite its critical role in agent decision-making, the function library remains an under-explored vulnerability. In this paper, we introd…

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…

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…

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 …

Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework
Jovan Stojkovic, Chaojie Zhang, \'I\~nigo Goiri, Ricardo Bianchini
https://arxiv.org/abs/2509.26534 https:…

Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework
The rapid rise of large language models (LLMs) has been driving an enormous demand for AI inference infrastructure, mainly powered by high-end GPUs. While these accelerators offer immense computational power, they incur high capital and operational costs due to frequent upgrades, dense power consumption, and cooling demands, making total cost of ownership (TCO) for AI datacenters a critical concern for cloud providers. Unfortunately, traditional datacenter lifecycle management (designed for gen…

DS-STAR: Data Science Agent via Iterative Planning and Verification
Jaehyun Nam, Jinsung Yoon, Jiefeng Chen, Jinwoo Shin, Tomas Pfister
https://arxiv.org/abs/2509.21825 https://…

DS-STAR: Data Science Agent via Iterative Planning and Verification
Data science, which transforms raw data into actionable insights, is critical for data-driven decision-making. However, these tasks are often complex, involving steps for exploring multiple data sources and synthesizing findings to deliver insightful answers. While large language models (LLMs) show significant promise in automating this process, they often struggle with heterogeneous data formats and generate sub-optimal analysis plans, as verifying plan sufficiency is inherently difficult with…

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…

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Yuqicheng Zhu, Nico Potyka, Daniel Hern\'andez, Yuan He, Zifeng Ding, Bo Xiong, Dongzhuoran Zhou, Evgeny Kharlamov, Steffen Staab
https://arxiv.org/abs/2508.20131

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose ArgRAG, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). ArgRAG constructs a QBAF from retrieved documents a…