Tootfinder

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
Zinan Tang, Xin Gao, Qizhi Pei, Zhuoshi Pan, Mengzhang Cai, Jiang Wu, Conghui He, Lijun Wu
https://arxiv.org/abs/2508.21589

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset curation that fail to adapt to evolving model capabilities. In this paper, we introduce Middo, a self-evolving Model-informed dynamic data optimization framework that uses model-aware data selection and context-preserving data refinement. U…

GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs
Aryan Yazdan Parast, Parsa Hosseini, Hesam Asadollahzadeh, Arshia Soltani Moakhar, Basim Azam, Soheil Feizi, Naveed Akhtar
https://arxiv.org/abs/2509.25178

GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs
Object hallucination in Multimodal Large Language Models (MLLMs) is a persistent failure mode that causes the model to perceive objects absent in the image. This weakness of MLLMs is currently studied using static benchmarks with fixed visual scenarios, which preempts the possibility of uncovering model-specific or unanticipated hallucination vulnerabilities. We introduce GHOST (Generating Hallucinations via Optimizing Stealth Tokens), a method designed to stress-test MLLMs by actively generati…

Temporal-Aware Iterative Speech Model for Dementia Detection
Chukwuemeka Ugwu, Oluwafemi Oyeleke
https://arxiv.org/abs/2510.00030 https://arxiv.org/pdf/251…

Temporal-Aware Iterative Speech Model for Dementia Detection
Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this pap…

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs
Erik Faust, Lisa Scheunemann
https://arxiv.org/abs/2508.21527

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs
In a recent work, we proposed a graph-based manifold learning scheme for the nonlinear Galerkin-reduction of quasi-static solid mechanical problems [1]. The resulting nonlinear approximation spaces can closely and flexibly represent nonlinear solution manifolds. The present work discusses how this nonlinear model order reduction (MOR) approach can be employed to reduce online computational costs by multiple orders of magnitude while retaining high levels of accuracy. We integrate two popular hy…

DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao, Weipu Zhang, Jian Sun, Gao Huang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.24804 …

DyMoDreamer: World Modeling with Dynamic Modulation
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-based reinforcement learning (MBRL) mitigates this by building world models that simulate environmental dynamics and generate synthetic experience, improving sample efficiency. However, conventional world models process observations holistically, failing to decouple dynamic objects and temporal features from static backg…

BugMagnifier: TON Transaction Simulator for Revealing Smart Contract Vulnerabilities
Yury Yanovich, Victoria Kovalevskaya, Maksim Egorov, Elizaveta Smirnova, Matvey Mishuris, Yash Madhwal, Kirill Ziborov, Vladimir Gorgadze, Subodh Sharma
https://arxiv.org/abs/2509.24444

BugMagnifier: TON Transaction Simulator for Revealing Smart Contract Vulnerabilities
The Open Network (TON) blockchain employs an asynchronous execution model that introduces unique security challenges for smart contracts, particularly race conditions arising from unpredictable message processing order. While previous work established vulnerability patterns through static analysis of audit reports, dynamic detection of temporal dependencies through systematic testing remains an open problem. We present BugMagnifier, a transaction simulation framework that systematically reveals…

Face2Feel: Emotion-Aware Adaptive User Interface
Ismail Alihan Hadimlioglu, Siddharth Linga
https://arxiv.org/abs/2510.00489 https://arxiv.org/pdf/2510.004…

Face2Feel: Emotion-Aware Adaptive User Interface
This paper presents Face2Feel, a novel user interface (UI) model that dynamically adapts to user emotions and preferences captured through computer vision. This adaptive UI framework addresses the limitations of traditional static interfaces by integrating digital image processing, face recognition, and emotion detection techniques. Face2Feel analyzes user expressions utilizing a webcam or pre-installed camera as the primary data source to personalize the UI in real-time. Although dynamically c…

HELIOS -- Hybrid Evaluation of Lifecycle and Impact of Outstanding Science v-2.0
Eduardo Garbayo
https://arxiv.org/abs/2508.21329 https://arxiv.org/pdf/250…

HELIOS -- Hybrid Evaluation of Lifecycle and Impact of Outstanding Science v-2.0
This paper presents a substantial enhancement of the HELIOS (Hybrid Evaluation of Lifecycle and Impact of Outstanding Science) model, transforming it from a static assessment tool into a dynamic and predictive framework for technological maturity. It addresses the limitations of the original model, which relied on linear normalization and fixed weights. Key modifications include the adoption of non-linear normalization functions (sigmoids), the integration of S-curve growth models for forecasti…

Boosting Pointer Analysis With Large Language Model-Enhanced Allocation Function Detection
Baijun Cheng, Kailong Wang, Ling Shi, Haoyu Wang, Peng Di, Yao Guo, Ding Li, Xiangqun Chen
https://arxiv.org/abs/2509.22530

Boosting Pointer Analysis With Large Language Model-Enhanced Allocation Function Detection
Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs where user-defined allocation functions (AFs) are pervasive. Existing approaches largely overlook these custom allocators, leading to coarse aliasing and reduced analysis precision. In this paper, we present AFD, a novel technique that enhances pointer analysis by automatically identifying and modeling custom allocatio…

Optimal Matching Strategies in Two-sided Markets: A Mean Field Approach
Erhan Bayraktar, Dantong Chu, Bohan Li, Ho Man Tai
https://arxiv.org/abs/2509.26531 https://

Optimal Matching Strategies in Two-sided Markets: A Mean Field Approach
This paper develops a mean field game framework for dynamic two-sided matching markets, extending existing matching theory by integrating micro-macro dynamics in two-sided environments. Unlike traditional matching models focusing on static equilibrium or unilateral optimization, our framework simultaneously captures dynamic interactions and strategic behaviors of both market sides, as well as the equilibrium. We model two types of agents who meet each other via Poisson processes and make simult…

Loopy Black-Hole Remnants
Asier Alonso-Bardaji
https://arxiv.org/abs/2508.21159 https://arxiv.org/pdf/2508.21159 …

Loopy Black-Hole Remnants
The quantized area predicted by loop quantum gravity suggests the existence of a lower bound for black-hole horizons. We prove this intuition within a covariant effective model for spherical loop quantum gravity, where nonsingular quasi-static black holes evaporate until their horizons attain the smallest positive eigenvalue of the area operator. Consistent with the third law of black-hole thermodynamics, this final state -- characterized by vanishing temperature and entropy -- cannot be realiz…

Text Adversarial Attacks with Dynamic Outputs
Wenqiang Wang, Siyuan Liang, Xiao Yan, Xiaochun Cao
https://arxiv.org/abs/2509.22393 https://arxiv.org/pdf/25…

Text Adversarial Attacks with Dynamic Outputs
Text adversarial attack methods are typically designed for static scenarios with fixed numbers of output labels and a predefined label space, relying on extensive querying of the victim model (query-based attacks) or the surrogate model (transfer-based attacks). To address this gap, we introduce the Textual Dynamic Outputs Attack (TDOA) method, which employs a clustering-based surrogate model training approach to convert the dynamic-output scenario into a static single-output scenario. To impro…

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Gaole Dai, Shiqi Jiang, Ting Cao, Yuqing Yang, Yuanchun Li, Rui Tan, Mo Li, Lili Qiu
https://arxiv.org/abs/2509.21823

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents, where access to ground-truth trajectories or application databases is often unavailable, and static trajectory-based LLM-as-a-Judge approaches suffer from limited accuracy. To address these challenges, we propose ProRe, a proactive reward system that leverages a general-purpose reasoner and domain-specific evaluator a…

From Phenomenology to a Nonlinear Model of Dynamic Snap-Through of an Elastica
Chiraprabha Bhattacharyya, Ramsharan Rangarajan
https://arxiv.org/abs/2509.21328 https://

From Phenomenology to a Nonlinear Model of Dynamic Snap-Through of an Elastica
Rotating the clamped ends of a buckled elastica induces a snap-through instability. Predicting the limit point and determining the equilibria at the start and end of the snap are routine computations in the quasi-static setting. The instability itself, however, is dynamic, and quite violently so. We propose an energy-preserving nonlinear single degree of freedom model for this dynamic phenomenon in the case of a symmetrically deforming elastica. The model hinges on a surprising observation rela…

Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation
Samiha Tariq
https://arxiv.org/abs/2509.24879 https://arxiv.org/pdf/2509…

Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation
This paper studies how visual traits and market cycles shape prices in NFT markets. Using 94,039 transactions from 26 major generative Ethereum collections, the analysis extracts 196 machine-quantified image features (covering color, composition, palette structure, geometry, texture, and deep learning embeddings), then applies a three-stage filter process to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable…

LISA Technical Report: An Agentic Framework for Smart Contract Auditing
Izaiah Sun, Daniel Tan, Andy Deng
https://arxiv.org/abs/2509.24698 https://arxiv.or…

LISA Technical Report: An Agentic Framework for Smart Contract Auditing
We present LISA, an agentic smart contract vulnerability detection framework that combines rule-based and logic-based methods to address a broad spectrum of vulnerabilities in smart contracts. LISA leverages data from historical audit reports to learn the detection experience (without model fine-tuning), enabling it to generalize learned patterns to unseen projects and evolving threat profiles. In our evaluation, LISA significantly outperforms both LLM-based approaches and traditional static an…

This 3D print was too ambitious; either there were too few supports and/or the wall thickness was too thin. #openscad #sphere #helix

Software Model Checking via Summary-Guided Search (Extended Version)
Ruijie Fang, Zachary Kincaid, Thomas Reps
https://arxiv.org/abs/2508.15137 https://arx…

Software Model Checking via Summary-Guided Search (Extended Version)
In this work, we describe a new software model-checking algorithm called GPS. GPS treats the task of model checking a program as a directed search of the program states, guided by a compositional, summary-based static analysis. The summaries produced by static analysis are used both to prune away infeasible paths and to drive test generation to reach new, unexplored program states. GPS can find both proofs of safety and counter-examples to safety (i.e., inputs that trigger bugs), and features a…

Quantum Fisher information as a witness of non-Markovianity and criticality in the spin-boson model
Daniele Parlato, Grazia Di Bello, Fabrizio Pavan, Giulio De Filippis, Carmine Antonio Perroni
https://arxiv.org/abs/2508.16413

Quantum Fisher information as a witness of non-Markovianity and criticality in the spin-boson model
The quantum Fisher information, the quantum analogue of the classical Fisher information, is a central quantity in quantum metrology and quantum sensing because of its connection to parameter estimation and fidelity susceptibility. Using numerically exact methods applied to a paradigmatic open quantum system, the spin-boson model, we calculate both static and dynamical quantum Fisher information matrix elements with respect to spin-bath couplings and magnetic field strengths. As the spin-bath i…

Static spacetimes with a Finsler angular sector
Erasmo Caponio
https://arxiv.org/abs/2508.20658 https://arxiv.org/pdf/2508.20658

Static spacetimes with a Finsler angular sector
We consider static spacetimes in spherical coordinates whose angular sector is described by a Finsler metric rather than the standard round metric on $S^2$. Our first contribution is kinematical: keeping the lapse and radial factors $e^{ν(r)}$, $e^{\vartheta(r)}$ arbitrary and using only Killing symmetries and the null constraint, we derive model--independent relations for circular photon orbits and the effective dynamics. Specializing the Finsler metric to a positive constant flag curvature R…

FlowVLA: Thinking in Motion with a Visual Chain of Thought
Zhide Zhong, Haodong Yan, Junfeng Li, Xiangchen Liu, Xin Gong, Wenxuan Song, Jiayi Chen, Haoang Li
https://arxiv.org/abs/2508.18269

FlowVLA: Thinking in Motion with a Visual Chain of Thought
Many Vision-Language-Action (VLA) models rely on an internal world model trained via next-frame prediction. This approach, however, struggles with physical reasoning as it entangles static appearance with dynamic motion, often resulting in implausible visual forecasts and inefficient policy learning. To address these limitations, we introduce the Visual Chain of Thought (Visual CoT): a pre-training framework that encourages a model to reason about how a scene evolves before predicting what it w…

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Hinrich Sch\"utze, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.19828

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking any learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning …

A Dynamic, Signals-Based Reinterpretation of Microeconomic Theory
Sarang Shah
https://arxiv.org/abs/2508.20268 https://arxiv.org/pdf/2508.20268

A Dynamic, Signals-Based Reinterpretation of Microeconomic Theory
Economics has long been a science of static equilibria, in which time is a second-order rather than first-order concern. Without time, economic modelers may neglect or obscure the role of time-dependent phenomena, e.g. path-dependency, and limit their ability to compare agnostically the model results with empirical observations. In this article, I outline a dynamic, signals-based recipe for building microeconomic models from traditional static models. I demonstrate this recipe using a classic "…

La critique de la CNIL est vraiment acide, mais super rigolo!
https://mastodon.social/@jeanbaptistemarie/115066647551417397

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
Matteo Cardoni, Sam Leroux
https://arxiv.org/abs/2509.20269 https://

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
As deep neural networks are increasingly deployed in dynamic, real-world environments, relying on a single static model is often insufficient. Changes in input data distributions caused by sensor drift or lighting variations necessitate continual model adaptation. In this paper, we propose a hybrid training methodology that enables efficient on-device domain adaptation by combining the strengths of Backpropagation and Predictive Coding. The method begins with a deep neural network trained offli…

I think this will be a good new challenge for #3dprinting. I think the tube diameter needs to be sufficient for stability, otherwise it will be too elastic and act like a spring instead of a ball. #openscad (thanks @damarberlari@x for his stunning visual article

Combined machine learning for stock selection strategy based on dynamic weighting methods
Lin Cai (Department of Statistics, Columbia University, New York, USA), Zhiyang He (Department of Engineering and Informatics, University of Sussex, Brighton, UK), Caiya Zhang (Department of Statistics and Data Science, Hangzhou City University, Hangzhou, China)
https://…

Combined machine learning for stock selection strategy based on dynamic weighting methods
This paper proposes a novel stock selection strategy framework based on combined machine learning algorithms. Two types of weighting methods for three representative machine learning algorithms are developed to predict the returns of the stock selection strategy. One is static weighting based on model evaluation metrics, the other is dynamic weighting based on Information Coefficients (IC). Using CSI 300 index data, we empirically evaluate the strategy' s backtested performance and model predic…

Bridge Micro-Deformation Monitoring Scheme with Integrated Sensing and Communications
Boxuan Sun, Hongliang Luo, Shaodan Ma, Feifei Gao
https://arxiv.org/abs/2509.17983 https://…

Bridge Micro-Deformation Monitoring Scheme with Integrated Sensing and Communications
In this paper, we propose a novel integrated sensing and communications (ISAC) scheme to perform bridge micro-deformation monitoring (BMDM) in complex environments. We first provide an excitation-bridge coupling model to represent the micro-deformation process of the bridge. Next, we design a novel frame structure for BMDM applications, and construct the OFDM echo channel model for basic scene of BMDM, including micro-deformation, dynamic objects, and static environment. Then, we develop a phas…

Photo-Thermally Tunable Photon-Pair Generation in Dielectric Metasurfaces
Omer Can Karaman, Hua Li, Elif Nur Dayi, Christophe Galland, Giulia Tagliabue
https://arxiv.org/abs/2508.19051

Photo-Thermally Tunable Photon-Pair Generation in Dielectric Metasurfaces
Photon-pair sources based on spontaneous four-wave mixing (SFWM) in integrated photonics are often spectrally static. We demonstrate and model a fundamental thermo-optical mechanism that modulates photon-pair generation in amorphous silicon (a-Si) thin films and metasurfaces via SFWM. Femtosecond-pulsed excitation yields g2(0) higher than 400 in unpatterned a-Si, confirming high-purity nonclassical emission. Resonant a-Si metasurfaces produce photon pairs at rates exceeding 3.8 kHz under 0.6 mW…

Inferring Mbh-Mbulge Evolution from the Gravitational Wave Background
Cayenne Matt, Kayhan Gultekin, Luke Kelley, Laura Blecha, Joseph Simon, Gabriella Agazie, Akash Anumarlapudi, Anne Archibald, Zaven Arzoumanian, Jeremy Baier, Paul Baker, Bence B\'ecsy, Adam Brazier, Paul Brook, Sarah Burke-Spolaor, Rand Burnette, Robin Case, James Casey-Clyde, Maria Charisi, Shami Chatterjee, Tyler Cohen, James Cordes, Neil Cornish, Fronefield Crawford, H. Thankful Cromartie, Kathryn Crowter, Me…

Inferring Mbh-Mbulge Evolution from the Gravitational Wave Background
We test the impact of an evolving supermassive black hole (SMBH) mass scaling relation (Mbh-Mbulge) on the predictions for the gravitational wave background (GWB). The observed GWB amplitude is 2-3 times higher than predicted by astrophysically informed models which suggests the need to revise the assumptions in those models. We compare a semi-analytic model's ability to reproduce the observed GWB spectrum with a static versus evolving-amplitude Mbh-Mbulge relation. We additionally consider the…

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
Chenghao Wu, Ruiyang Ren, Junjie Zhang, Ruirui Wang, Zhongrui Ma, Qi Ye, Wayne Xin Zhao
https://arxiv.org/abs/2508.18812

STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent …

Preconditioning of a hybridizable discontinuous Galerkin method for Biot's consolidation model
Esteban Henr\'iquez, Jeonghun J. Lee, Sander Rhebergen
https://arxiv.org/abs/2508.12991

Preconditioning of a hybridizable discontinuous Galerkin method for Biot's consolidation model
We present a parameter-robust preconditioner for a hybridizable discontinuous Galerkin (HDG) discretization of a four-field formulation of Biot's consolidation model. We first determine a parameter-robust preconditioner for the full discretization. HDG methods, however, allow for static condensation. We therefore apply the framework presented in our previous work [arXiv:2503.05918, 2025] to show that a reduced form of the preconditioner is also parameter-robust for the reduced HDG discretizatio…

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2509.14163

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum--classical actor--critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multil…

On the one-dimensional piston model with Large Velocity Variations
Dian Hu, Qianfeng Li, Yongqian Zhang
https://arxiv.org/abs/2508.13971 https://arxiv.org/…

On the one-dimensional piston model with Large Velocity Variations
This paper investigates the dynamics of a one-dimensional piston expanding into a static rarefied gas. Using asymptotic analysis in the limit of vanishing initial density, we derive sharp estimates for the piston-shock distance, the separation of characteristic speeds, and the reflection coefficient associated with characteristic waves interacting with the leading shock front. Based on these estimates, we apply the method of characteristics to prove the global-in-time existence of piecewise smo…

Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination
Ming Yang, Dongrun Li, Xin Wang, Xiaoyang Yu, Xiaoming Wu, Shibo He
https://arxiv.org/abs/2508.17954

Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination
Cross-client data heterogeneity in federated learning induces biases that impede unbiased consensus condensation and the complementary fusion of generalization- and personalization-oriented knowledge. While existing approaches mitigate heterogeneity through model decoupling and representation center loss, they often rely on static and restricted metrics to evaluate local knowledge and adopt global alignment too rigidly, leading to consensus distortion and diminished model adaptability. To addre…

Google now lets US-based Google Photos users turn static images into silent four-second videos using its Veo 3 model (Elissa Welle/The Verge)
https://www.theverge.com/ai-artificial-intelligence/771630/google-photos-veo3-video-animate-ca…

Google Photos now lets you animate your camera roll with Veo 3 for free
Google Photos now offers Veo 3 for turning images into short videos.

Finally #3dprinting again. Had some hotend nozzle clogging and learned about different methods to overcome that. Turns out 'cold pull' is best. Now only 2.5 more hours to go. ;)

A generalized static spherically symmetric anisotropic compact star model in isotropic coordinates
B S Ratanpal, Bhavesh Suthar
https://arxiv.org/abs/2508.16373 https://

A generalized static spherically symmetric anisotropic compact star model in isotropic coordinates
In this article, an exact solution of Einstein's field equations for spherically symmetric anisotropic matter distributions in isotropic coordinates is obtained. For this, the solution has been obtained by using a generalized physically significant metric potential B(r) and a specific choice of the anisotropy. This new class of generalized solutions is singularity-free, which can be used to describe relativistic compact stars.

A General Model for Static Contact Angles
Carlos E Colosqui
https://arxiv.org/abs/2509.14692 https://arxiv.org/pdf/2509.14692

A General Model for Static Contact Angles
The problem of contact angle and hysteresis determination has direct implications for engineering applications of wetting, colloid and surface science. Significant technical challenges can arise under real-world operating conditions, because the static contact angle is strongly influenced by contamination at the liquid-solid and liquid-vapor interfaces, chemical aging over long times, and environmental variables such as relative humidity and temperature. Analytical models that account for these…

Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines
Sayan Mandal, Hua Jiang
https://arxiv.org/abs/2510.10290 https://

Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines
Automated code review adoption lags in compliance-heavy settings, where static analyzers produce high-volume, low-rationale outputs, and naive LLM use risks hallucination and incurring cost overhead. We present a production system for grounded, PR-native review that pairs static-analysis findings with AST-guided context extraction and a single-GPU, on-demand serving stack (quantized open-weight model, multi-tier caching) to deliver concise explanations and remediation guidance. Evaluated on saf…

MCP-Guard: A Defense Framework for Model Context Protocol Integrity in Large Language Model Applications
Wenpeng Xing, Zhonghao Qi, Yupeng Qin, Yilin Li, Caini Chang, Jiahui Yu, Changting Lin, Zhenzhen Xie, Meng Han
https://arxiv.org/abs/2508.10991

MCP-Guard: A Defense Framework for Model Context Protocol Integrity in Large Language Model Applications
The integration of Large Language Models (LLMs) with external tools via protocols such as the Model Context Protocol (MCP) introduces critical security vulnerabilities, including prompt injection, data exfiltration, and other threats. To counter these challenges, we propose MCP-Guard, a robust, layered defense architecture designed for LLM--tool interactions. MCP-Guard employs a three-stage detection pipeline that balances efficiency with accuracy: it progresses from lightweight static scanning…

A Timeless Game: A Game-Theoretic Model of Mass-Geometry Relations
Milad Ghadimi
https://arxiv.org/abs/2509.15229 https://arxiv.org/pdf/2509.15229

A Timeless Game: A Game-Theoretic Model of Mass-Geometry Relations
We develop a minimal, timeless game-theoretic representation of the mass-geometry relation. An "Object" (mass) and "Space" (geometry) choose strategies in a static normal-form game; utilities encode stability as mutual consistency rather than dynamical payoffs. In a 2x2 toy model, the equilibria correspond to "light-flat" and "heavy-curved" configurations; a continuous variant clarifies when only trivial interior equilibria appear versus a continuum along a matching ray. Philosophically, the po…

Symmetry-breaking perturbations in the Jahn-Teller-Hubbard model
Natsuki Okada, Tatsuya Miki, Yusuke Nomura, Philipp Werner, Shintaro Hoshino
https://arxiv.org/abs/2508.07477 ht…

Symmetry-breaking perturbations in the Jahn-Teller-Hubbard model
We study the effect of symmetry-breaking perturbations in the multiorbital Hubbard model coupled to anisotropic Jahn-Teller phonons, which is relevant for the description of fulleride superconductors. This system is often approximated by a model with static antiferromagnetic (AFM) Hund's coupling, in which the coupling to the Jahn-Teller phonon is effectively described, but the retardation effect associated with phonon propagation is neglected. We compare the properties of the models with stati…

SONAR: Self-Distilled Continual Pre-training for Domain Adaptive Audio Representation
Yizhou Zhang, Yuan Gao, Wangjin Zhou, Zicheng Yuan, Keisuke Imoto, Tatsuya Kawahara
https://arxiv.org/abs/2509.15703

SONAR: Self-Distilled Continual Pre-training for Domain Adaptive Audio Representation
Self-supervised learning (SSL) on large-scale datasets like AudioSet has become the dominant paradigm for audio representation learning. While the continuous influx of new, unlabeled audio presents an opportunity to enrich these static representations, a naive approach is to retrain the model from scratch using all available data. However, this method is computationally prohibitive and discards the valuable knowledge embedded in the previously trained model weights. To address this inefficiency…

Anisotropic compact star model with quadratic equation of state in paraboloidal spacetime
B S Ratanpal, Bhavesh Suthar
https://arxiv.org/abs/2508.16392 https://

Anisotropic compact star model with quadratic equation of state in paraboloidal spacetime
In this work, we report a new exact solution of Einstein's field equations for static spherically symmetric anisotropic matter distributions on the background of paraboloidal spacetime by assuming a quadratic equation of state. The model parameters were found by matching the interior spacetime metric with the Schwarzschild exterior metric. The new exact solution is non-singular and meets all of the physical plausibility conditions for a realistic compact star. The detailed physical analysis of …

Crosslisted article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- A General Model for Static Contact Angles
Carlos E Colosqui

Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads
Jakob Scheffels, Elizabeth Qian, Iason Papaioannou, Elisabeth Ullmann
https://arxiv.org/abs/2510.07950

Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads
Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engineering applications, but computational solution of Bayesian inverse problems is often expensive because standard solution approaches require many evaluations of the forward model mapping the parameter value to predicted observations. In many settings, this forward model is expensive because it requires the solution o…

GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions
Helong Huang, Min Cen, Kai Tan, Xingyue Quan, Guowei Huang, Hong Zhang
https://arxiv.org/abs/2508.07650

GraphCoT-VLA: A 3D Spatial-Aware Reasoning Vision-Language-Action Model for Robotic Manipulation with Ambiguous Instructions
Vision-language-action models have emerged as a crucial paradigm in robotic manipulation. However, existing VLA models exhibit notable limitations in handling ambiguous language instructions and unknown environmental states. Furthermore, their perception is largely constrained to static two-dimensional observations, lacking the capability to model three-dimensional interactions between the robot and its environment. To address these challenges, this paper proposes GraphCoT-VLA, an efficient end…

Dissecting the moat regime at low energies I: Renormalization and the phase structure
Fabian Rennecke, Shi Yin
https://arxiv.org/abs/2510.06712 https://arx…

Dissecting the moat regime at low energies I: Renormalization and the phase structure
Dense QCD matter can feature a moat regime, where the static energy of mesons is minimal at nonzero momentum. Valuable insights into this regime can be gained using low-energy models. This, however, requires a careful assessment of model artifacts. We therefore study the effects of renormalization and in-medium modifications of quark-meson interaction on the moat regime. To capture the main effects, we use a two-flavor quark-meson model at finite temperature and baryon density in the random pha…

Revisiting $\mu$SR Studies of Ion Dynamics in the Light of Extended Kubo-Toyabe Model
Takashi U. Ito, Ryosuke Kadono
https://arxiv.org/abs/2508.05968 https://

Revisiting $μ$SR Studies of Ion Dynamics in the Light of Extended Kubo-Toyabe Model
The dynamical Kubo-Toyabe (dKT) function is extended to describe the spin relaxation under the coexisting dynamical and static internal magnetic fields. A detailed re-evaluation of the previous $μ^\pm$SR data in Na$_x$CoO$_2$ using this function disfavors the conventional interpretation based on sodium-ion diffusion and instead supports the $μ^+$ self-diffusion scenario. This also resolves the long-standing inconsistencies in the dKT-function-based $μ$SR studies on ion diffusion from the vie…

Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents
Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan
https://arxiv.org/abs/2509.17488

Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents
The increasing autonomy of LLM agents in handling sensitive communications, accelerated by Model Context Protocol (MCP) and Agent-to-Agent (A2A) frameworks, creates urgent privacy challenges. While recent work reveals significant gaps between LLMs' privacy Q&A performance and their agent behavior, existing benchmarks remain limited to static, simplified scenarios. We present PrivacyChecker, a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage…

almost... Getting closer to this Rattan / Takraw ball model in #Openscad for #3dprinting

Phase field modelling of the growth and detachment of bubbles in a hydrogen electrolyzer
Carlos Uriarte, Marco A. Fontelos, Manuel Array\'as
https://arxiv.org/abs/2508.16585

Phase field modelling of the growth and detachment of bubbles in a hydrogen electrolyzer
We develop and implement numerically a phase field model for the growth and detachment of a gas bubble resting on an electrode and being filled with hydrogen produced by water electrolysis. The bubble is surrounded by a viscous liquid, has a prescribed static contact angle and is also subject to gravitational forces. We compute, as a function of the static contact angle, the time at which the bubble detaches from the substrate and what volume it has at that time. We also investigate de dependen…

Emergent statistical mechanics in holographic random tensor networks
Shozab Qasim, Jens Eisert, Alexander Jahn
https://arxiv.org/abs/2508.16570 https://arx…

Emergent statistical mechanics in holographic random tensor networks
Recent years have enjoyed substantial progress in capturing properties of complex quantum systems by means of random tensor networks (RTNs), which form ensembles of quantum states that depend only on the tensor network geometry and bond dimensions. Of particular interest are RTNs on hyperbolic geometries, with local tensors typically chosen from the unitary Haar measure, that model critical boundary states of holographic bulk-boundary dualities. In this work, we elevate static pictures of ensem…

On Fixed-Parameter Tractability of Weighted 0-1 Timed Matching Problem on Temporal Graphs
Rinku Kumar, Bodhisatwa Mazumdar, Subhrangsu Mandal
https://arxiv.org/abs/2508.10562 ht…

On Fixed-Parameter Tractability of Weighted 0-1 Timed Matching Problem on Temporal Graphs
Temporal graphs are introduced to model systems where the relationships among the entities of the system evolve over time. In this paper, we consider the temporal graphs where the edge set changes with time and all the changes are known a priori. The underlying graph of a temporal graph is a static graph consisting of all the vertices and edges that exist for at least one timestep in the temporal graph. The concept of 0-1 timed matching in temporal graphs was introduced by Mandal and Gupta [DAM…

Anomalous Criticality of Absorbing State Transition toward Jamming
He-Da Wang, Bo Wang, Qun-Li Lei, Yu-Qiang Ma
https://arxiv.org/abs/2510.06641 https://ar…

Anomalous Criticality of Absorbing State Transition toward Jamming
The jamming transition is traditionally regarded as a geometric transition governed by static contact networks. Recently, dynamic phase transitions of athermal particles under periodic shear provide a new lens on this problem, leading to a conjecture that jamming transition corresponds to an absorbing-state transition within the Manna (conserved directed percolation) universality class. Here, by re-examing the biased random organization model, a minimal model for particles under periodic sheari…

Deep Feedback Models
David Calhas, Arlindo L. Oliveira
https://arxiv.org/abs/2509.15905 https://arxiv.org/pdf/2509.15905

Deep Feedback Models
Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces dynamics into otherwise static architectures, enabling DFMs to iteratively refine their internal state and mimic aspects of biological decision making. We model this process as a differential equation solved through a recurrent neural network, stabilized via exponential decay to ensure convergence. To evaluate their ef…

Electron transfer between surface-acoustic-wave-induced moving and static quantum dots
Mikel Olano, Geza Giedke
https://arxiv.org/abs/2509.01525 https://ar…

Electron transfer between surface-acoustic-wave-induced moving and static quantum dots
Fast long-range interactions between distant quantum dots in arrays remains an unsolved issue, which can be key to solve scalability issues in quantum simulation and computation processes, particularly related to the overhead associated with quantum error correction schemes. Furthermore, transport between static and moving quantum dots, relevant in surface acoustic wave induced experiments, has not been studied in detail. This article presents a paradigmatic model for picturing this process, wh…

Factor-Driven Network Informed Restricted Vector Autoregression
Brendan Martin, Mihai Cucuringu, Alessandra Luati, Francesco Sanna Passino
https://arxiv.org/abs/2508.02198 https…

Factor-Driven Network Informed Restricted Vector Autoregression
High-dimensional financial time series often exhibit complex dependence relations driven by both common market structures and latent connections among assets. To capture these characteristics, this paper proposes Factor-Driven Network Informed Restricted Vector Autoregression (FNIRVAR), a model for the common and idiosyncratic components of high-dimensional time series with an underlying unobserved network structure. The common component is modelled by a static factor model, which allows for st…

Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation
Kelin Ren, Chan-Yang Ju, Dong-Ho Lee
https://arxiv.org/abs/2509.09114 https://

Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation
Multimodal recommendation systems are increasingly becoming foundational technologies for e-commerce and content platforms, enabling personalized services by jointly modeling users' historical behaviors and the multimodal features of items (e.g., visual and textual). However, most existing methods rely on either static fusion strategies or graph-based local interaction modeling, facing two critical limitations: (1) insufficient ability to model fine-grained cross-modal associations, leading to …

Optimal Fees for Liquidity Provision in Automated Market Makers
Steven Campbell, Philippe Bergault, Jason Milionis, Marcel Nutz
https://arxiv.org/abs/2508.08152 https://

Optimal Fees for Liquidity Provision in Automated Market Makers
Passive liquidity providers (LPs) in automated market makers (AMMs) face losses due to adverse selection (LVR), which static trading fees often fail to offset in practice. We study the key determinants of LP profitability in a dynamic reduced-form model where an AMM operates in parallel with a centralized exchange (CEX), traders route their orders optimally to the venue offering the better price, and arbitrageurs exploit price discrepancies. Using large-scale simulations and real market data, w…

Confidence-Modulated Speculative Decoding for Large Language Models
Jaydip Sen, Subhasis Dasgupta, Hetvi Waghela
https://arxiv.org/abs/2508.15371 https://a…

Confidence-Modulated Speculative Decoding for Large Language Models
Speculative decoding has emerged as an effective approach for accelerating autoregressive inference by parallelizing token generation through a draft-then-verify paradigm. However, existing methods rely on static drafting lengths and rigid verification criteria, limiting their adaptability across varying model uncertainties and input complexities. This paper proposes an information-theoretic framework for speculative decoding based on confidence-modulated drafting. By leveraging entropy and mar…

Diversity First, Quality Later: A Two-Stage Assumption for Language Model Alignment
Zetian Sun, Dongfang Li, Baotian Hu
https://arxiv.org/abs/2508.10530 https://

Diversity First, Quality Later: A Two-Stage Assumption for Language Model Alignment
The alignment of language models (LMs) with human preferences is critical for building reliable AI systems. The problem is typically framed as optimizing an LM policy to maximize the expected reward that reflects human preferences. Recently, Direct Preference Optimization (DPO) was proposed as a LM alignment method that directly optimize the policy from static preference data, and further improved by incorporating on-policy sampling (i.e., preference candidates generated during the training loo…

Last post for today, but I had to do the video of my #openscad woven ribbon ball model. #creativecoding

A fluid--peridynamic structure model of deformation and damage of microchannels
Ziyu Wang, Ivan C. Christov
https://arxiv.org/abs/2508.02875 https://arxiv.…

A fluid--peridynamic structure model of deformation and damage of microchannels
Soft-walled microchannels arise in many applications, ranging from organ-on-a-chip platforms to soft-robotic actuators. However, despite extensive research on their static and dynamic response, the potential failure of these devices has not been addressed. To this end, we explore fluid--structure interaction in microchannels whose compliant top wall is governed by a nonlocal mechanical theory capable of simulating both deformation and material failure. We develop a one-dimensional model by coup…

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar
https://arxiv.org/abs/2509.09485

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model parameters and gradient information are susceptible to privacy leakage. Although Differentially Private SGD (DPSGD) addresses privacy concerns, its static noise mechanism impacts the error bounds for model performance. Additionally, with the exponential increase in model parameters, efficient learning of these models using s…

A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy
Luciano Pollicino
https://arxiv.org/abs/2508.13233 https://

A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy
Traditional macroeconomic models, based on static algebraic systems, fail to capture the dynamics of a bimonetary economy like Argentina's. This paper proposes a framework based on category theory to develop a more flexible and structured model that represents the evolving relationships between key variables such as inflation expectations, interest rates, and currency demand. Using concepts like objects, morphisms, learning/forgetful functors, limits, and colimits, the model is applied to empir…

A Parallel Solver with Multiphysics Finite Element Method for Poroelasticity Coupled with Elasticity Model
Zhihao Ge, Chengxin Wang
https://arxiv.org/abs/2509.06673 https://

A Parallel Solver with Multiphysics Finite Element Method for Poroelasticity Coupled with Elasticity Model
In this paper, we propose a parallel solver for solving the quasi-static linear poroelasticity coupled with linear elasticity model in the Lagrange multiplier framework. Firstly, we reformulate the model into a coupling of the nearly incompressible elasticity and an unsteady affection-diffusion equations by setting new variable ``elastic pressure" and ``volumetric fluid content". And we introduce a Lagrange multiplier to guarantee the normal stress continuity on the interface. Then, we give the…

Puppeteer: Rig and Animate Your 3D Models
Chaoyue Song, Xiu Li, Fan Yang, Zhongcong Xu, Jiacheng Wei, Fayao Liu, Jiashi Feng, Guosheng Lin, Jianfeng Zhang
https://arxiv.org/abs/2508.10898

Puppeteer: Rig and Animate Your 3D Models
Modern interactive applications increasingly demand dynamic 3D content, yet the transformation of static 3D models into animated assets constitutes a significant bottleneck in content creation pipelines. While recent advances in generative AI have revolutionized static 3D model creation, rigging and animation continue to depend heavily on expert intervention. We present Puppeteer, a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects. Our system fi…

PROD: Palpative Reconstruction of Deformable Objects through Elastostatic Signed Distance Functions
Hamza El-Kebir
https://arxiv.org/abs/2508.12554 https://

PROD: Palpative Reconstruction of Deformable Objects through Elastostatic Signed Distance Functions
We introduce PROD (Palpative Reconstruction of Deformables), a novel method for reconstructing the shape and mechanical properties of deformable objects using elastostatic signed distance functions (SDFs). Unlike traditional approaches that rely on purely geometric or visual data, PROD integrates palpative interaction -- measured through force-controlled surface probing -- to estimate both the static and dynamic response of soft materials. We model the deformation of an object as an elastostati…

Constraints on the Sen black hole mass and charge from quasi-periodic oscillations
Kuantay Boshkayev, Marco Muccino
https://arxiv.org/abs/2508.15682 https://

Constraints on the Sen black hole mass and charge from quasi-periodic oscillations
We analyze quasi-periodic oscillation data from selected X-ray binary systems hosting black holes. To model the spacetime geometry, we resort the static Sen solution -- originally derived in the framework of heterotic string theory -- which reduces to the Schwarzschild spacetime for vanishing electric charge. By fitting the observed frequencies within the relativistic precession model, we constrain the mass and charge parameters of the Sen black hole and discuss their astrophysical implications…

Screening Masses for Scalar and Pseudoscalar Mesons and their Diquark Partners: Insights from the Contact Interaction Model
M. A. Ram\'irez-Garrido, R. J. Hern\'andez-Pinto, I. M. Higuera-Angulo, L. X. Guti\'errez-Guerrero
https://arxiv.org/abs/2508.01099

Screening Masses for Scalar and Pseudoscalar Mesons and their Diquark Partners: Insights from the Contact Interaction Model
In this study, we calculate the screening masses of forty spin-zero meson and diquark states composed of light, heavy and heavy-light quarks, within a symmetry-preserving framework based on Dyson-Schwinger equations and a vector $\times$ vector contact interaction. This effective interaction reproduces hadron static properties at $T=0$ MeV that are in close agreement with experimental data and results from more sophisticated interaction kernels. Our analysis reveals that, at temperatures above …

Continual Release of Densest Subgraphs: Privacy Amplification & Sublinear Space via Subsampling
Felix Zhou
https://arxiv.org/abs/2510.11640 https://arx…

Continual Release of Densest Subgraphs: Privacy Amplification & Sublinear Space via Subsampling
We study the sublinear space continual release model for edge-differentially private (DP) graph algorithms, with a focus on the densest subgraph problem (DSG) in the insertion-only setting. Our main result is the first continual release DSG algorithm that matches the additive error of the best static DP algorithms and the space complexity of the best non-private streaming algorithms, up to constants. The key idea is a refined use of subsampling that simultaneously achieves privacy amplification…

Research on Quality Measurement and Dynamic Transmission of Higher Education in China from the Perspective of Dual Circulation: An Empirical Analysis Based on Entropy Weight-TOPSIS and PVAR Models
Zhangming Yang, Xiaoting Yin, Dahai Zhang
https://arxiv.org/abs/2508.09216

Research on Quality Measurement and Dynamic Transmission of Higher Education in China from the Perspective of Dual Circulation: An Empirical Analysis Based on Entropy Weight-TOPSIS and PVAR Models
Drawing on the new pattern of 'dual circulation', an evaluation system for the development level of China's higher education has been constructed, utilizing data from Chinese provinces over the period 2020 to 2022. Initially, the evaluation model is established, followed by the screening of initial indicators through correlation analysis. Subsequently, an entropy weight-TOPSIS model are employed for static quantification, and the PVAR model is introduced for the first time to describe the dynam…

TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs
Daniele Cipollone, Egor Bogomolov, Arie van Deursen, Maliheh Izadi
https://arxiv.org/abs/2508.02455

TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs
Token-level code completion is one of the most critical features in modern Integrated Development Environments (IDEs). It assists developers by suggesting relevant identifiers and APIs during coding. While completions are typically derived from static analysis, their usefulness depends heavily on how they are ranked, as correct predictions buried deep in the list are rarely seen by users. Most current systems rely on hand-crafted heuristics or lightweight machine learning models trained on user…

Learning User Preferences for Image Generation Model
Wenyi Mo, Ying Ba, Tianyu Zhang, Yalong Bai, Biye Li
https://arxiv.org/abs/2508.08220 https://arxiv.or…

Learning User Preferences for Image Generation Model
User preference prediction requires a comprehensive and accurate understanding of individual tastes. This includes both surface-level attributes, such as color and style, and deeper content-related aspects, such as themes and composition. However, existing methods typically rely on general human preferences or assume static user profiles, often neglecting individual variability and the dynamic, multifaceted nature of personal taste. To address these limitations, we propose an approach built upo…

Dynamic Stall Characteristics and Modelling of Time-Varying Pitching Kinematics
Sahar Rezapour, Karen Mulleners
https://arxiv.org/abs/2508.10647 https://ar…

Dynamic Stall Characteristics and Modelling of Time-Varying Pitching Kinematics
We present an experimental investigation examining how the complexity of pitching kinematics influences dynamic stall characteristics, including the stall delay and aerodynamic force response. The study examines whether the pitch rate defined at the static stall angle adequately characterises time-varying pitching kinematics for stall onset prediction. We then evaluate the performance of the generalised Goman-Khrabrov model in predicting force responses of nonlinear pitching motions and propose…

Control of Dipolar Dynamics by Geometrical Programming
Jiaqi You, John M. Doyle, Zirui Liu, Scarlett S. Yu, Avikar Periwal
https://arxiv.org/abs/2508.11785 https://

Control of Dipolar Dynamics by Geometrical Programming
We propose and theoretically analyze methods for quantum many-body control through geometric reshaping of molecular tweezer arrays. Dynamic rearrangement during entanglement is readily available due to the extended coherence times of molecular rotational qubits. We show how motional dephasing can be suppressed and enhanced spin squeezing can be achieved in an actively rearranged short-range XY model. We also analyze in detail a specific static geometry that significantly suppresses decoherence.…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Yubo Li, Ramayya Krishnan, Rema Padman
https://arxiv.org/abs/2510.02712 http…

Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present the first comprehensive survival analysis of conversational AI robustness, analyzing 36,951 conversation turns across 9 stat…

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
Faduo Liang, Yunfeng Yang, Shi-Lu Dai
https://arxiv.org/abs/2510.02885

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently me…

Dissipation-Enhanced Localization in a Disorder-Free $\mathbb{Z}_2$ Lattice Gauge System
Xuanpu Yang, Xiang-Ping Jiang, Lei Pan
https://arxiv.org/abs/2509.06642 https://

Dissipation-Enhanced Localization in a Disorder-Free $\mathbb{Z}_2$ Lattice Gauge System
The $\mathbb{Z}_2$ lattice gauge model, as the simplest realization of a lattice gauge theory, exhibits rich and unconventional physics. One of its most remarkable features is disorder-free localization, where localization emerges not from explicit quenched disorder but from static background $\mathbb{Z}_2$ gauge charges, leading to persistent memory of the initial state. In this work, we investigate the dissipative dynamics of the $\mathbb{Z}_2$ lattice gauge model by coupling it to a Markovia…

Are Large Reasoning Models Interruptible?
Tsung-Han Wu, Mihran Miroyan, David M. Chan, Trevor Darrell, Narges Norouzi, Joseph E. Gonzalez
https://arxiv.org/abs/2510.11713 https:…

Are Large Reasoning Models Interruptible?
Large Reasoning Models (LRMs) excel at complex reasoning but are traditionally evaluated in static, "frozen world" settings: model responses are assumed to be instantaneous, and the context of a request is presumed to be immutable over the duration of the response. While generally true for short-term tasks, the "frozen world" assumption breaks down in modern reasoning tasks such as assistive programming, where models may take hours to think through problems and code may change dramatically from…

Singularity and differentiability at the origin of regular black holes
Tommaso Antonelli, Marco Sebastianutti
https://arxiv.org/abs/2509.15477 https://arxi…

Singularity and differentiability at the origin of regular black holes
The divergence of curvature invariants at a given point signals the incompleteness of the spacetime, and the derivative order of these diverging invariants determines the differentiability class of the considered spacetime. We hereby focus on a general static and spherically symmetric geometry and determine, in the full non-linear regime and in a model independent way, the conditions that the metric functions must satisfy in order to achieve regularity at the origin. This work is structured aro…

LLMHoney: A Real-Time SSH Honeypot with Large Language Model-Driven Dynamic Response Generation
Pranjay Malhotra
https://arxiv.org/abs/2509.01463 https://a…

LLMHoney: A Real-Time SSH Honeypot with Large Language Model-Driven Dynamic Response Generation
Cybersecurity honeypots are deception tools for engaging attackers and gather intelligence, but traditional low or medium-interaction honeypots often rely on static, pre-scripted interactions that can be easily identified by skilled adversaries. This Report presents LLMHoney, an SSH honeypot that leverages Large Language Models (LLMs) to generate realistic, dynamic command outputs in real time. LLMHoney integrates a dictionary-based virtual file system to handle common commands with low latency…

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu
https://arxiv.org/abs/2509.07342

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a tempor…

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Wenjie Zhang
https://arxiv.org/abs/2510.12434

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, w…

A unified model for linear responses of physical networks
Jos\'e M. Ortiz-Tav\'arez, William Stephenson, Xiaoming Mao
https://arxiv.org/abs/2508.04616 https://

A unified model for linear responses of physical networks
Many physical systems--from mechanical lattices and electrical circuits to biological tissues and architected metamaterials--can be understood as networks transmitting physical quantities. We present a unified mathematical framework for describing linear responses of such physical networks using tools from algebraic graph theory. This approach captures static and dynamic behaviors across multiple domains, including mechanical, electrical, thermal, and diffusive responses using node and edge var…

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Minkyoung Cho, Ruben Ohana, Christian Jacobsen, Adityan Jothi, Min-Hung Chen, Z. Morley Mao, Ethem Can
https://arxiv.org/abs/2510.09561

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Current controllable diffusion models typically rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality. This approach uses a static conditioning strategy for a dynamic, multi-stage denoising process, limiting the model's ability to adapt its response as the generation evolves from coarse structure to fine detail. We introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that enables dynamic, context-aware control by c…

CWM: An Open-Weights LLM for Research on Code Generation with World Models
FAIR CodeGen team, Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, Kunhao Zheng, Jordi Armengol-Estap\'e, Pedram Bashiri, Maximilian Beck, Pierre Chambon, Abhishek Charnalia, Chris Cummins, Juliette Decugis, Zacharias V. Fisches, Fran\c{c}ois Fleuret, Fabian Gloeckle, Alex Gu, Michael Hassid, Daniel Haziza, Badr You…

CWM: An Open-Weights LLM for Research on Code Generation with World Models
We release Code World Model (CWM), a 32-billion-parameter open-weights LLM, to advance research on code generation with world models. To improve code understanding beyond what can be learned from training on static code alone, we mid-train CWM on a large amount of observation-action trajectories from Python interpreter and agentic Docker environments, and perform extensive multi-task reasoning RL in verifiable coding, math, and multi-turn software engineering environments. With CWM, we provide …

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu
https://arxiv.org/abs/2510.09275

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) are fundamentally misaligned with real-world clinical practice. Most of them rely on static benchmarks derived from public medical exam items, which tend to overestimate model performance and ignore the difference between textbook cases and the ambiguous, varying conditions in the real world. Recent efforts toward dynamic evaluation offer a promi…

Oganesson versus Uranium Hydrogen-like Ions from the Viewpoint of Old Quantum Mechanics
Kamal Barley, Andreas Ruffing, Sergei K. Suslov
https://arxiv.org/abs/2509.06249 https://…

Oganesson versus Uranium Hydrogen-like Ions from the Viewpoint of Old Quantum Mechanics
We compare, within the framework of the old Bohr-Sommerfeld atomic model, Uranium versus hypothetical Oganesson relativistic hydrogen-like ions. Existence of a self-intercepting orbit in the super strong static Coulomb field is demonstrated with the aid of Mathematica computer algebra system. A possibility of a similar 'Oganesson-type' effect in a strong gravitational field is also mentioned.

Quintessence-Chameleon transitions in anisotropic Kiselev model of neutron stars
M. V. Pradosh Keshav (Christ University, Bangalore), V. Jithesh (Christ University, Bangalore), Kenath Arun (Christ University, Bangalore)
https://arxiv.org/abs/2508.04744

Quintessence-Chameleon transitions in anisotropic Kiselev model of neutron stars
We investigate a chameleon scalar field dynamically interacting with a Kiselev-type metric, where the static anisotropic fluid part of the metric is replaced by a density-dependent scalar field non-minimally coupled to curvature. This construction enables a transition from screened behavior in high-density regions-where the scalar acquires an effective mass m_ϕ\proptoρ^1/2-to unscreened quintessence dynamics at large scales, characterized by a critical screening radius r_\rm crit\propto m_ϕ^…

single twist cord trefoil knot #3d #model #openscad #code

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Qiyu Chen, Zhen Qu, Wei Luo, Haiming Yao, Yunkang Cao, Yuxin Jiang, Yinan Duan, Huiyuan Luo, Chengkan Lv, Zhengtao Zhang
https://arxiv.org/abs/2508.03447

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error. However, it still faces the following challenges: (i) static learnable tokens struggle to …

Perfect fluid dark matter: a viability test with galaxy rotation curves
Jan Kuncewicz
https://arxiv.org/abs/2509.12268 https://arxiv.org/pdf/2509.12268

Perfect fluid dark matter: a viability test with galaxy rotation curves
The anomalous rotation curves of galaxies provide compelling evidence for dark matter, yet its fundamental nature and distribution remain key unresolved issues in astrophysics. In this work, we investigate a dark matter model derived from first principles within General Relativity, treating the halo as a perfect fluid with a specific anisotropic equation of state characterized by a single parameter. This framework yields two families of static, spherically symmetric solutions: a Power-Law metri…

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
Patrick Amadeus Irawan, Ryandito Diandaru, Belati Jagad Bintang Syuhada, Randy Zakya Suchrady, Alham Fikri Aji, Genta Indra Winata, Fajri Koto, Samuel Cahyawijaya
https://arxiv.org/abs/2509.05060

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
We introduce Entropy2Vec, a novel framework for deriving cross-lingual language representations by leveraging the entropy of monolingual language models. Unlike traditional typological inventories that suffer from feature sparsity and static snapshots, Entropy2Vec uses the inherent uncertainty in language models to capture typological relationships between languages. By training a language model on a single language, we hypothesize that the entropy of its predictions reflects its structural sim…

Exploring Satellite Quantum Key Distribution under Atmospheric Constraints
Aditya Ajith, S. Saravana Veni
https://arxiv.org/abs/2508.05235 https://arxiv.or…

Exploring Satellite Quantum Key Distribution under Atmospheric Constraints
Satellite Quantum Key Distribution creates a pathway for secure global communication with a level of security that is peerless. However, ground-to-satellite Quantum Key Distribution links are degraded due to the atmospheric turbulence. This paper gives a numerical framework using angular spectrum propagation, Hufnagel-Valley model of turbulence and Von Karman phase screens and takes into account the static losses introduced due to the absorption of the beam by the different elements and compoun…

Compact stars with gravitational wave echoes in $f(R,L_{m},T)$ gravitational theory
Meghanil Sinha, S. Surendra Singh
https://arxiv.org/abs/2508.04736 https://

Compact stars with gravitational wave echoes in $f(R,L_{m},T)$ gravitational theory
This work explores the gravitational wave echoes (GWEs) from the compact stellar configurations in the backdrop of $f(R,L_{m},T)$ gravity within static and spherically symmetric framework. Our study has utilized the MIT Bag model and color-favour-locked (CFL) phase equations of state (EoS) for matter description. Mass-radius profiles were determined by solving the hydrostatic equilibrium equations. Model parameter variations were used to assess the configuration stability here. TOV solutions he…

Microlensing of non-singular black holes at finite size: a ray tracing approach
Jens Boos, Hao Hu
https://arxiv.org/abs/2510.10282 https://arxiv.org/pdf/25…

Microlensing of non-singular black holes at finite size: a ray tracing approach
We study the gravitational microlensing of various static and spherically symmetric non-singular black holes (and horizonless, non-singular compact objects of similar size). For pointlike sources we extend the parametrized post-Newtonian lensing framework to fourth order, whereas for extended sources we develop a ray tracing approach via a simple radiative transfer model. Modelling non-relativistic proper motion of the lens in front of a background star we record the apparent brightness as a fu…

Inverse Language Modeling towards Robust and Grounded LLMs
Davide Gabrielli, Simone Sestito, Iacopo Masi
https://arxiv.org/abs/2510.01929 https://arxiv.org…

Inverse Language Modeling towards Robust and Grounded LLMs
The current landscape of defensive mechanisms for LLMs is fragmented and underdeveloped, unlike prior work on classifiers. To further promote adversarial robustness in LLMs, we propose Inverse Language Modeling (ILM), a unified framework that simultaneously 1) improves the robustness of LLMs to input perturbations, and, at the same time, 2) enables native grounding by inverting model outputs to identify potentially toxic or unsafe input triggers. ILM transforms LLMs from static generators into …