Tootfinder

CoEx -- Co-evolving World-model and Exploration
Minsoo Kim, Seung-won Hwang
https://arxiv.org/abs/2507.22281 https://arxiv.org/pdf/2507.22281

CoEx -- Co-evolving World-model and Exploration
Planning in modern LLM agents relies on the utilization of LLM as an internal world model, acquired during pretraining. However, existing agent designs fail to effectively assimilate new observations into dynamic updates of the world model. This reliance on the LLM's static internal world model is progressively prone to misalignment with the underlying true state of the world, leading to the generation of divergent and erroneous plans. We introduce a hierarchical agent architecture, CoEx, in wh…

Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
https://arxiv.org/abs/2506.21754 https://

Online design of experiments by active learning for nonlinear system identification
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and state-space models. We adapt various existing AL approaches for static model regression to the dynamic context, coupling them with a Kalman filter to update the model parameters recursively, and also cope with the presence of input and output constraints. We show the increased sample efficiency of the proposed approache…

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Ahmed Sabbah, Radi Jarrar, Samer Zein, David Mohaisen
https://arxiv.org/abs/2507.22772 https://

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and nine machine learning and deep learning algorithms, as well as Large Language Models (LLMs). Various feature types--static, dynamic, hybrid, semantic, and image-based--were considered. The results showed…

Replicating the behaviour of electric vehicle drivers using an agent-based reinforcement learning model
Zixin Feng, Qunshan Zhao, Alison Heppenstall
https://arxiv.org/abs/2507.21341

Replicating the behaviour of electric vehicle drivers using an agent-based reinforcement learning model
Despite the rapid expansion of electric vehicle (EV) charging networks, questions remain about their efficiency in meeting the growing needs of EV drivers. Previous simulation-based approaches, which rely on static behavioural rules, have struggled to capture the adaptive behaviours of human drivers. Although reinforcement learning has been introduced in EV simulation studies, its application has primarily focused on optimising fleet operations rather than modelling private drivers who make ind…

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen
https://arxiv.org/abs/2507.21743

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which c…

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…

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2506.21931

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism int…

A two-state generalisation of the strong collision model
Ola Kenji Forslund
https://arxiv.org/abs/2508.20727 https://arxiv.org/pdf/2508.20727

A two-state generalisation of the strong collision model
Muon spin relaxation is a powerful technique for probing static and dynamic local magnetic fields. The strong collision model, based on a Gaussian-Markovian process, is commonly used to account for dynamical effects. Yet, it remains limited in describing systems where the local field undergoes discrete state changes. To address this, I introduce a generalized two-state strong collision model that explicitly incorporates transitions between distinct local field environments during fluctuations. …

Scaling functions in the soft-wall AdS/QCD models
Zhongzheng Zhang, Danning Li, Lang Yu, Zhibin Li, Xinyang Wang
https://arxiv.org/abs/2507.22724 https://a…

Scaling functions in the soft-wall AdS/QCD models
We investigate the static scaling behavior of the chiral condensate near the two-flavor critical point within the framework of the soft-wall AdS/QCD. The scaling functions are extracted from the chiral order parameters and are found to precisely match those obtained through mean-field calculations. Additionally, it is also checked that the scaling functions are independent of the specific construction of the holographic model. Furthermore, we develop the formalism for calculating the chiral sus…

A Simulation-Based Conceptual Model for Tokenized Recycling: Integrating Blockchain, Market Dynamics, and Behavioral Economics
Atta Ul Mustafa
https://arxiv.org/abs/2507.19901 h…

A Simulation-Based Conceptual Model for Tokenized Recycling: Integrating Blockchain, Market Dynamics, and Behavioral Economics
This study develops a conceptual simulation model for a tokenized recycling incentive system that integrates blockchain infrastructure, market-driven pricing, behavioral economics, and carbon credit mechanisms. The model aims to address the limitations of traditional recycling systems, which often rely on static government subsidies and fail to generate sustained public participation. By introducing dynamic token values linked to real-world supply and demand conditions, as well as incorporating…

SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge
Lena John, Kheir Eddine Farfar, S\"oren Auer, Oliver Karras
https://arxiv.org/abs/2506.21819

SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge
Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-…

Active Liquid Crystal Theory Explains the Collective Organization of Microtubules in Human Mitotic Spindles
Colm P. Kelleher, Suryanarayana Maddu, Mustafa Basaran, Thomas M\"uller-Reichert, Michael J. Shelley, Daniel J. Needleman
https://arxiv.org/abs/2507.22273

Active Liquid Crystal Theory Explains the Collective Organization of Microtubules in Human Mitotic Spindles
How thousands of microtubules and molecular motors self-organize into spindles remains poorly understood. By combining static, nanometer-resolution, large-scale electron tomography reconstructions and dynamic, optical-resolution, polarized light microscopy, we test an active liquid crystal continuum model of mitotic spindles in human tissue culture cells. The predictions of this coarse-grained theory quantitatively agree with the experimentally measured spindle morphology and fluctuation spectr…

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 …

ReCatcher: Towards LLMs Regression Testing for Code Generation
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam, Foutse Khomh
https://arxiv.org/abs/2507.19390 https://…

ReCatcher: Towards LLMs Regression Testing for Code Generation
Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in correctness but also in code quality and performance. To address this, we present ReCatcher, a regression testing framework for Python code generation. ReCatcher systematically compares two LLMs, typically a current model and a candidate update, across three dimensions: logical correctness, static code quality, and exec…

Development of MR spectral analysis method robust against static magnetic field inhomogeneity
Shuki Maruyama, Hidenori Takeshima
https://arxiv.org/abs/2506.20897

Development of MR spectral analysis method robust against static magnetic field inhomogeneity
Purpose:To develop a method that enhances the accuracy of spectral analysis in the presence of static magnetic field B0 inhomogeneity. Methods:The authors proposed a new spectral analysis method utilizing a deep learning model trained on modeled spectra that consistently represent the spectral variations induced by B0 inhomogeneity. These modeled spectra were generated from the B0 map and metabolite ratios of the healthy human brain. The B0 map was divided into a patch size of subregions, and t…

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…

Time-varying ecological interactions characterise equilibrium and stability
Annalisa Caligiuri, Emile Emery, Leonardo Ferreira, Juan Garc\'ia-Castillo, Simon D. Lindner, Javier Molina-Hern\'andez, Nelson Aloysio Reis de Almeida Passos, V\'itor Hugo Ribeiro, Marika Sartore, Boxuan Wang, Violeta Calleja-Solanas
https://

Time-varying ecological interactions characterise equilibrium and stability
Ecological communities are composed of species interactions that respond to environmental fluctuations. Despite increasing evidence of temporal variation in these interactions, most theoretical frameworks remain rooted in static assumptions. Here, we develop and apply a time-varying network model to five long-term ecological datasets spanning diverse taxa and environments. Using a generalized Lotka-Volterra framework with environmental covariates, we quantify temporal rewiring of interspecific …

Static Contact Angles of Mixtures: Classical Density Functional Theory and Experimental Investigation
Benjamin Bursik, Nikolaos Karadimitriou, Holger Steeb, Joachim Gross
https://arxiv.org/abs/2506.21007

Static Contact Angles of Mixtures: Classical Density Functional Theory and Experimental Investigation
This work assesses a classical density functional theory (DFT) model for predicting macroscopic static contact angles of pure substances and mixtures by comparison to own experimental data. We employ a DFT with a Helmholtz energy functional based on the perturbed-chain statistical associating fluid theory (PC-SAFT) for the fluid-fluid interactions and an effective external potential for the solid-fluid interactions. The solid substrate is characterized by adjusting a single solid-solid interact…

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…

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…

Dissociation of one-dimensional excitons by static electric field
Adriana Garc\'ia, Alexander L\'opez, Jorge Quereda, Francisco Dom\'inguez-Adame
https://arxiv.org/abs/2507.17337

Dissociation of one-dimensional excitons by static electric field
The quantum states of an electron-hole pair in one-dimensional semiconductors under a static electric field are theoretically analyzed using a two-band model with on-site Coulomb interaction. In the absence of static field, the electron and hole are always bound, forming an exciton regardless of the Coulomb interaction strength, in contrast to what occurs in higher-dimensional semiconductors. The static field modifies the wave function of the electron-hole pair, turning bound states into contin…

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

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…

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 "…

Querying Graph-Relational Data
Michael J. Sullivan, Zhibo Chen, Elvis Pranskevichus, Robert J. Simmons, Victor Petrovykh, Alja\v{z} Mur Er\v{z}en, Yury Selivanov
https://arxiv.org/abs/2507.16089

Querying Graph-Relational Data
For applications that store structured data in relational databases, there is an impedance mismatch between the flat representations encouraged by relational data models and the deeply nested information that applications expect to receive. In this work, we present the graph-relational database model, which provides a flexible, compositional, and strongly-typed solution to this "object-relational mismatch." We formally define the graph-relational database model and present a static and dynamic …

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…

$\xi$-Based adaptive phase field model for quasi-static anti-plane fracture
Maria P. Fernando, S. M. Mallikarjunaiah
https://arxiv.org/abs/2506.12360 https…

$ξ$-Based adaptive phase field model for quasi-static anti-plane fracture
The $ξ$-based spatially adaptive three-field variable phase-field model for quasi-static anti-plane crack propagation is introduced. A dynamically optimized regularization length is integrated to improve computational efficiency and accuracy in numerical approximations. A local adaptive mesh refinement strategy is developed, which maintains an optimal balance between mesh resolution and the accurate depiction of fractures using the \textsf{AT1} diffuse interface model. The total energy functio…

VLA-Mark: A cross modal watermark for large vision-language alignment model
Shuliang Liu, Qi Zheng, Jesse Jiaxi Xu, Yibo Yan, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, Xuming Hu
https://arxiv.org/abs/2507.14067

VLA-Mark: A cross modal watermark for large vision-language alignment model
Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark, a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination. Our approach integrates multiscale visual-textua…

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
Jianmin Hu, Minxian Xu, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.17133 https://

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
In recent years, the Mixture-of-Experts (MoE) architecture has been widely applied to large language models (LLMs), providing a promising solution that activates only a subset of the model's parameters during computation, thereby reducing overall memory requirements and allowing for faster inference compared to dense models. Despite these advantages, existing systems still face issues of low efficiency due to static model placement and lack of dynamic workloads adaptation. This leads to subopti…

Dynamical theory for spherical black holes in modified gravity
Asier Alonso-Bardaji, David Brizuela
https://arxiv.org/abs/2507.19380 https://arxiv.org/pdf/…

Dynamical theory for spherical black holes in modified gravity
We provide a general algorithm to construct a Hamiltonian, such that its dynamical flow covariantly defines any given spherically symmetric and static metric. This Hamiltonian is defined as a linear combination of the standard (general relativistic) radial diffeomorphism constraint plus a Hamiltonian constraint that is appropriately deformed as compared to its corresponding form in general relativity though it does not include higher-derivative terms. Therefore, given a static model of spherica…

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…

Model Checking as Program Verification by Abstract Interpretation (Extended Version)
Paolo Baldan, Roberto Bruni, Francesco Ranzato, Diletta Rigo
https://arxiv.org/abs/2506.05525 …

Model Checking as Program Verification by Abstract Interpretation (Extended Version)
Abstract interpretation offers a powerful toolset for static analysis, tackling precision, complexity and state-explosion issues. In the literature, state partitioning abstractions based on (bi)simulation and property-preserving state relations have been successfully applied to abstract model checking. Here, we pursue a different track in which model checking is seen as an instance of program verification. To this purpose, we introduce a suitable language-called MOKA (for MOdel checking as abst…

DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection
Ye Tian, Liangliang Song, Peng Qian, Yanbin Wang, Jianguo Sun, Yifan Jia
https://arxiv.org/abs/2506.20123

DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection
The detection of malicious accounts on Ethereum - the preeminent DeFi platform - is critical for protecting digital assets and maintaining trust in decentralized finance. Recent advances highlight that temporal transaction evolution reveals more attack signatures than static graphs. However, current methods either fail to model continuous transaction dynamics or incur high computational costs that limit scalability to large-scale transaction networks. Furthermore, current methods fail to consid…

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 …

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…

Sandwich: Separating Prefill-Decode Compilation for Efficient CPU LLM Serving
Juntao Zhao, Jiuru Li, Chuan Wu
https://arxiv.org/abs/2507.18454 https://arxi…

Sandwich: Separating Prefill-Decode Compilation for Efficient CPU LLM Serving
Utilizing CPUs to serve large language models (LLMs) is a resource-friendly alternative to GPU serving. Existing CPU-based solutions ignore workload differences between the prefill and the decode phases of LLM inference, applying a static per-NUMA (Non-Uniform Memory Access) node model partition and utilizing vendor libraries for operator-level execution, which is suboptimal. We propose Sandwich, a hardware-centric CPU-based LLM serving engine that uses different execution plans for the prefill…

Chimera games emerging from coevolutionary dynamics with endogenous feedbacks
Federico Maria Quetti, Andrea Civilini, Giacomo Frigerio, Silvia Figini, Giacomo Livan, Vito Latora
https://arxiv.org/abs/2506.20603

Chimera games emerging from coevolutionary dynamics with endogenous feedbacks
From climate change to financial bubbles and wars, the rapid pace of change in our world clearly shows that the commonly adopted assumption of a static environment in evolutionary game-theoretic models is often unrealistic. In this work, we introduce a coevolutionary game model where the game environment and the strategy dynamics are mutually coupled. In particular, the game played at each time is endogenously defined as a convex combination of two social dilemmas, weighted by the current abund…

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…

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Masudul Hasan Masud Bhuiyan, Gianluca De Stefano, Giancarlo Pellegrino, Cristian-Alexandru Staicu
https://arxiv.org/abs/2506.18191

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Static analysis plays a key role in finding bugs, including security issues. A critical step in static analysis is building accurate call graphs that model function calls in a program. However, due to hard-to-analyze language features, existing call graph construction algorithms for JavaScript are neither sound nor complete. Prior work shows that even advanced solutions produce false edges and miss valid ones. In this work, we assist these tools by identifying missed call edges. Our main idea i…

2D Su-Schrieffer-Heeger Model with static domain walls and quasiperiodic disorder
Surajit Mandal, Satyaki Kar
https://arxiv.org/abs/2506.17786 https://

2D Su-Schrieffer-Heeger Model with static domain walls and quasiperiodic disorder
We revisit the problem of a two dimensional Su-Schrieffer-Heeger (SSH) model on a square lattice to first minutely analyze its spectra including zero energy states (ZES) and in-gap states both under periodic and open boundaries. Thereafter, a series of antiphase domain walls (DW), distributed along two orthogonal lines, are introduced in the lattice with SSH like hoppings and this causes the ZES to localize not only at the corners but also at the intersection of the lines of domain walls. In-ga…

Hatevolution: What Static Benchmarks Don't Tell Us
Chiara Di Bonaventura, Barbara McGillivray, Yulan He, Albert Mero\~no-Pe\~nuela
https://arxiv.org/abs/2506.12148

Hatevolution: What Static Benchmarks Don't Tell Us
Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speec…

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…

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.

JT de Sitter Gravity as a Model of Coleman-de Luccia Tunneling
Sidan A, Tom Banks
https://arxiv.org/abs/2506.09283 https://arxiv.org/…

JT de Sitter Gravity as a Model of Coleman-de Luccia Tunneling
We interpret the Euclidean solution of JT de Sitter gravity as the Coleman-de Luccia instanton for the decay of the low-entropy horizon of its static patch solution into either a Big Crunch or an infinite-entropy Lorentzian de Sitter cosmology. As in previous work by one of the authors, the Big Crunch is interpreted as bounding the entropy of the static state. The principle of detailed balance then guarantees it will transition back to a higher entropy causal diamond in the expanding cosmology.…

The Supermarket Model on a Dynamic Regular Hypergraph
John Fernley, Bal\'azs Gerencs\'er
https://arxiv.org/abs/2507.03931 https://

The Supermarket Model on a Dynamic Regular Hypergraph
The supermarket model is a system of $n$ queues each with serving rates $1$ and arrival rates $λ$ per vertex, where tasks will move on arrival to the shortest adjacent queue. We consider the supermarket model in the small $λ$ regime on a large dynamic configuration hypergraph with stubs swapping their hyperedge membership at rate $κ$. This interpolates previous investigations of the supermarket model on static graphs of bounded degree (where an exponential tail produces a logarithmic queue…

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…

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…

Computing Tree Structures in Anonymous Graphs via Mobile Agents
Prabhat Kumar Chand, Manish Kumar, Anisur Rahaman Molla
https://arxiv.org/abs/2506.19365 ht…

Computing Tree Structures in Anonymous Graphs via Mobile Agents
Minimum Spanning Tree (MST) and Breadth-First Search (BFS) tree constructions are classical problems in distributed computing, traditionally studied in the message-passing model, where static nodes communicate via messages. This paper investigates MST and BFS tree construction in an agent-based network, where mobile agents explore a graph and compute. Each node hosts one agent, and communication occurs when agents meet at a node. We consider $n$ agents initially dispersed (one per node) in an a…

Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
Aaron Ho (MIT Plasma Science and Fusion Center, Cambridge, USA), Lorenzo Zanisi (UKAEA Culham Centre for Fusion Energy, Abingdon, UK), Bram de Leeuw (Radboud University, Nijmegen, Netherlands), Vincent Galvan (MIT Plasma Science and Fusion Center, Cambridge, USA), Pablo Rodriguez-Fernandez (MIT Plasma Science and Fusion Center, Cambridge, USA), Nath…

Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
This work demonstrates a proof-of-principle for using uncertainty-aware architectures, in combination with active learning techniques and an in-the-loop physics simulation code as a data labeller, to construct efficient datasets for data-driven surrogate model generation. Building off of a previous proof-of-principle successfully demonstrating training set reduction on static pre-labelled datasets, using the ADEPT framework, this strategy was applied again to the plasma turbulent transport prob…

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…

Aggregated Individual Reporting for Post-Deployment Evaluation
Jessica Dai, Inioluwa Deborah Raji, Benjamin Recht, Irene Y. Chen
https://arxiv.org/abs/2506.18133

Aggregated Individual Reporting for Post-Deployment Evaluation
The need for developing model evaluations beyond static benchmarking, especially in the post-deployment phase, is now well-understood. At the same time, concerns about the concentration of power in deployed AI systems have sparked a keen interest in 'democratic' or 'public' AI. In this work, we bring these two ideas together by proposing mechanisms for aggregated individual reporting (AIR), a framework for post-deployment evaluation that relies on individual reports from the public. An AIR mech…

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 …

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
Malikussaid, Sutiyo
https://arxiv.org/abs/2506.20102 h…

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
The convergence of IT and OT has created hyper-connected ICS, exposing critical infrastructure to a new class of adaptive, intelligent adversaries that render static defenses obsolete. Existing security paradigms often fail to address a foundational "Trinity of Trust," comprising the fidelity of the system model, the integrity of synchronizing data, and the resilience of the analytical engine against sophisticated evasion. This paper introduces the ARC framework, a method for achieving analytic…

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…

Discrete time shadow price revisited
Tomasz Rogala, {\L}ukasz Stettner
https://arxiv.org/abs/2506.14708 https://arxiv.org/pdf/2506.14…

Discrete time shadow price revisited
In the paper discrete time shadow price is constructed for the market with several assets with given bid and ask prices. Shadow price is the price such that the problem of optimal utility from terminal wealth on the market without transaction costs gives the same value function as in the case of bid and ask prices. In the paper we solve first static problem for two assets and then we construct the shadow price for dynamic model. Finally we present construction of the shadow price for several as…

Steering Conceptual Bias via Transformer Latent-Subspace Activation
Vansh Sharma, Venkat Raman
https://arxiv.org/abs/2506.18887 https://

Steering Conceptual Bias via Transformer Latent-Subspace Activation
This work examines whether activating latent subspaces in language models (LLMs) can steer scientific code generation toward a specific programming language. Five causal LLMs were first evaluated on scientific coding prompts to quantify their baseline bias among four programming languages. A static neuron-attribution method, perturbing the highest activated MLP weight for a C++ or CPP token, proved brittle and exhibited limited generalization across prompt styles and model scales. To address th…

Static vs dynamic rough energy landscapes: Where is diffusion faster?
Dmitrii E. Makarov, Peter Sollich
https://arxiv.org/abs/2506.02195 https://

Static vs dynamic rough energy landscapes: Where is diffusion faster?
Molecules in dense environments, such as biological cells, are subjected to forces that fluctuate both in time and in space. While spatial fluctuations are captured by Lifson-Jackson-Zwanzig's model of "diffusion in a rough potential", and temporal fluctuations are often viewed as leading to additional friction effects, a unified view where the environment fluctuates both in time and in space is currently lacking. Here we introduce a discrete-state model of a landscape fluctuating both in time …

General Signal Model and Capacity Limit for Rydberg Quantum Information System
Jieao Zhu, Linglong Dai
https://arxiv.org/abs/2506.23455 https://

General Signal Model and Capacity Limit for Rydberg Quantum Information System
Rydberg atomic receivers represent a transformative approach to achieving high-sensitivity, broadband, and miniaturized radio frequency (RF) reception. However, existing static signal models for Rydberg atomic receivers rely on the steady-state assumption of atomic quantum states, which cannot fully describe the signal reception process of dynamic signals. To fill in this gap, in this paper, we present a general model to compute the dynamic signal response of Rydberg atomic receivers in closed …

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…

Agentic Semantic Control for Autonomous Wireless Space Networks: Extending Space-O-RAN with MCP-Driven Distributed Intelligence
Eduardo Baena, Paolo Testolina, Michele Polese, Sergi Aliaga, Andrew Benincasa, Dimitrios Koutsonikolas, Josep Jornet, Tommaso Melodia
https://arxiv.org/abs/2506.10925

Agentic Semantic Control for Autonomous Wireless Space Networks: Extending Space-O-RAN with MCP-Driven Distributed Intelligence
Lunar surface operations impose stringent requirements on wireless communication systems, including autonomy, robustness to disruption, and the ability to adapt to environmental and mission-driven context. While Space-O-RAN provides a distributed orchestration model aligned with 3GPP standards, its decision logic is limited to static policies and lacks semantic integration. We propose a novel extension incorporating a semantic agentic layer enabled by the Model Context Protocol (MCP) and Agent-…

rt-RISeg: Real-Time Model-Free Robot Interactive Segmentation for Active Instance-Level Object Understanding
Howard H. Qian, Yiting Chen, Gaotian Wang, Podshara Chanrungmaneekul, Kaiyu Hang
https://arxiv.org/abs/2507.10776

rt-RISeg: Real-Time Model-Free Robot Interactive Segmentation for Active Instance-Level Object Understanding
Successful execution of dexterous robotic manipulation tasks in new environments, such as grasping, depends on the ability to proficiently segment unseen objects from the background and other objects. Previous works in unseen object instance segmentation (UOIS) train models on large-scale datasets, which often leads to overfitting on static visual features. This dependency results in poor generalization performance when confronted with out-of-distribution scenarios. To address this limitation, …

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…

Beyond Compliance: A User-Autonomy Framework for Inclusive and Customizable Web Accessibility
Lalitha A R
https://arxiv.org/abs/2506.10324 https://

Beyond Compliance: A User-Autonomy Framework for Inclusive and Customizable Web Accessibility
This paper proposes a shift from compliance-centered web accessibility to a care-driven model that prioritizes user autonomy, using neurodivergent users as a catalyst case for broader personalization needs. While accessibility standards offer a flexible framework, they are often interpreted and implemented as static compliance checklists, our approach reframes it as a flexible, user-centered process. We introduce a customizable Comfort Mode framework that allows users to adapt interface setting…

Stress Softening Damage in Strongly Nonlinear Viscoelastic Soft Materials A Physics Informed Data Driven Constitutive Model with Time Temperature Coupling
Alireza Ostadrahimi, Amir Teimouri, Kshitiz Upadhyay, Guoqiang Li
https://arxiv.org/abs/2507.12694

Stress Softening Damage in Strongly Nonlinear Viscoelastic Soft Materials A Physics Informed Data Driven Constitutive Model with Time Temperature Coupling
This study presents a novel physics informed, data-driven modeling framework for capturing the strongly nonlinear thermo-viscoelastic behavior of soft materials exhibiting stress softening, with emphasis on the Mullins effect. Unlike previous approaches limited to quasi-static or isothermal conditions, our model unifies rate dependence, temperature sensitivity, large strain cyclic loading, and evolving damage mechanisms. Thermodynamic admissibility is ensured via a custom loss function that emb…

Static Spherical Vacuum Solution to Bumblebee Gravity with Time-like VEVs
Hao Li, Jie Zhu
https://arxiv.org/abs/2506.17957 https://ar…

Static Spherical Vacuum Solution to Bumblebee Gravity with Time-like VEVs
The static spherical vacuum solution in a bumblebee gravity model where the bumblebee field \(B_μ\) has a time-like vacuum expectation value \(b_μ\) is studied. We show that in general curved spacetime solutions are not allowed and only the Minkowski spacetime exists. However, it is surprising that a non-trivial solution can be obtained so long as a unique condition for the vacuum expectation \(b^2\equiv -b^μb_μ=2/κ\), where \(κ=8πG\) is satisfied. We argue that naturally this solution i…

Parallelism Meets Adaptiveness: Scalable Documents Understanding in Multi-Agent LLM Systems
Chengxuan Xia, Qianye Wu, Sixuan Tian, Yilun Hao
https://arxiv.org/abs/2507.17061 htt…

Parallelism Meets Adaptiveness: Scalable Documents Understanding in Multi-Agent LLM Systems
Large language model (LLM) agents have shown increasing promise for collaborative task completion. However, existing multi-agent frameworks often rely on static workflows, fixed roles, and limited inter-agent communication, reducing their effectiveness in open-ended, high-complexity domains. This paper proposes a coordination framework that enables adaptiveness through three core mechanisms: dynamic task routing, bidirectional feedback, and parallel agent evaluation. The framework allows agents…

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets
Mathurin Videau, Badr Youbi Idrissi, Alessandro Leite, Marc Schoenauer, Olivier Teytaud, David Lopez-Paz
https://arxiv.org/abs/2506.14761

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets
Tokenization imposes a fixed granularity on the input text, freezing how a language model operates on data and how far in the future it predicts. Byte Pair Encoding (BPE) and similar schemes split text once, build a static vocabulary, and leave the model stuck with that choice. We relax this rigidity by introducing an autoregressive U-Net that learns to embed its own tokens as it trains. The network reads raw bytes, pools them into words, then pairs of words, then up to 4 words, giving it a mul…

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Song Wang, Zhen Tan, Zihan Chen, Shuang Zhou, Tianlong Chen, Jundong Li
https://arxiv.org/abs/2506.17784

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topo…

Computation-resource-efficient Task-oriented Communications
Jingwen Fu, Ming Xiao, Chao Ren, Mikael Skoglund
https://arxiv.org/abs/2507.07422 https://

Computation-resource-efficient Task-oriented Communications
The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile phones and UAVs, make TOC challenging for many tasks. To address the problem, we propose a novel TOC method with two models: a static and a dynamic model. In the static model, we apply a neural network (NN) as a task-oriented encoder (TOE) when there is no c…

Agentic Distributed Computing
Ajay D. Kshemkalyani, Manish Kumar, Anisur Rahaman Molla, Gokarna Sharma
https://arxiv.org/abs/2507.04459 https://

Agentic Distributed Computing
The most celebrated and extensively studied model of distributed computing is the {\em message-passing model,} in which each vertex/node of the (distributed network) graph corresponds to a static computational device that communicates with other devices through passing messages. In this paper, we consider the {\em agentic model} of distributed computing which extends the message-passing model in a new direction. In the agentic model, computational devices are modeled as relocatable or mobile co…

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…

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…

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Ziyue Li, Yang Li, Tianyi Zhou
https://arxiv.org/abs/2507.07996 https://arxiv.org/pdf/2507.07996 https://arxiv.org/html/2507.07996
arXiv:2507.07996v1 Announce Type: new
Abstract: Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.
toXiv_bot_toot

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…

Charged black holes surrounded by shells of charged particles
David Fajman, Hannah Seer
https://arxiv.org/abs/2506.21293 https://arxi…

Charged black holes surrounded by shells of charged particles
We construct new static solutions to the Einstein-Vlasov-Maxwell system. These solutions model Reissner-Nordstrom-type black holes with surrounding shells of charged matter.

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
Hao Wu, Haijun Wang, Shangwang Li, Yin Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.08561

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users. State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited state variable-attackers manipulate token prices to gain illicit profits. In this paper, we propose PriceSleuth, a novel method that leverages the Large Language Model (LLM) and static analysis to detect Price Manipulation (PM) attacks proactively.…

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…

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…

Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial
Neil K. R. Sehgal, Manuel Tonneau, Andy Tan, Shivan J. Mehta, Alison Buttenheim, Lyle Ungar, Anish K. Agarwal, Sharath Chandra Guntuku
https://arxiv.org/abs/2507.08211

Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial
Large language model (LLM) chatbots show increasing promise in persuasive communication. Yet their real-world utility remains uncertain, particularly in clinical settings where sustained conversations are difficult to scale. In a pre-registered randomized controlled trial, we enrolled 915 U.S. adults (ages 45-75) who had never completed colorectal cancer (CRC) screening. Participants were randomized to: (1) no message control, (2) expert-written patient materials, (3) single AI-generated messag…

Wetting and Pattern Formation in Non-Reciprocal Ternary Phase Separation
Xiao Ma, Michael E. Cates
https://arxiv.org/abs/2506.18702 https://

Wetting and Pattern Formation in Non-Reciprocal Ternary Phase Separation
Non-reciprocal interactions are among the simplest mechanisms that drive a physical system out of thermal equilibrium, leading to novel phenomena such as oscillatory pattern formation. In this paper, we introduce a ternary phase separation model, with non-reciprocal interactions between two of the three phases and a spectator phase that mimics a boundary. Through numerical simulations, we uncover three distinct phase behaviours: a quasi-static regime, characterized by well-defined non-equilibri…

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
Fengxiao Tang, Huan Li, Ming Zhao, Zongzong Wu, Shisong Peng, Tao Yin
https://arxiv.org/abs/2507.11310

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
Verifying the credibility of Cyber Threat Intelligence (CTI) is essential for reliable cybersecurity defense. However, traditional approaches typically treat this task as a static classification problem, relying on handcrafted features or isolated deep learning models. These methods often lack the robustness needed to handle incomplete, heterogeneous, or noisy intelligence, and they provide limited transparency in decision-making-factors that reduce their effectiveness in real-world threat envi…

On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data: Extended Version
Ter\'ezia Slanin\'akov\'a, Jaroslav Olha, David Proch\'azka, Matej Antol, Vlastislav Dohnal
https://arxiv.org/abs/2507.05865

On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data: Extended Version
One of the main challenges within the growing research area of learned indexing is the lack of adaptability to dynamically expanding datasets. This paper explores the dynamization of a static learned index for complex data through operations such as node splitting and broadening, enabling efficient adaptation to new data. Furthermore, we evaluate the trade-offs between static and dynamic approaches by introducing an amortized cost model to assess query performance in tandem with the build costs…

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Zengjue Chen, Runliang Niu, He Kong, Qi Wang
https://arxiv.org/abs/2506.08440

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Recent advances in Vision-Language-Action (VLA) model have demonstrated strong generalization capabilities across diverse scenes, tasks, and robotic platforms when pretrained at large-scale datasets. However, these models still require task-specific fine-tuning in novel environments, a process that relies almost exclusively on supervised fine-tuning (SFT) using static trajectory datasets. Such approaches neither allow robot to interact with environment nor do they leverage feedback from live ex…

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.…

Adaptive finite element convergence analysis of AT1 phase-field model for quasi-static fracture in strain-limiting solids
Ram Manohar, S. M. Mallikarjunaiah
https://arxiv.org/abs/2507.00376

Adaptive finite element convergence analysis of AT1 phase-field model for quasi-static fracture in strain-limiting solids
This research rigorously investigates the convergence of adaptive finite element methods for regularized variational models of quasi-static brittle fracture in elastic solids. We specifically examine a novel Ambrosio-Tortorelli (AT1) phase-field model within the framework of elasticity theories, particularly for material models characterized by an algebraically nonlinear stress-strain relationship. Two distinct and novel adaptive mesh refinement algorithms, underpinned by robust local error ind…

Tunable anyonic permeability across ${\mathbb{Z}_2}$ spin liquid junctions
Sayak Bhattacharjee, Soumya Sur, Adhip Agarwala
https://arxiv.org/abs/2506.17394

Tunable anyonic permeability across ${\mathbb{Z}_2}$ spin liquid junctions
We introduce two classes of junctions in a toric code, a prototypical model of a $\mathbb{Z}_2$ quantum spin liquid, and study the nature of anyonic transport across them mediated by Zeeman fields. In the first class of junctions, termed potential barrier junctions, the charges sense effective static potentials and a change in the band mass. In a particular realization, while the junction is completely transparent to the electric charge, magnetic charge transmission is allowed only after a crit…

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…

Strategy Evolution in the Adoption of Conservation Tillage Technology under Time Preference Heterogeneity and Lemon Market: Insights from Evolutionary Dynamics
Dingyi Wang, Ruqiang Guo, Qian Lu
https://arxiv.org/abs/2507.15497

Strategy Evolution in the Adoption of Conservation Tillage Technology under Time Preference Heterogeneity and Lemon Market: Insights from Evolutionary Dynamics
The promotion of Conservation Tillage Technology (CTT) is critical for mitigating global soil degradation, yet their actual adoption rates remain substantially lower than anticipated targets. Existing research predominantly focuses on static factor analyses, failing to adequately capture the dynamic evolutionary mechanisms of farmer strategic interactions and the impacts of information asymmetries in agricultural markets. This study constructs an evolutionary game model integrating heterogeneou…

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…

"Odd" Toric Code in a tilted field: Higgs-confinement multicriticality, spontaneous self-duality symmetry breaking, and valence bond solids
Umberto Borla, Ayush De, Snir Gazit
https://arxiv.org/abs/2507.16523

"Odd" Toric Code in a tilted field: Higgs-confinement multicriticality, spontaneous self-duality symmetry breaking, and valence bond solids
We investigate the quantum phase diagram of an ``odd'' variant of the two-dimensional Ising Fradkin--Shenker model, characterized by a uniform background of static $e$ and $m$ charges. Using large-scale tensor network and exact diagonalization methods, we determine the topology of the phase diagram, identifying an ``odd'' deconfined phase, confinement- and Higgs-induced valence bond solids (VBS), and a trivial paramagnet. Most notably, we uncover an exotic multicritical point along the self-dua…

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
Anton Changalidis, Aki H\"arm\"a
https://arxiv.org/abs/2506.14704

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
This paper studies how the model architecture and data configurations influence the empirical memorization capacity of generative transformers. The models are trained using synthetic text datasets derived from the Systematized Nomenclature of Medicine (SNOMED) knowledge graph: triplets, representing static connections, and sequences, simulating complex relation patterns. The results show that embedding size is the primary determinant of learning speed and capacity, while additional layers provi…

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…

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…

deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnesses
Georgios Androutsopoulos, Antonio Bianchi
https://arxiv.org/abs/2506.15648

deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnesses
Although Rust ensures memory safety by default, it also permits the use of unsafe code, which can introduce memory safety vulnerabilities if misused. Unfortunately, existing tools for detecting memory bugs in Rust typically exhibit limited detection capabilities, inadequately handle Rust-specific types, or rely heavily on manual intervention. To address these limitations, we present deepSURF, a tool that integrates static analysis with Large Language Model (LLM)-guided fuzzing harness generat…

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
Hang Su, Jun Luo, Chang Liu, Xiao Yang, Yichi Zhang, Yinpeng Dong, Jun Zhu
https://arxiv.org/abs/2506.23844

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities. While these capabilities significantly expand the functional scope of AI, they also introduce qualitatively novel security risks - such as memory poisoning, tool misuse, reward hacking, and emergent mi…

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Songlin Zhai, Guilin Qi, Yuan Meng
https://arxiv.org/abs/2507.08704

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Knowledge graphs (KGs) play a critical role in enhancing large language models (LLMs) by introducing structured and grounded knowledge into the learning process. However, most existing KG-enhanced approaches rely on parameter-intensive fine-tuning, which risks catastrophic forgetting and degrades the pretrained model's generalization. Moreover, they exhibit limited adaptability to real-time knowledge updates due to their static integration frameworks. To address these issues, we introduce the f…

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
Ishika Singh, Ankit Goyal, Stan Birchfield, Dieter Fox, Animesh Garg, Valts Blukis
https://arxiv.org/abs/2506.01196

OG-VLA: 3D-Aware Vision Language Action Model via Orthographic Image Generation
We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) with the robustness of 3D-aware policies. We address the challenge of mapping natural language instructions and multi-view RGBD observations to quasi-static robot actions. 3D-aware robot policies achieve state-of-the-art performance on precise robot manipulation tasks, but struggle with generalization to unseen instructions, scenes, and objects. On …

Spherically Symmetric Potentials in Quadratic $f(R)$ Gravity
Roger Anderson Hurtado
https://arxiv.org/abs/2506.15123 https://arxiv.or…

Spherically Symmetric Potentials in Quadratic $f(R)$ Gravity
We study the gravitational potential arising from static, spherically symmetric sources in a modified gravity theory described by a quadratic $f(R)$ model. The linearized field equations lead to a fourth-order differential equation, whose solution includes Newtonian and Yukawa-like terms. Imposing regularity at the origin and asymptotic flatness fixes all integration constants, making the potential fully determined by the mass distribution. We analyze classical profiles such as Plummer, Hernqui…

Dark Energy Behavior from Static Equation of State in Non-Minimally Coupled Gravity with Scalar Deformation
N. Myrzakulov, S. H. Shekh, S. R. Bhoyar, Anirudh Pradhan
https://arxiv.org/abs/2507.05298

Dark Energy Behavior from Static Equation of State in Non-Minimally Coupled Gravity with Scalar Deformation
In this study, we explore the cosmological implications of a modified gravity theory characterized by the function \( f(R, Σ, T) \), where \( R \) is the Ricci scalar, \( Σ\) represents a geometric deformation term, and \( T \) denotes the trace of the energy-momentum tensor. The model is reconstructed under the framework of three fundamental energy conditions: Null Energy Condition (NEC), Dominant Energy Condition (DEC), and Strong Energy Condition (SEC). We derive the corresponding Hubble p…

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^…