Tootfinder

Met$^2$Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems
Shaohan Li, Hao Yang, Min Chen, Xiaolin Qin
https://arxiv.org/abs/2507.17189

Met$^2$Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems
The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances have been made by the \textbf{end-to-end methods}, thanks to deep learning techniques, but they face limitations of \textit{representation inconsistency} in multivariable integration and struggle to effectively capture the dependency between variables, which is required in complex weather systems. Treating different variables as distinct modalities and appl…

Dual Iterative Learning Control for Multiple-Input Multiple-Output Dynamics with Validation in Robotic Systems
Jan-Hendrik Ewering, Alessandro Papa, Simon F. G. Ehlers, Thomas Seel, Michael Meindl
https://arxiv.org/abs/2509.18723

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
Jinming Fan, Chao Qian, Shaodong Zhou
https://arxiv.org/abs/2509.17810 https://

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
Accurate prediction of molecular properties in complex chemical systems is crucial for accelerating material discovery and chemical innovation.However,current computational methods often struggle to capture the intricate compositional interplay across complex chemical systems,from intramolecular bonds to intermolecular forces.In this work,we introduce MesoNet,a novel framework founded on the principle of multi-representation learning and specifically designed for multi-molecule modeling.The cor…

A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination
Chengjin Wang, Yanmin Zhou, Zhipeng Wang, Zheng Yan, Feng Luan, Shuo Jiang, Runjie Shen, Hongrui Sang, Bin He
https://arxiv.org/abs/2509.16963

A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination
Humans and animals can make real-time adjustments to movements by imagining their action outcomes to prevent unanticipated or even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception-action loops to handle physical interaction-induced uncertainties in perception and system modeling within complex systems. Inspired by the action-awareness capability of animal intelligence, this study proposes an imaginati…

Filtering amplitude dependence of correlation dynamics in complex systems: application to the cryptocurrency market
Marcin W\k{a}torek, Marija Bezbradica, Martin Crane, Jaros{\l}aw Kwapie\'n, Stanis{\l}aw Dro\.zd\.z
https://arxiv.org/abs/2509.18820

Filtering amplitude dependence of correlation dynamics in complex systems: application to the cryptocurrency market
Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient ρ(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for…

Real decomposable maps on operator systems
David P. Blecher, Christiaan H. Pretorius
https://arxiv.org/abs/2509.17708 https://arxiv.org/pdf/2509.17708

Real decomposable maps on operator systems
We initiate and study the theory of ``real decomposable maps" between real operator systems. (Formally, this is new even in the complex case, which hitherto has restricted itself to the case where the systems are complex $C^*$-algebras.) We investigate how the definition interacts with the existing theory (which it generalizes) and with the complexification. A surprising term appears in the `Jordan decomposition' of real decomposable maps. This term constitutes a new class of completely bounded…

Complex Frequency Fingerprint: Interacting Driven Non-Hermitian Skin Effect
Zhesen Yang, Zihan Wang, Juntao Huang, Zijian Zheng, Jiangping Hu
https://arxiv.org/abs/2509.18828 ht…

I've probably mentioned that I'm working on switching #Gentoo from our half-broken eselect-ldso logic to #FlexiBLAS. This also involves a transition period where both setups would be supported.
A good thing is that the switch is ABI-compatible with the previous state (or at least it's supposed to be — we're working with upstream on fixing function coverage). Since libblas.so, liblapack.so and the rest are replaced by symlinks, programs that link to them will simply start using FlexiBLAS. So far, so good.
Unfortunately, switching the other way doesn't work as well. Stuff newly built against our libblas.so & co. symlinks naturally reads FlexiBLAS's SONAME from them, and links to libflexiblas directly. So should you decide to switch back, some packages will stay linked to FlexiBLAS and will need to rebuilt.
In order to avoid this, I would have to replace the symlinks with wrapper libraries, having libblas.so.3 and so on SONAMEs, and linking to libflexiblas. Unfortunately, a dummy wrapper isn't going to work — the linker will complain about using indirect symbols from libflexiblas.so. So I would probably have to "reexport" their symbols somehow, and ideally split into appropriate libraries, so that `-Wl,--as-needed` wouldn't drop some of them. But how to do that?
Well, let's look at the existing logic for eselect-ldso — clearly both BLIS and OpenBLAS create some wrappers. So I've spent some time investigating upstream Makefiles, and literally couldn't find the respective targets. I mean, these are quite complex Makefiles, but I'm grepping hard and can't find even a partial match.
As it turns out, these Makefile targets are added by Gentoo-specific patches. And these patches are just horrible. In case of OpenBLAS, they create the wrapper libraries by linking all the relevant .o files from OpenBLAS build, plus the shared OpenBLAS library. So the OpenBLAS symbols relevant to each interface end up duplicated in libblas.so, liblapack.so, etc., and apparently the symbols needed by them are taken from libopenblas.so. The individual interface libraries aren't even linked to one another, so they expose their own duplicate symbols, but use the implementation from OpenBLAS instead.
BLIS is even worse — the patch is simply creating libblas.so and libcblas.so, using all BLIS objects directly, plus symbol visibility to hide symbols irrelevant to the library. So yes, libblis.so, libblas.so and libcblas.so are roughly three separate copies of the same library, differing only in symbol visibility. And of course libcblas.so doesn't use libblas.so.
Truly #GSoC quality.

SSNet: Flexible and robust channel extrapolation for fluid antenna systems enabled by an self-supervised learning framework
Yuan Gao, Yiming Liu, Runze Yu, Shengli Liu, Yanliang Jin, Shunqing Zhang, Shugong Xu, Xiaoli Chu
https://arxiv.org/abs/2509.17797

SSNet: Flexible and robust channel extrapolation for fluid antenna systems enabled by an self-supervised learning framework
Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical structure. Traditional methods, such as pilot-based interpolation and compressive sensing, are not only computationally intensive but also lack adaptability. Current extrapolation techniques relying on rigid parametric models do not accommodate the dynamic env…

Braiding of dynamical eigenvalues of Hermitian bosonic Kitaev chains
Heming Wang, Shanhui Fan
https://arxiv.org/abs/2509.18879 https://arxiv.org/pdf/2509.1…

Braiding of dynamical eigenvalues of Hermitian bosonic Kitaev chains
In quantum mechanics, observables correspond to Hermitian operators, and the spectra are restricted to be real. However, the dynamics of the underlying fields may allow complex eigenvalues and therefore create the possibility of braiding structures. Here we study the braiding of dynamical eigenvalues in quantum systems by considering Hermitian bosonic Kitaev chains with multiple bands. The dynamics of the quantum fields in these systems are described by their dynamic matrices, which have comple…

Mapple: A Domain-Specific Language for Mapping Distributed Heterogeneous Parallel Programs
Anjiang Wei, Rohan Yadav, Hang Song, Wonchan Lee, Ke Wang, Alex Aiken
https://arxiv.org/abs/2507.17087

Mapple: A Domain-Specific Language for Mapping Distributed Heterogeneous Parallel Programs
Optimizing parallel programs for distributed heterogeneous systems remains a complex task, often requiring significant code modifications. Task-based programming systems improve modularity by separating performance decisions from core application logic, but their mapping interfaces are often too low-level. In this work, we introduce Mapple, a high-level, declarative programming interface for mapping distributed applications. Mapple provides transformation primitives to resolve dimensionality mi…

From PREVENTion to REACTion: Enhancing Failure Resolution in Naval Systems
Maria Teresa Rossi, Leonardo Mariani, Oliviero Riganelli
https://arxiv.org/abs/2508.15584 https://

From PREVENTion to REACTion: Enhancing Failure Resolution in Naval Systems
Complex and large industrial systems often misbehave, for instance, due to wear, misuse, or faults. To cope with these incidents, it is important to timely detect their occurrences, localize the sources of the problems, and implement the appropriate countermeasures. This paper reports our experience with a state-of-the-art failure prediction method, PREVENT, and its extension with a troubleshooting module, REACT, applied to naval systems developed by Fincantieri. Our results show how to integra…

An Optimal Control Interpretation of Augmented Distributed Optimization Algorithms
Liam Hallinan, Ioannis Lestas
https://arxiv.org/abs/2509.17785 https://a…

An Optimal Control Interpretation of Augmented Distributed Optimization Algorithms
Distributed optimization algorithms are used in a wide variety of problems involving complex network systems where the goal is for a set of agents in the network to solve a network-wide optimization problem via distributed update rules. In many applications, such as communication networks and power systems, transient performance of the algorithms is just as critical as convergence, as the algorithms link to physical processes which must behave well. Primal-dual algorithms have a long history in…

📊 Multiple #MCP servers can be connected in single request, enabling #AI coordination across different systems for complex business workflows and automation
🌐

A Verified Compiler for Quantum Simulation
Liyi Li, Fenfen An, Federico Zahariev, Zhi Xiang Chong, Amr Sabry, Mark Gordon
https://arxiv.org/abs/2509.18583 https://

Social influence on complex networks as a perturbation to individual behavior
Dhruv Mittal, Fl\'avio L. Pinheiro, V\'itor V. Vasconcelos
https://arxiv.org/abs/2509.17948

Social influence on complex networks as a perturbation to individual behavior
Cooperation is fundamental to the functioning of biological and social systems in both human and animal populations, with the structure of interactions playing a crucial role. Previous studies have used networks to describe interactions and explore the evolution of cooperation, but with limited transposability to social settings due to biologically relevant assumptions. Exogenous processes -- that affect the individual and are not derived from social interactions -- even if unbiased, have a rol…

Dimensionality effect on exceptional fermionic superfluidity with spin-dependent asymmetric hopping
Soma Takemori, Kazuki Yamamoto, Akihisa Koga
https://arxiv.org/abs/2509.17414

Dimensionality effect on exceptional fermionic superfluidity with spin-dependent asymmetric hopping
Non-Hermitian (NH) quantum systems host exceptional points (EPs), where eigenstates and eigenvalues coalesce, leading to unconventional many-body phenomena absent in Hermitian systems. While NH fermionic systems with complex interactions exhibit superfluid (SF) breakdown with EPs, spin-dependent asymmetric hopping can stabilize a NH superfluid (NH-SF) that coexists with EPs. In this work, we investigate the quasi-one-dimensional NH attractive Fermi-Hubbard model by using NH BCS theory. We demon…

Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications
Selva Ta\c{s}, Mahmut El Huseyni, \"Ozay Ezerceli, Reyhan Bayraktar, Fatma Bet\"ul Terzio\u{g}lu
https://arxiv.org/abs/2509.17671

Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications
The widespread adoption of Large Language Models (LLMs) has been hindered by their tendency to hallucinate, generating plausible but factually incorrect information. While Retrieval-Augmented Generation (RAG) systems attempt to address this issue by grounding responses in external knowledge, hallucination remains a persistent challenge, particularly for morphologically complex, low-resource languages like Turkish. This paper introduces Turk-LettuceDetect, the first suite of hallucination detect…

Designing Culturally Aligned AI Systems For Social Good in Non-Western Contexts
Deepak Varuvel Dennison, Mohit Jain, Tanuja Ganu, Aditya Vashistha
https://arxiv.org/abs/2509.16158

Designing Culturally Aligned AI Systems For Social Good in Non-Western Contexts
AI technologies are increasingly deployed in high-stakes domains such as education, healthcare, law, and agriculture to address complex challenges in non-Western contexts. This paper examines eight real-world deployments spanning seven countries and 18 languages, combining 17 interviews with AI developers and domain experts with secondary research. Our findings identify six cross-cutting factors - Language, Domain, Demography, Institution, Task, and Safety - that structured how systems were des…

Security Vulnerabilities in Software Supply Chain for Autonomous Vehicles
Md Wasiul Haque, Md Erfan, Sagar Dasgupta, Md Rayhanur Rahman, Mizanur Rahman
https://arxiv.org/abs/2509.16899

Security Vulnerabilities in Software Supply Chain for Autonomous Vehicles
The interest in autonomous vehicles (AVs) for critical missions, including transportation, rescue, surveillance, reconnaissance, and mapping, is growing rapidly due to their significant safety and mobility benefits. AVs consist of complex software systems that leverage artificial intelligence (AI), sensor fusion algorithms, and real-time data processing. Additionally, AVs are becoming increasingly reliant on open-source software supply chains, such as open-source packages, third-party software …

Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Minfeng Qi, Tianqing Zhu, Lefeng Zhang, Ningran Li, Wanlei Zhou
https://arxiv.org/abs/2509.16736

Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tra…

Reminds me of a tactic I used in my adapted Suckerfish fly-outs a millennia ago (b&w arrows for nested items so clients could use any color).
“Taking a shot at the double focus ring problem using modern CSS”
https://piccalil.li/blog/taking-a-shot

Taking a shot at the double focus ring problem using modern CSS
A double focus ring is really useful in general but especially so with complex, themed systems. Eric Bailey shows us how to develop a really smart system, using modern CSS.

Overview of complex organic molecule observations in protostellar systems
P. Nazari
https://arxiv.org/abs/2508.15771 https://arxiv.org/pdf/2508.15771

Overview of complex organic molecule observations in protostellar systems
Complex organic molecules (COMs) have been detected abundantly at various stages of star formation, particularly in the warm protostellar phase. The progress in gas-phase measurements has been accelerated by the advent of the Atacama Large Millimeter/submillimeter Array and in ice measurements by the James Webb Space Telescope. Particularly, the community has moved from single-source studies of COMs to statistical analyses because of these powerful instruments. In this article, I review surveys…

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Chuzhan Hao, Wenfeng Feng, Yuewei Zhang, Hao Wang
https://arxiv.org/abs/2507.17365

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning
Multi-step agentic retrieval systems based on large language models (LLMs) have demonstrated remarkable performance in complex information search tasks. However, these systems still face significant challenges in practical applications, particularly in generating factually inconsistent intermediate queries and inefficient search trajectories, which can lead to reasoning deviations or redundant computations. To address these issues, we propose DynaSearcher, an innovative search agent enhanced by…

Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Akshay Govind Srinivasan, Ryan Jacob George, Jayden Koshy Joe, Hrushikesh Kant, Harshith M R, Sachin Sundar, Sudharshan Suresh, Rahul Vimalkanth, Vijayavallabh
https://arxiv.org/abs/2509.16369

Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Accurate and reliable knowledge retrieval is vital for financial question-answering, where continually updated data sources and complex, high-stakes contexts demand precision. Traditional retrieval systems rely on a single database and retriever, but financial applications require more sophisticated approaches to handle intricate regulatory filings, market analyses, and extensive multi-year reports. We introduce a framework for financial Retrieval Augmented Generation (RAG) that leverages agent…

On Temporal Guidance and Iterative Refinement in Audio Source Separation
Tobias Morocutti, Jonathan Greif, Paul Primus, Florian Schmid, Gerhard Widmer
https://arxiv.org/abs/2507.17297

On Temporal Guidance and Iterative Refinement in Audio Source Separation
Spatial semantic segmentation of sound scenes (S5) involves the accurate identification of active sound classes and the precise separation of their sources from complex acoustic mixtures. Conventional systems rely on a two-stage pipeline - audio tagging followed by label-conditioned source separation - but are often constrained by the absence of fine-grained temporal information critical for effective separation. In this work, we address this limitation by introducing a novel approach for S5 th…

Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
Yu Sha, Shuiping Gou, Bo Liu, Johannes Faber, Ningtao Liu, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou
https://arxiv.org/abs/2508.12375

Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks…

Exploration of Evolving Quantum Key Distribution Network Architecture Using Model-Based Systems Engineering
Hayato Ishida, Amal Elsokary, Maria Aslam, Catherine White, Michael J. de C. Henshaw, Siyuan Ji
https://arxiv.org/abs/2508.15733

Exploration of Evolving Quantum Key Distribution Network Architecture Using Model-Based Systems Engineering
Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quan…

BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
Parsa Vahidi, Omid G. Sani, Maryam M. Shanechi
https://arxiv.org/abs/2509.18627 https://

BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
Neural populations exhibit complex recurrent structures that drive behavior, while continuously receiving and integrating external inputs from sensory stimuli, upstream regions, and neurostimulation. However, neural populations are often modeled as autonomous dynamical systems, with little consideration given to the influence of external inputs that shape the population activity and behavioral outcomes. Here, we introduce BRAID, a deep learning framework that models nonlinear neural dynamics un…

Stochastic Dynamics of Urban Predator-Prey Systems: Integrating Human Disturbance and Functional Responses
Arhonefe Joseph Ogethakpo, Ozioma Ogoegbulem, Sarduana Joshua Apanapudor, Helen Onovwerosuoke Sanubi
https://arxiv.org/abs/2509.18217

Stochastic Dynamics of Urban Predator-Prey Systems: Integrating Human Disturbance and Functional Responses
Urban ecosystems exhibit complex predator-prey dynamics increasingly disrupted by anthropogenic disturbances (e.g., noise, habitat fragmentation). Classical Lotka-Volterra (LV) models fail to capture these human-induced stressors, and integrated frameworks incorporating functional responses, stochasticity, and spatial dynamics remain scarce. We develop a comprehensive stochastic model to quantify how human disturbance reshapes predator-prey interactions in urban landscapes, using rat-cat system…

Crosslisted article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Filtering amplitude dependence of correlation dynamics in complex systems: application to the cry...
Marcin W\k{a}torek, Marija Bezbradica, Martin Crane, Jaros{\l}aw Kwapie\'n, Stanis{\l}aw Dro\.zd\.…

New migration patterns in high planet-star mass ratio systems in disks with low viscosity
Mariana S\'anchez, Sijme-Jan Paardekooper, Nienke van der Marel, Pablo Ben\'itez-Llambay, Gijs D. Mulders
https://arxiv.org/abs/2509.15719

New migration patterns in high planet-star mass ratio systems in disks with low viscosity
Migration of giant planets remains a complex topic. While significant progress has been made for high-viscosity disks, the migration of planets with large planet-star mass ratios in low-viscosity environments is still not fully understood. We study the migration of such planets in disks with $α= 10^{-4}$ and derive analytical prescriptions applicable across stellar masses, from Sun-like stars to M dwarfs. Using hydrodynamical simulations with FARGO3D, we explored planets with mass ratios $10^{…

Quantum Chaos Diagnostics for Open Quantum Systems from Bi-Lanczos Krylov Dynamics
Matteo Baggioli, Kyoung-Bum Huh, Hyun-Sik Jeong, Xuhao Jiang, Keun-Young Kim, Juan F. Pedraza
https://arxiv.org/abs/2508.13956

Quantum Chaos Diagnostics for Open Quantum Systems from Bi-Lanczos Krylov Dynamics
In Hermitian systems, Krylov complexity has emerged as a powerful diagnostic of quantum dynamics, capable of distinguishing chaotic from integrable phases, in agreement with established probes such as spectral statistics and out-of-time-order correlators. By contrast, its role in non-Hermitian settings, relevant for modeling open quantum systems, remains less understood due to the challenges posed by complex eigenvalues and the limitations of standard approaches such as singular value decomposi…

Demonstration of an integral method for estimating wall shear stress in complex high-speed flows
Mateus A. R. Braga, Robyn L. Macdonald
https://arxiv.org/abs/2508.15024 https://…

Demonstration of an integral method for estimating wall shear stress in complex high-speed flows
Turbulent flows over blunt bodies with distributed roughness present a class of problems relevant to hypersonic atmospheric entry systems. However, accurate predictions of shear stress on such bodies remains elusive. This work presents a simple integral formulation to infer wall shear stress based on the Favre-averaged streamwise momentum equation, integrated once in the wall-normal direction. The proposed integral formulation eliminates streamwise dependence, relying only on data and gradients…

From Atoms to Dynamics: Learning the Committor Without Collective Variables
Sergio Contreras Arredondo, Chenyu Tang, Radu A. Talmazan, Alberto Meg\'ias, Cheng Giuseppe Chen, Christophe Chipot
https://arxiv.org/abs/2507.17700

From Atoms to Dynamics: Learning the Committor Without Collective Variables
This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the i…

Kuramoto Mean Field Game with Intrinsic Frequencies
Rene Carmona, Quentin Cormier, Mete Soner
https://arxiv.org/abs/2509.18000 https://arxiv.org/pdf/2509.1…

Kuramoto Mean Field Game with Intrinsic Frequencies
This paper studies a mean field game formulation of the classical Kuramoto model for synchronization. Our model captures the diversity within the population by considering random intrinsic frequencies, which allows us to study the impact of this heterogeneity on synchronization patterns and stability. Our findings contribute insights into the interplay between intrinsic frequency diversity and synchronization dynamics, offering a more realistic understanding of complex systems. The proposed fra…

In line with my longtime pet issue of cascading #SystemicFailure in complex systems which we've created and come to depend upon, yet have seemingly forgotten how to maintain.
We saw a hint of what's possible in the early years of Covid when shortages rolled across the economy in unpredictable ways. I fear what might happen as the Fash keep dismantling all forms of systemic in…

CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Ruiguo Zhong, Ruoyu Yao, Pei Liu, Xiaolong Chen, Rui Yang, Jun Ma
https://arxiv.org/abs/2509.17080

CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Accurate trajectory prediction and motion planning are crucial for autonomous driving systems to navigate safely in complex, interactive environments characterized by multimodal uncertainties. However, current generation-then-evaluation frameworks typically construct multiple plausible trajectory hypotheses but ultimately adopt a single most likely outcome, leading to overconfident decisions and a lack of fallback strategies that are vital for safety in rare but critical scenarios. Moreover, th…

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
Akira Matsui, Takashi Teramoto, Eiji Motohashi, Hiroyuki Tsurumi
https://arxiv.org/abs/2509.14508

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
The digital economy implements complex incentive systems to retain users through point redemption. Understanding user behavior in such complex incentive structures presents a fundamental challenge, especially in estimating the value of these digital assets against traditional money. This study tackles this question by analyzing large-scale, real-world transaction data from a popular personal finance application that captures both monetary spending and point-based transactions across Japan's dee…

Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech
Sang Hoon Woo, Sehun Lee, Kang-wook Kim, Gunhee Kim
https://arxiv.org/abs/2509.16028 https://…

Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech
Spoken dialogue systems increasingly employ large language models (LLMs) to leverage their advanced reasoning capabilities. However, direct application of LLMs in spoken communication often yield suboptimal results due to mismatches between optimal textual and verbal delivery. While existing approaches adapt LLMs to produce speech-friendly outputs, their impact on reasoning performance remains underexplored. In this work, we propose Think-Verbalize-Speak, a framework that decouples reasoning fr…

Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
Hiroki Sayama
https://arxiv.org/abs/2509.11997 https://ar…

Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
The complex systems keyword diagram generated by the author in 2010 has been used widely in a variety of educational and outreach purposes, but it definitely needs a major update and reorganization. This short paper reports our recent attempt to update the keyword diagram using information collected from the following multiple sources: (a) collective feedback posted on social media, (b) recent reference books on complex systems and network science, (c) online resources on complex systems, and (…

The Approach of Sliced Inference in Systems of Stochastic Differential Equations with Comments on the Heston Model
Ahmet Umur \"Ozsoy
https://arxiv.org/abs/2508.15725 https…

The Approach of Sliced Inference in Systems of Stochastic Differential Equations with Comments on the Heston Model
Stochastic differential equations have been an important tool in modeling complex financial relations, equipped with the possibility of being multidimensional to better oversee complexities inherent in finance. This multidimensionality, however, comes with a larger parameter space to estimate. Therefore, via a dimension reduction method, Sliced Inverse Regression, we aim to reduce high-dimensional parameter space to a reduced feature space and aim to estimate the parameters on this new featured…

Dirac monopole magnets in non-Hermitian systems
Haiyang Yu, Tao Jiang, Li-Chen Zhao
https://arxiv.org/abs/2508.15186 https://arxiv.org/pdf/2508.15186

Dirac monopole magnets in non-Hermitian systems
We theoretically establish that non-Hermitian perturbations induce a topological transformation of point-like Dirac monopoles into extended monopole distributions, characterized by distinct charge configurations emergent from three distinct Berry connection forms. Using piecewise adiabatic evolution, we confirm the validity of these configurations through observations of complex geometric phases. Most critically, we find a quantitative relation $Δϕ_d = Δϕ_g$, which quantifies how cumulative…

Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
Vinura Galwaduge, Jagath Samarabandu
https://arxiv.org/abs/2507.17161

Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
Modern network intrusion detection systems (NIDS) frequently utilize the predictive power of complex deep learning models. However, the "black-box" nature of such deep learning methods adds a layer of opaqueness that hinders the proper understanding of detection decisions, trust in the decisions and prevent timely countermeasures against such attacks. Explainable AI (XAI) methods provide a solution to this problem by providing insights into the causes of the predictions. The majority of the exi…

A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem
Nikhil N S (Indian Institute of Science, Bengaluru, India), Amol Dilip Joshi (Indian Institute of Science, Bengaluru, India, TCS Research, Tata Consultancy Services Ltd), Bilal Muhammed (TCS Research, Tata Consultancy Services Ltd), Soban Babu (TCS Research, Tata Consultancy Services Ltd)

A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem
Selecting a solution algorithm for the Facility Layout Problem (FLP), an NP-hard optimization problem with a multiobjective trade-off, is a complex task that requires deep expert knowledge. The performance of a given algorithm depends on specific problem characteristics such as its scale, objectives, and constraints. This creates a need for a data-driven recommendation method to guide algorithm selection in automated design systems. This paper introduces a new recommendation method to make such…

Reliable Smoke Detection via Optical Flow-Guided Feature Fusion and Transformer-Based Uncertainty Modeling
Nitish Kumar Mahala, Muzammil Khan, Pushpendra Kumar
https://arxiv.org/abs/2508.14597

Reliable Smoke Detection via Optical Flow-Guided Feature Fusion and Transformer-Based Uncertainty Modeling
Fire outbreaks pose critical threats to human life and infrastructure, necessitating high-fidelity early-warning systems that detect combustion precursors such as smoke. However, smoke plumes exhibit complex spatiotemporal dynamics influenced by illumination variability, flow kinematics, and environmental noise, undermining the reliability of traditional detectors. To address these challenges without the logistical complexity of multi-sensor arrays, we propose an information-fusion framework by…

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Milena Davudova, Ziyuan Cai, Valentina Giunchiglia, Dragos C. Gruia, Giulia Sanguedolce, Adam Hampshire, Fatemeh Geranmayeh
https://arxiv.org/abs/2507.17326

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Detailed assessment of language impairment following stroke remains a cognitively complex and clinician-intensive task, limiting timely and scalable diagnosis. Automatic Speech Recognition (ASR) foundation models offer a promising pathway to augment human evaluation through intelligent systems, but their effectiveness in the context of speech and language impairment remains uncertain. In this study, we evaluate whether Whisper, a state-of-the-art ASR foundation model, can be applied to transcri…

Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems
Minh Chung, Yaknan Gambo, Burak Mete, Xiao-Ting Michelle To, Florian Kr\"otz, Korbinian Staudacher, Martin Letras, Xiaolong Deng, Mounika Vavilala, Amir Raoofy, Jorge Echavarria, Luigi Iapichino, Laura Schulz, Josef Weidendorfer, Martin Schulz
https://

Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems
Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed t…

Relational Dissonance in Human-AI Interactions: The Case of Knowledge Work
Emrecan Gulay, Eleonora Picco, Enrico Glerean, Corinna Coupette
https://arxiv.org/abs/2509.15836 https…

Relational Dissonance in Human-AI Interactions: The Case of Knowledge Work
When AI systems allow human-like communication, they elicit increasingly complex relational responses. Knowledge workers face a particular challenge: They approach these systems as tools while interacting with them in ways that resemble human social interaction. To understand the relational contexts that arise when humans engage with anthropomorphic conversational agents, we need to expand existing human-computer interaction frameworks. Through three workshops with qualitative researchers, we f…

Emergence of power laws in hierarchical dynamics on multi-level graphs
Tommaso Rondini, Gregorio Berselli, Mirko Degli Esposti, Armando Bazzani
https://arxiv.org/abs/2509.18782 …

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Ronit Virwani, Ruchika Suryawanshi
https://arxiv.org/abs/2508.13371 https://

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses. Current state-of-the-art neural planning approaches struggle with complex domains, producing plans with missing preconditions, inconsistent goals, and hallucinations. While classical planners provide logical guarantees, they lack the flexibility and natural language understanding capabilities needed for modern autonomous systems. Existing neuro-symbolic…

Statistical stability for systems semi-conjugate to pre-piecewise convex or expanding maps with countably many branches
Rafael Lucena
https://arxiv.org/abs/2508.11878 https://…

Statistical stability for systems semi-conjugate to pre-piecewise convex or expanding maps with countably many branches
We investigate the statistical stability of a class of dynamical systems that are semi-conjugate to pre-piecewise convex or expanding maps with countably many branches. These systems may exhibit complex features such as unbounded derivatives, discontinuities, or infinite Markov partitions, which pose significant challenges for stability analysis. Considering one-parameter families of transformations $\{F_δ\}_{δ\in [0,1)}$ with corresponding invariant measures $\{μ_δ\}_{δ\in [0,1)}$, we pro…

SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Xiao Zhou, Zengqi Peng, Jun Ma
https://arxiv.org/abs/2509.17850

SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Accurate trajectory prediction of surrounding vehicles (SVs) is crucial for autonomous driving systems to avoid misguided decisions and potential accidents. However, achieving reliable predictions in highly dynamic and complex traffic scenarios remains a significant challenge. One of the key impediments lies in the limited effectiveness of current approaches to capture the multi-modal behaviors of drivers, which leads to predicted trajectories that deviate from actual future motions. To address…

Kinematics of the interstellar medium using Gaia: A catalogue of 102 YSO-MC associations within 3.5 kpc from the Sun with 3D velocities
Ji-Xuan Zhou, Guang-Xing Li, Bing-Qiu Chen
https://arxiv.org/abs/2509.18496

Kinematics of the interstellar medium using Gaia: A catalogue of 102 YSO-MC associations within 3.5 kpc from the Sun with 3D velocities
Kinematic information is crucial for understanding the evolution of complex systems, such as interstellar gas. Obtaining full 3D kinematic information is a crucial final step for modeling and interpretation. Molecular clouds are nurseries where stars are born. Stars at a very early stage, like young stellar objects (YSOs), inherit the spatial and kinematic structure of the gas patches they originate from. In this paper, we combine measurements of radial velocities towards the gas and the kinema…

Robust tracking MPC for perturbed nonlinear systems -- Extended version
Marco Polver, Daniel Limon, Fabio Previdi, Antonio Ferramosca
https://arxiv.org/abs/2508.14248 https://…

Robust tracking MPC for perturbed nonlinear systems -- Extended version
This paper presents a novel robust predictive controller for constrained nonlinear systems that is able to track piece-wise constant setpoint signals. The tracking model predictive controller presented in this paper extends the nonlinear MPC for tracking to the more complex case of nonlinear systems subject to bounded and not necessarily additive perturbations. The optimal control problem that is solved at each step penalizes the deviation of the predicted nominal system trajectory from an arti…

Competing Supramolecular Structures: Dielectric and Rheological Spectroscopy on Glycerol/Propanol Mixtures
Jan Philipp Gabriel
https://arxiv.org/abs/2509.15341 https://

Competing Supramolecular Structures: Dielectric and Rheological Spectroscopy on Glycerol/Propanol Mixtures
Significant progress has been made in recent years in understanding the dynamics of pure hydrogen-bonded systems by analyzing the spectral shape of various susceptibilities. Monohydroxy- and polyalcohols are currently considered to form transient supramolecular hydrogen-bonded structures in the form of chains, rings, and networks. This complex dynamic behavior has been identified in network-forming glycerol and chain-forming propanol by combining dielectric and light-scattering spectra. We appl…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Spatio-temporal Dynamical Indices for Complex Systems
Dong, Messori, Faranda, Gualandi, Lucarini, Mengaldo

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Phuong Mai Dinh, Van-Nam Huynh
https://arxiv.org/abs/2509.17889

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Multi-objective optimization (MOO) is essential for solving complex real-world problems involving multiple conflicting objectives. However, many practical applications - including engineering design, autonomous systems, and machine learning - often yield non-convex, degenerate, or discontinuous Pareto frontiers, which involve traditional scalarization and Pareto Set Learning (PSL) methods that struggle to approximate accurately. Existing PSL approaches perform well on convex fronts but tend to …

Measuring irreversibility by counting: a random coarse-graining framework
Ruicheng Bao, Naruo Ohga, Sosuke Ito
https://arxiv.org/abs/2508.11586 https://arx…

Measuring irreversibility by counting: a random coarse-graining framework
Thermodynamic irreversibility is a fundamental concept in statistical physics, yet its experimental measurement remains challenging, especially for complex systems. We introduce a novel random coarse-graining framework to identify model-free measures of irreversibility in complex many-body systems. These measures are constructed from the asymmetry of cross-correlation functions between suitably chosen observables, providing rigorous lower bounds on entropy production. For many-particle systems,…

Quantum Monte Carlo calculations in the nuclear shell model by the complex Langevin method
Yuhma Asano, Yuta Ito, Jun Nishimura, Noritaka Shimizu
https://arxiv.org/abs/2508.15116

Quantum Monte Carlo calculations in the nuclear shell model by the complex Langevin method
The nuclear shell model is known to describe the properties of various nuclei extremely well. However, the auxiliary-field quantum Monte Carlo calculations cannot be applied to it with general interactions due to the sign problem. The model has therefore been investigated primarily by variational methods, where the accuracy of the results depends crucially on the ansatz for the wave function. Here we perform the auxiliary-field quantum Monte Carlo calculations in the case of small systems at fi…

Pseudogap in a Fermi-Hubbard quantum simulator
Lev Haldar Kendrick, Anant Kale, Youqi Gang, Alexander Dennisovich Deters, Martin Lebrat, Aaron W. Young, Markus Greiner
https://arxiv.org/abs/2509.18075 …

Pseudogap in a Fermi-Hubbard quantum simulator
Understanding doped Mott insulators is a fundamental goal in condensed matter physics, with relevance to cuprate superconductors and other quantum materials. The doped Hubbard model minimally describes such systems, and has explicated some of their complex behavior. However, many open questions remain concerning the anomalous metallic states which emerge at low temperatures and intermediate doping and which, in cuprates, give rise to high-temperature superconductivity upon cooling. Here we obse…

Unveiling the Miniband Structure of Graphene Moir\'e Superlattices via Gate-dependent Terahertz Photocurrent Spectroscopy
Juan A. Delgado-Notario, Stephen R. Power, Wojciech Knap, Manuel Pino, JinLuo Cheng, Daniel Vaquero, Takashi Taniguchi, Kenji Watanabe, Jes\'us E. Vel\'azquez-P\'erez, Yahya M. Meziani, Pablo Alonso-Gonz\'alez, Jos\'e M. Caridad

Unveiling the Miniband Structure of Graphene Moiré Superlattices via Gate-dependent Terahertz Photocurrent Spectroscopy
Moiré superlattices formed at the interface between stacked two-dimensional atomic crystals offer limitless opportunities to design materials with widely tunable properties and engineer intriguing quantum phases of matter. However, despite progress, precise probing of the electronic states and tantalizingly complex band textures of these systems remain challenging. Here, we present gate-dependent terahertz photocurrent spectroscopy as a robust technique to detect, explore and quantify intricat…

Simulating the interplay of dipolar and quadrupolar interactions in NMR by spin dynamic mean-field theory
Timo Gr\"a{\ss}er, G\"otz S. Uhrig
https://arxiv.org/abs/2507.17720

Simulating the interplay of dipolar and quadrupolar interactions in NMR by spin dynamic mean-field theory
The simulation of nuclear magnetic resonance (NMR) experiments is a notoriously difficult task, if many spins participate in the dynamics. The recently established dynamic mean-field theory for high-temperature spin systems (spinDMFT) represents an efficient yet accurate method to deal with this scenario. SpinDMFT reduces a complex lattice system to a time-dependent single-site problem, which can be solved numerically with small computational effort. Since the approach retains local quantum deg…

LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems
Ron Solomon, Yarin Yerushalmi Levi, Lior Vaknin, Eran Aizikovich, Amit Baras, Etai Ohana, Amit Giloni, Shamik Bose, Chiara Picardi, Yuval Elovici, Asaf Shabtai
https://arxiv.org/abs/2508.12412

LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems
The incorporation of large language models in multi-agent systems (MASs) has the potential to significantly improve our ability to autonomously solve complex problems. However, such systems introduce unique challenges in monitoring, interpreting, and detecting system failures. Most existing MAS observability frameworks focus on analyzing each individual agent separately, overlooking failures associated with the entire MAS. To bridge this gap, we propose LumiMAS, a novel MAS observability framew…

Towards the Coordination and Verification of Heterogeneous Systems with Data and Time
Tim Kr\"auter, Adrian Rutle, Yngve Lamo, Harald K\"onig, Francisco Dur\'an
https://arxiv.org/abs/2508.12325

Towards the Coordination and Verification of Heterogeneous Systems with Data and Time
Modern software systems are often realized by coordinating multiple heterogeneous parts, each responsible for specific tasks. These parts must work together seamlessly to satisfy the overall system requirements. To verify such complex systems, we have developed a non-intrusive coordination framework capable of performing formal analysis of heterogeneous parts that exchange data and include real-time capabilities. The framework utilizes a linguistic extension, which is implemented as a central b…

MedReseacher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
Ailing Yu, Lan Yao, Jingnan Liu, Zhe Chen, Jiajun Yin, Yuan Wang, Xinhao Liao, Zhiling Ye, Ji Li, Yun Yue, Hansong Xiao, Hualei Zhou, Chunxiao Guo, Peng Wei, Jinjie Gu
https://arxiv.org/abs/2508.14880…

MedReseacher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
Recent developments in Large Language Model (LLM)-based agents have shown impressive capabilities spanning multiple domains, exemplified by deep research systems that demonstrate superior performance on complex information-seeking and synthesis tasks. While general-purpose deep research agents have shown impressive capabilities, they struggle significantly with medical domain challenges, as evidenced by leading proprietary systems achieving limited accuracy on complex medical benchmarks. The ke…

Data-Driven Probabilistic Evaluation of Logic Properties with PAC-Confidence on Mealy Machines
Swantje Plambeck, Ali Salamati, Eyke Huellermeier, Goerschwin Fey
https://arxiv.org/abs/2508.14710

Data-Driven Probabilistic Evaluation of Logic Properties with PAC-Confidence on Mealy Machines
Cyber-Physical Systems (CPS) are complex systems that require powerful models for tasks like verification, diagnosis, or debugging. Often, suitable models are not available and manual extraction is difficult. Data-driven approaches then provide a solution to, e.g., diagnosis tasks and verification problems based on data collected from the system. In this paper, we consider CPS with a discrete abstraction in the form of a Mealy machine. We propose a data-driven approach to determine the safety p…

EmoQ: Speech Emotion Recognition via Speech-Aware Q-Former and Large Language Model
Yiqing Yang, Man-Wai Mak
https://arxiv.org/abs/2509.15775 https://arxiv…

EmoQ: Speech Emotion Recognition via Speech-Aware Q-Former and Large Language Model
The performance of speech emotion recognition (SER) is limited by the insufficient emotion information in unimodal systems and the feature alignment difficulties in multimodal systems. Recently, multimodal large language models (MLLMs) have made progress in SER. However, MLLMs still suffer from hallucination and misclassification problems in complex emotion reasoning. To address these problems, we propose an MLLM-based framework called EmoQ, which generates query embeddings that fuse multimodal…

SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Pengzhi Zhong, Xinzhe Wang, Dan Zeng, Qihua Zhou, Feixiang He, Shuiwang Li
https://arxiv.org/abs/2508.14607

SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Brain-inspired Spiking Neural Networks (SNNs) exhibit significant potential for low-power computation, yet their application in visual tasks remains largely confined to image classification, object detection, and event-based tracking. In contrast, real-world vision systems still widely use conventional RGB video streams, where the potential of directly-trained SNNs for complex temporal tasks such as multi-object tracking (MOT) remains underexplored. To address this challenge, we propose SMTrack…

Complex-valued Phase Synchrony Reveals Directional Coupling in FMRI and Tracks Medication Effects
Sir-Lord Wiafe, Najme Soleimani, Masoud Seraji, Bradley Baker, Robyn Miller, Ashkan Faghiri, Vince D. Calhoun
https://arxiv.org/abs/2509.13481

Complex-valued Phase Synchrony Reveals Directional Coupling in FMRI and Tracks Medication Effects
Understanding interactions in complex systems requires capturing the directionality of coupling, not only its strength. Phase synchronization captures this timing, yet most methods either reduce phase to its cosine or collapse it into scaler indices such as phase-locking value, discarding directionality. We propose a complex-valued phase synchrony (CVPS) framework that estimates phase with an adaptive Gabor wavelet and preserves both cosine and sine components. Simulations confirm that CVPS rec…

Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems
Chiranjit Mitra
https://arxiv.org/abs/2508.12314 https://arxiv.org/pdf/2508…

Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems
We present a novel interdisciplinary framework that bridges synchronization theory and multi-agent AI systems by adapting the Kuramoto model to describe the collective dynamics of heterogeneous AI agents engaged in complex task execution. By representing AI agents as coupled oscillators with both phase and amplitude dynamics, our model captures essential aspects of agent specialization, influence, and communication within networked systems. We introduce an order parameter to quantify the degree…

OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service
Zhipeng Wei, Kuo Cai, Junda She, Jie Chen, Minghao Chen, Yang Zeng, Qiang Luo, Wencong Zeng, Ruiming Tang, Kun Gai, Guorui Zhou
https://arxiv.org/abs/2508.14646

OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service
Local life service is a vital scenario in Kuaishou App, where video recommendation is intrinsically linked with store's location information. Thus, recommendation in our scenario is challenging because we should take into account user's interest and real-time location at the same time. In the face of such complex scenarios, end-to-end generative recommendation has emerged as a new paradigm, such as OneRec in the short video scenario, OneSug in the search scenario, and EGA in the advertising sce…

Transduction is All You Need for Structured Data Workflows
Alfio Gliozzo, Naweed Khan, Christodoulos Constantinides, Nandana Mihindukulasooriya, Nahuel Defosse, Junkyu Lee
https://arxiv.org/abs/2508.15610

Transduction is All You Need for Structured Data Workflows
This paper introduces Agentics, a modular framework for building agent-based systems capable of structured reasoning and compositional generalization over complex data. Designed with research and practical applications in mind, Agentics offers a novel perspective on working with data and AI workflows. In this framework, agents are abstracted from the logical flow and they are used internally to the data type to enable logical transduction among data. Agentics encourages AI developers to focus o…

Tight Inter-Core Cache Contention Analysis for WCET Estimation on Multicore Systems
Shuai Zhao, Jieyu Jiang, Shenlin Cai, Yaowei Liang, Chen Jie, Yinjie Fang, Wei Zhang, Guoquan Zhang, Yaoyao Gu, Xiang Xiao, Wei Qin, Xiangzhen Ouyang, Wanli Chang
https://arxiv.org/abs/2508.13863

Tight Inter-Core Cache Contention Analysis for WCET Estimation on Multicore Systems
WCET (Worst-Case Execution Time) estimation on multicore architecture is particularly challenging mainly due to the complex accesses over cache shared by multiple cores. Existing analysis identifies possible contentions between parallel tasks by leveraging the partial order of the tasks or their program regions. Unfortunately, they overestimate the number of cache misses caused by a remote block access without considering the actual cache state and the number of accesses. This paper reports a n…

A Vision-Based Shared-Control Teleoperation Scheme for Controlling the Robotic Arm of a Four-Legged Robot
Murilo Vinicius da Silva, Matheus Hipolito Carvalho, Juliano Negri, Thiago Segreto, Gustavo J. G. Lahr, Ricardo V. Godoy, Marcelo Becker
https://arxiv.org/abs/2508.14994

A Vision-Based Shared-Control Teleoperation Scheme for Controlling the Robotic Arm of a Four-Legged Robot
In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and versatility for complex operations. However, teleoperating quadruped robots is challenging due to the lack of integrated obstacle detection and intuitive control methods for the robotic arm, increasing collision risks in confined or dynamically changing workspaces. Teleoperation via joysticks or pads can be …

Multi-QIDA method for VQE state preparation in molecular systems
Fabio Tarocco, Davide Materia, Leonardo Ratini, Leonardo Guidoni
https://arxiv.org/abs/2508.11270 https://

Multi-QIDA method for VQE state preparation in molecular systems
The development of quantum algorithms and their application to quantum chemistry has introduced new opportunities for solving complex molecular problems that are computationally infeasible for classical methods. In quantum chemistry, the Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to estimate ground-state energies of molecular systems. Despite its promise, VQE faces challenges such as scalability issues, high circuit depths, and barren plateaus that ma…

Ontology Creation and Management Tools: the Case of Anatomical Connectivity
Natallia Kokash, Bernard de Bono, Tom Gillespie
https://arxiv.org/abs/2509.15780 https://

Ontology Creation and Management Tools: the Case of Anatomical Connectivity
We are developing infrastructure to support researchers in mapping data related to the peripheral nervous system and other physiological systems, with an emphasis on their relevance to the organs under investigation. The nervous system, a complex network of nerves and ganglia, plays a critical role in coordinating and transmitting signals throughout the body. To aid in this, we have created ApiNATOMY, a framework for the topological and semantic representation of multiscale physiological circui…

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing
Youssef Maklad, Fares Wael, Ali Hamdi, Wael Elsersy, Khaled Shaban
https://arxiv.org/abs/2508.14300

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing
Traditional protocol fuzzing techniques, such as those employed by AFL-based systems, often lack effectiveness due to a limited semantic understanding of complex protocol grammars and rigid seed mutation strategies. Recent works, such as ChatAFL, have integrated Large Language Models (LLMs) to guide protocol fuzzing and address these limitations, pushing protocol fuzzers to wider exploration of the protocol state space. But ChatAFL still faces issues like unreliable output, LLM hallucinations, …

Overview of Complex System Design
John W. Sheppard
https://arxiv.org/abs/2508.11026 https://arxiv.org/pdf/2508.11026

Overview of Complex System Design
This chapter serves as an introduction to systems engineering focused on the broad issues surrounding realizing complex integrated systems. What is a system? We pose a number of possible definitions and perspectives, but leave open the opportunity to consider the system from the target context where it will be used. Once we have a system in mind, we acknowledge the fact that this system needs to integrate a variety of pieces, components, subsystems, in order for it to accomplish its task. There…

Diffusion-driven pattern formation in an opinion dynamical network model
Tim Mauch, Thilo Gross
https://arxiv.org/abs/2508.15377 https://arxiv.org/pdf/2508…

Diffusion-driven pattern formation in an opinion dynamical network model
The spatial organization of individuals and their interactions in communities are important factors known to preserve diversity in many complex systems. Inspired by metapopulation models from ecology, we study opinion formation using a network-based approach in which nodes represent communities of interacting agents holding one of two competing opinions, and links represent avenues of migration. Agents adapt to the dominant opinion within a community or migrate toward communities with similar v…

TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
Yu Xia, Rui Zhong, Zeyu Song, Wei Yang, Junchen Wan, Qingpeng Cai, Chi Lu, Peng Jiang
https://arxiv.org/abs/2508.15388

TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
The extensive world knowledge and powerful reasoning capabilities of large language models (LLMs) have attracted significant attention in recommendation systems (RS). Specifically, The chain of thought (CoT) has been shown to improve the performance of LLMs on complex reasoning tasks for RS. However, due to the fact that LLMs often suffer from hallucination issues, there is no guarantee that their reasoning CoT is effective. A key challenge is to further enhance the recommendation capabilities …

In-Context Decision Making for Optimizing Complex AutoML Pipelines
Amir Rezaei Balef, Katharina Eggensperger
https://arxiv.org/abs/2508.13657 https://arxiv…

In-Context Decision Making for Optimizing Complex AutoML Pipelines
Combined Algorithm Selection and Hyperparameter Optimization (CASH) has been fundamental to traditional AutoML systems. However, with the advancements of pre-trained models, modern ML workflows go beyond hyperparameter optimization and often require fine-tuning, ensembling, and other adaptation techniques. While the core challenge of identifying the best-performing model for a downstream task remains, the increasing heterogeneity of ML pipelines demands novel AutoML approaches. This work extend…

Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis
Yu Ge (Nanjing University), Linna Xie (Nanjing University), Zhong Li (Nanjing University), Yu Pei (The Hong Kong Polytechnic University), Tian Zhang (Nanjing University)
https://arxiv.org/abs/2509.13782

Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis
Large Language Model Powered Multi-Agent Systems (MASs) are increasingly employed to automate complex real-world problems, such as programming and scientific discovery. Despite their promising, MASs are not without their flaws. However, failure attribution in MASs - pinpointing the specific agent actions responsible for failures - remains underexplored and labor-intensive, posing significant challenges for debugging and system improvement. To bridge this gap, we propose FAMAS, the first spectru…

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Tianyue Ou, Saujas Vaduguru, Daniel Fried
https://arxiv.org/abs/2508.12981 https://a…

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-constraint planning tasks involve conditioning on detailed information and satisfying complex interdependent constraints, which can pose a challenge for these systems. In this study, we construct an LLM-based MAS for a travel planning task which is representative of these challenges. We evaluate the impact of a notebook…

Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints
Takuya Kiyokawa, Tomoki Ishikura, Shingo Hamada, Genichiro Matsuda, Kensuke Harada
https://arxiv.org/abs/2509.14564

Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints
This study presents a system integration approach for planning schedules, sequences, tasks, and motions for reconfigurable robots to automatically disassemble constrained structures in a non-destructive manner. Such systems must adapt their configuration and coordination to the target structure, but the large and complex search space makes them prone to local optima. To address this, we integrate multiple robot arms equipped with different types of tools, together with a rotary stage, into a re…

Exact Classicalization of N-Level Quantum Systems Interacting with a Bath: Theory and Applications
Daniel Mart\'inez-Gil, Pedro Bargue\~no, Salvador Miret-Art\'es
https://arxiv.org/abs/2509.10131

Exact Classicalization of N-Level Quantum Systems Interacting with a Bath: Theory and Applications
In this manuscript, starting from a five-step algorithmic procedure for exactly classicalizing the dynamics of N-level quantum systems, we incorporate a classical bath of harmonic oscillators to model environmental interactions. Using the geometry of complex projective spaces CP(N-1) and a Langevin formalism, we obtain N-1 Hamilton's equations which encode both the quantum system and the bath degrees of freedom, representing a generalization of the Caldeira-Legget model in a complex projective …

INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
Kaiyuan Li, Dongdong Mao, Yongxiang Tang, Yanhua Cheng, Yanxiang Zeng, Chao Wang, Xialong Liu, Peng Jiang
https://arxiv.org/abs/2508.11565

INFNet: A Task-aware Information Flow Network for Large-Scale Recommendation Systems
Feature interaction has long been a cornerstone of ranking models in large-scale recommender systems due to its proven effectiveness in capturing complex dependencies among features. However, existing feature interaction strategies face two critical challenges in industrial applications: (1) The vast number of categorical and sequential features makes exhaustive interaction computationally prohibitive, often resulting in optimization difficulties. (2) Real-world recommender systems typically in…

Preguss: It Analyzes, It Specifies, It Verifies
Zhongyi Wang, Tengjie Lin, Mingshuai Chen, Mingqi Yang, Haokun Li, Xiao Yi, Shengchao Qin, Jianwei Yin
https://arxiv.org/abs/2508.14532

Preguss: It Analyzes, It Specifies, It Verifies
Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to context-length limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This pa…

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Bicheng Wang, Jinping Wang, Yibo Sue
https://arxiv.org/abs/2510.09082

Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
Learning complex network dynamics is fundamental to understanding, modelling and controlling real-world complex systems. There are two main problems in the task of predicting the dynamic evolution of complex networks: on the one hand, existing methods usually use simple graphs to describe the relationships in complex networks; however, this approach can only capture pairwise relationships, while there may be rich non-pairwise structured relationships in the network. First-order GNNs have diffic…

Learning non-Markovian Dynamical Systems with Signature-based Encoders
Eliott Pradeleix, R\'emy Hosseinkhan-Boucher, Alena Shilova, Onofrio Semeraro, Lionel Mathelin
https://arxiv.org/abs/2509.12022

Learning non-Markovian Dynamical Systems with Signature-based Encoders
Neural ordinary differential equations offer an effective framework for modeling dynamical systems by learning a continuous-time vector field. However, they rely on the Markovian assumption - that future states depend only on the current state - which is often untrue in real-world scenarios where the dynamics may depend on the history of past states. This limitation becomes especially evident in settings involving the continuous control of complex systems with delays and memory effects. To capt…

Towards Reliable Multi-Agent Systems for Marketing Applications via Reflection, Memory, and Planning
Lorenzo Jaime Yu Flores, Junyi Shen, Xiaoyuan Gu
https://arxiv.org/abs/2508.11120

Towards Reliable Multi-Agent Systems for Marketing Applications via Reflection, Memory, and Planning
Recent advances in large language models (LLMs) enabled the development of AI agents that can plan and interact with tools to complete complex tasks. However, literature on their reliability in real-world applications remains limited. In this paper, we introduce a multi-agent framework for a marketing task: audience curation. To solve this, we introduce a framework called RAMP that iteratively plans, calls tools, verifies the output, and generates suggestions to improve the quality of the audie…

Post-hoc LLM-Supported Debugging of Distributed Processes
Dennis Schiese, Andreas Both
https://arxiv.org/abs/2508.14540 https://arxiv.org/pdf/2508.14540

Post-hoc LLM-Supported Debugging of Distributed Processes
In this paper, we address the problem of manual debugging, which nowadays remains resource-intensive and in some parts archaic. This problem is especially evident in increasingly complex and distributed software systems. Therefore, our objective of this work is to introduce an approach that can possibly be applied to any system, at both the macro- and micro-level, to ease this debugging process. This approach utilizes a system's process data, in conjunction with generative AI, to generate natur…

Human Interaction for Collaborative Semantic SLAM using Extended Reality
Laura Ribeiro, Muhammad Shaheer, Miguel Fernandez-Cortizas, Ali Tourani, Holger Voos, Jose Luis Sanchez-Lopez
https://arxiv.org/abs/2509.14949

Human Interaction for Collaborative Semantic SLAM using Extended Reality
Semantic SLAM (Simultaneous Localization and Mapping) systems enrich robot maps with structural and semantic information, enabling robots to operate more effectively in complex environments. However, these systems struggle in real-world scenarios with occlusions, incomplete data, or ambiguous geometries, as they cannot fully leverage the higher-level spatial and semantic knowledge humans naturally apply. We introduce HICS-SLAM, a Human-in-the-Loop semantic SLAM framework that uses a shared exte…

MODE: Learning compositional representations of complex systems with Mixtures Of Dynamical Experts
Nathan Quiblier, Roy Friedman, Matthew Ricci
https://arxiv.org/abs/2510.09594 …

MODE: Learning compositional representations of complex systems with Mixtures Of Dynamical Experts
Dynamical systems in the life sciences are often composed of complex mixtures of overlapping behavioral regimes. Cellular subpopulations may shift from cycling to equilibrium dynamics or branch towards different developmental fates. The transitions between these regimes can appear noisy and irregular, posing a serious challenge to traditional, flow-based modeling techniques which assume locally smooth dynamics. To address this challenge, we propose MODE (Mixture Of Dynamical Experts), a graphic…

Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
Ruofan Lu, Yichen Li, Yintong Huo
https://arxiv.org/abs/2508.13143 https://

Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rates without systematically analyzing the interactions, communication mechanisms, and failure causes within these systems. To bridge this gap, we present a benchmark of 34 representative programmable tasks designed to rigorously assess autonomous agents. Using this benchmark, we evaluate three popular open-so…

RewardRank: Optimizing True Learning-to-Rank Utility
Gaurav Bhatt, Kiran Koshy Thekumparampil, Tanmay Gangwani, Tesi Xiao, Leonid Sigal
https://arxiv.org/abs/2508.14180 https://…

RewardRank: Optimizing True Learning-to-Rank Utility
Traditional ranking systems rely on proxy loss functions that assume simplistic user behavior, such as users preferring a rank list where items are sorted by hand-crafted relevance. However, real-world user interactions are influenced by complex behavioral biases, including position bias, brand affinity, decoy effects, and similarity aversion, which these objectives fail to capture. As a result, models trained on such losses often misalign with actual user utility, such as the probability of an…

Towards Open-Ended Emotional Support Conversations in LLMs via Reinforcement Learning with Future-Oriented Rewards
Ting Yang, Li Chen, Huimin Wang
https://arxiv.org/abs/2508.12935

Towards Open-Ended Emotional Support Conversations in LLMs via Reinforcement Learning with Future-Oriented Rewards
Emotional Support Conversation (ESC) systems aim to alleviate users' emotional difficulties and provide long-term, systematic support for emotional well-being. However, most large language model (LLM)-based ESC systems rely on predefined strategies, which limits their effectiveness in complex, real-life scenarios. To enable flexible responses to diverse emotional problem scenarios, this paper introduces a novel end-to-end framework (RLFF-ESC) that directly learns enduring emotionally supportive…

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

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

A layered architecture for log analysis in complex IT systems
Thorsten Wittkopp
https://arxiv.org/abs/2509.08698 https://arxiv.org/pdf/2509.08698

A layered architecture for log analysis in complex IT systems
In the evolving IT landscape, stability and reliability of systems are essential, yet their growing complexity challenges DevOps teams in implementation and maintenance. Log analysis, a core element of AIOps, provides critical insights into complex behaviors and failures. This dissertation introduces a three-layered architecture to support DevOps in failure resolution. The first layer, Log Investigation, performs autonomous log labeling and anomaly classification. We propose a method that label…

Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Alva West, Yixuan Weng, Minjun Zhu, Zhen Lin, Yue Zhang
https://arxiv.org/abs/2509.10401

Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Failure attribution in multi-agent systems -- pinpointing the exact step where a decisive error occurs -- is a critical yet unsolved challenge. Current methods treat this as a pattern recognition task over long conversation logs, leading to critically low step-level accuracy (below 17\%), which renders them impractical for debugging complex systems. Their core weakness is a fundamental inability to perform robust counterfactual reasoning: to determine if correcting a single action would have ac…