Tootfinder

A causality-based divide-and-conquer algorithm for nonequilibrium Green's function calculations with quantics tensor trains
Ken Inayoshi, Maksymilian \'Sroda, Anna Kauch, Philipp Werner, Hiroshi Shinaoka
https://arxiv.org/abs/2509.15028

A causality-based divide-and-conquer algorithm for nonequilibrium Green's function calculations with quantics tensor trains
We propose a causality-based divide-and-conquer algorithm for nonequilibrium Green's function calculations with quantics tensor trains. This algorithm enables stable and efficient extensions of the simulated time domain by exploiting the causality of Green's functions. We apply this approach within the framework of nonequilibrium dynamical mean-field theory to the simulation of quench dynamics in symmetry-broken phases, where long-time simulations are often required to capture slow relaxation d…

Applied causality to infer protein dynamics and kinetics
Akashnathan Aranganathan, Eric R. Beyerle
https://arxiv.org/abs/2508.12060 https://arxiv.org/pdf/2…

Applied causality to infer protein dynamics and kinetics
The use of generative machine learning models, trained on the experimentally resolved structures deposited in the protein data bank, is an attractive approach to sampling conformational ensembles of proteins. Unfortunately, since the machine-learned model utilized to generate these ensembles is not tied to an equation of motion, such as a molecular dynamics integrator or other causal generator of the dynamics, there is no timescale or causal information encoded in them. As such, with this work,…

Causality Matters: How Temporal Information Emerges in Video Language Models
Yumeng Shi, Quanyu Long, Yin Wu, Wenya Wang
https://arxiv.org/abs/2508.11576 https://

Causality Matters: How Temporal Information Emerges in Video Language Models
Video language models (VideoLMs) have made significant progress in multimodal understanding. However, temporal understanding, which involves identifying event order, duration, and relationships across time, still remains a core challenge. Prior works emphasize positional encodings (PEs) as a key mechanism for encoding temporal structure. Surprisingly, we find that removing or modifying PEs in video inputs yields minimal degradation in the performance of temporal understanding. In contrast, reve…

The power of dynamic causality in observer-based design for soft sensor applications
William Farlessyost, Sebastian Oberst, Shweta Singh
https://arxiv.org/abs/2509.11336 https:/…

The power of dynamic causality in observer-based design for soft sensor applications
This paper introduces a novel framework for optimizing observer-based soft sensors through dynamic causality analysis. Traditional approaches to sensor selection often rely on linearized observability indices or statistical correlations that fail to capture the temporal evolution of complex systems. We address this gap by leveraging liquid-time constant (LTC) networks, continuous-time neural architectures with input-dependent time constants, to systematically identify and prune sensor inputs wi…

Towards a European Quantum Act: A Two-Pillar Framework for Regulation and Innovation
Mauritz Kop
https://arxiv.org/abs/2509.14262 https://arxiv.org/pdf/250…

Towards a European Quantum Act: A Two-Pillar Framework for Regulation and Innovation
Quantum technologies promise transformative advancements but pose significant dual-use risks. Realizing their potential while mitigating risks necessitates a robust, anticipatory, and harmonized EU regulatory framework, grounded in the precautionary principle. Such a framework must be sui generis, as foundational quantum mechanical phenomena -incl. superposition, entanglement, and tunneling -defy the intuitive classical assumptions of factual certainty, causality, and locality that underpin exi…

How Causal Abstraction Underpins Computational Explanation
Atticus Geiger, Jacqueline Harding, Thomas Icard
https://arxiv.org/abs/2508.11214 https://arxiv.…

How Causal Abstraction Underpins Computational Explanation
Explanations of cognitive behavior often appeal to computations over representations. What does it take for a system to implement a given computation over suitable representational vehicles within that system? We argue that the language of causality -- and specifically the theory of causal abstraction -- provides a fruitful lens on this topic. Drawing on current discussions in deep learning with artificial neural networks, we illustrate how classical themes in the philosophy of computation and …

(Super)$\,$Gravity from Positivity
Brando Bellazzini, Alex Pomarol, Marcello Romano, Francesco Sciotti
https://arxiv.org/abs/2507.12535 https://

(Super)$\,$Gravity from Positivity
We investigate whether the effective theory for isolated, massive, and weakly interacting spin-$3/2$ particles is compatible with causality and unitarity-i.e., the positivity of scattering amplitudes. We find no solution to positivity constraints, except when gravitons are also present and couple in a (nearly) supersymmetric way. Gravity is thus bootstrapped from $S$-matrix consistency conditions for the longitudinal and transverse polarizations of massive spin-$3/2$ states. For two such partic…

Causality analysis of electricity market liberalization on electricity price using novel Machine Learning methods
Orr Shahar, Stefan Lessmann, Daniel Traian Pele
https://arxiv.org/abs/2507.12331

Causality analysis of electricity market liberalization on electricity price using novel Machine Learning methods
Relationships between the energy and the finance markets are increasingly important. Understanding these relationships is vital for policymakers and other stakeholders as the world faces challenges such as satisfying humanity's increasing need for energy and the effects of climate change. In this paper, we investigate the causal effect of electricity market liberalization on the electricity price in the US. By performing this analysis, we aim to provide new insights into the ongoing debate abou…

Causal mechanism and mediation analysis for macroeconomics dynamics: a bridge of Granger and Sims causality
Jean-Marie Dufour, Endong Wang
https://arxiv.org/abs/2509.05284 https…

Causal mechanism and mediation analysis for macroeconomics dynamics: a bridge of Granger and Sims causality
This paper introduces a novel concept of impulse response decomposition to disentangle the dynamic contributions of the mediator variables in the transmission of structural shocks. We justify our decomposition by drawing on causal mediation analysis and demonstrating its equivalence to the average mediation effect. Our result establishes a formal link between Sims and Granger causality. Sims causality captures the total effect, while Granger causality corresponds to the mediation effect. We con…

Topological Regularization of 1 Loop and 2 Loop Gravitational Corrections in the Higgs Fermion Sector
Sebasti\'an Al\'i Sacasa-C\'espedes
https://arxiv.org/abs/2508.13183

Topological Regularization of 1 Loop and 2 Loop Gravitational Corrections in the Higgs Fermion Sector
This work presents topological regularization (TR), a geometric framework for controlling ultraviolet divergences in gravitational loop corrections to the Higgs fermion sector. The method embeds flat spacetime into a compact four dimensional manifold, introducing a curvature induced high energy cutoff that preserves both Lorentz invariance and causality. The regulator is determined by the manifold's Euler characteristic, ensuring power law suppression of short distance modes without breaking sp…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Nonlinear Causality in Time Series Networks: With Application to Motor Imagery vs Execution
Sipan Aslan, Hernando Ombao

An equivalence between time-symmetry and cyclic causality in quantum theory
Eliot Jean, Ralph Silva, V. Vilasini
https://arxiv.org/abs/2508.02463 https://a…

An equivalence between time-symmetry and cyclic causality in quantum theory
Understanding the relationship between the time-symmetric nature of physical laws and the apparent directionality of causality is a central question in quantum foundations. The standard operational formulation, widely used in quantum information, imposes a definite, acyclic causal order on agents' operations, contrasting with time-symmetric dynamics. Two prominent extensions of this framework are the multi-time state (MTS) formalism, which incorporates time symmetry via arbitrary pre- and post-…

Temporal Incompatibility of Quantum Superposition and Relativistic Causality in Toponium Formation
Chang Xiong, Yu-Jie Zhang
https://arxiv.org/abs/2507.05703

Temporal Incompatibility of Quantum Superposition and Relativistic Causality in Toponium Formation
Wave-particle duality, a cornerstone of quantum mechanics, faces a stringent test in toponium formation. Quantum superposition predicts instantaneous projection into bound states upon top quark pair creation, while relativistic causality requires a finite formation time of $\sim n^3 \times 10^{-25}$ s for the $nS$ states. This temporal conflict challenges the coexistence of wavelike and particlelike behavior at ultrashort timescales. Exploiting the top quark's intrinsic lifetime ($τ_t \sim 5 \…

On the causality between affective impact and coordinated human-robot reactions
Morten Roed Frederiksen, Kasper St{\o}y
https://arxiv.org/abs/2508.04834 https://

On the causality between affective impact and coordinated human-robot reactions
In an effort to improve how robots function in social contexts, this paper investigates if a robot that actively shares a reaction to an event with a human alters how the human perceives the robot's affective impact. To verify this, we created two different test setups. One to highlight and isolate the reaction element of affective robot expressions, and one to investigate the effects of applying specific timing delays to a robot reacting to a physical encounter with a human. The first test was…

Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees
Ryohei Oura, yuji Ito
https://arxiv.org/abs/2507.07319

Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees
Recent decision-making systems are increasingly complicated, making it crucial to verify and understand their behavior for a given specification. A promising approach is to comprehensively explain undesired behavior in the systems modeled by Markov decision processes (MDPs) through formal verification and causal reasoning. However, the reliable explanation using model-based probabilistic causal analysis has not been explored when the MDP's transition probabilities are uncertain. This paper prop…

GCnet: Using Granger causality to explore the dynamic causality relations among genes as-sociated with intellectual disability in human brain
Lukas Madsen Brandt, Katja Nowick, Jing Qin
https://arxiv.org/abs/2508.05136

GCnet: Using Granger causality to explore the dynamic causality relations among genes as-sociated with intellectual disability in human brain
Intellectual disability (ID) is defined by an IQ under 70, in addition to deficits in two or more adaptive behaviors that affect everyday living. Throughout history, individuals with ID have often been margin-alized from society and continue to suffer significantly even in modern times. A varying proportion of ID cases are attributable to genetic causes. Identifying the causal relation among these ID-associated genes and their gene expression pattern during brain development process would gain …

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

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

Detecting Causality with Conjugation Quandles over Dihedral Groups
Zining Fan
https://arxiv.org/abs/2509.03544 https://arxiv.org/pdf/2509.03544

Detecting Causality with Conjugation Quandles over Dihedral Groups
We study whether quandle colorings can detect causality of events for links realized as skies in a $(2+1)$-dimensional globally hyperbolic spacetime $X$. Building off the Allen--Swenberg paper in which their $2$-sky link was conjectured to be causally related, they showed that the Alexander--Conway polynomial does not distinguish that link from the connected sum of two Hopf links, corresponding to two causally unrelated events. We ask whether the Alexander--Conway polynomial together with diffe…

Causal Emergence of Consciousness through Learned Multiscale Neural Dynamics in Mice
Zhipeng Wang, Yingqi Rong, Kaiwei Liu, Mingzhe Yang, Jiang Zhang, Jing He
https://arxiv.org/abs/2509.10891

Causal Emergence of Consciousness through Learned Multiscale Neural Dynamics in Mice
Consciousness spans macroscopic experience and microscopic neuronal activity, yet linking these scales remains challenging. Prevailing theories, such as Integrated Information Theory, focus on a single scale, overlooking how causal power and its dynamics unfold across scales. Progress is constrained by scarce cross-scale data and difficulties in quantifying multiscale causality and dynamics. Here, we present a machine learning framework that infers multiscale causal variables and their dynamics…

Detecting extreme event-driven causality
Siyang Yu, Yu Huang, Zuntao Fu
https://arxiv.org/abs/2509.05043 https://arxiv.org/pdf/2509.05043

Detecting extreme event-driven causality
The occurrence of some extreme events (such as marine heatwaves or exceptional circulations) can cause other extreme events (such as heatwave, drought and flood). These concurrent extreme events have a great impact on environment and human health. However, how to detect and quantify the causes and impacts of these extreme events by a data-driven way is still unsolved. In this study, the dynamic system method is extended to develop a method for detecting the causality between extreme events. Tak…

Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
Dubing Chen, Huan Zheng, Yucheng Zhou, Xianfei Li, Wenlong Liao, Tao He, Pai Peng, Jianbing Shen
https://arxiv.org/abs/2509.08388

Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
Vision-based 3D semantic occupancy prediction is a critical task in 3D vision that integrates volumetric 3D reconstruction with semantic understanding. Existing methods, however, often rely on modular pipelines. These modules are typically optimized independently or use pre-configured inputs, leading to cascading errors. In this paper, we address this limitation by designing a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline. Ground…

Searching for actual causes: Approximate algorithms with adjustable precision
Samuel Reyd, Ada Diaconescu, Jean-Louis Dessalles
https://arxiv.org/abs/2507.07857

Searching for actual causes: Approximate algorithms with adjustable precision
Causality has gained popularity in recent years. It has helped improve the performance, reliability, and interpretability of machine learning models. However, recent literature on explainable artificial intelligence (XAI) has faced criticism. The classical XAI and causality literature focuses on understanding which factors contribute to which consequences. While such knowledge is valuable for researchers and engineers, it is not what non-expert users expect as explanations. Instead, these users…

Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
Carlos Cinelli, Avi Feller, Guido Imbens, Edward Kennedy, Sara Magliacane, Jose Zubizarreta
https://arxiv.org/abs/2508.17099

Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
Causality and causal inference have emerged as core research areas at the interface of modern statistics and domains including biomedical sciences, social sciences, computer science, and beyond. The field's inherently interdisciplinary nature -- particularly the central role of incorporating domain knowledge -- creates a rich and varied set of statistical challenges. Much progress has been made, especially in the last three decades, but there remain many open questions. Our goal in this discuss…

Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
Xu Shang, Yang Zheng
https://arxiv.org/abs/2509.09027 https://arxiv.org/p…

Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
This paper explores the role of regularization in data-driven predictive control (DDPC) through the lens of convex relaxation. Using a bi-level optimization framework, we model system identification as an inner problem and predictive control as an outer problem. Within this framework, we show that several regularized DDPC formulations, including l1-norm penalties, projection-based regularizers, and a newly introduced causality-based regularizer, can be viewed as convex relaxations of their resp…

I guess we'll just need to run an RCT to investigate causality.

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Combs, Causality and Contractions in Atomic Markov Categories
Dario Stein, M\'ark Sz\'eles

A Spontaneous Symmetry Breaking Machine -- A Theory for a Novel Type of Spontaneous Symmetry Breaking in a Unique Dissipative System and one Application
Toshiya Sato
https://arxiv.org/abs/2507.11044

A Spontaneous Symmetry Breaking Machine -- A Theory for a Novel Type of Spontaneous Symmetry Breaking in a Unique Dissipative System and one Application
We focus on an interesting dissipative system (DS) found in a photonics system. In this DS, robust causality is created, and as a result, it becomes possible to find behavior that can be understood as spontaneous symmetry breaking (SSB). Furthermore, by arranging such DSs as basic elements and building certain relationships between them through optical interference, it becomes possible to create a unique system that causes complex SSB as a whole. This unique SSB can be understood as having a du…

Timelike convergence condition in regular black-hole spacetimes with (anti-)de Sitter core
Johanna Borissova, Stefano Liberati, Matt Visser
https://arxiv.org/abs/2509.08590 http…

Timelike convergence condition in regular black-hole spacetimes with (anti-)de Sitter core
The Hawking-Penrose (1970) singularity theorem weakens the causality assumption of global hyperbolicity used in the Penrose (1965) singularity theorem, at the expense of invoking the stronger timelike (instead of null) convergence condition (TCC). We analyze the TCC for a large class of dynamical spherically symmetric spacetimes, and show that it decomposes into three independent conditions on the Misner-Sharp mass function. For stationary black holes only two of these are non-trivial. One of t…

MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
Yunyang Cao, Juekai Lin, Wenhao Li, Bo Jin
https://arxiv.org/abs/2508.18873 https://

MOCHA: Discovering Multi-Order Dynamic Causality in Temporal Point Processes
Discovering complex causal dependencies in temporal point processes (TPPs) is critical for modeling real-world event sequences. Existing methods typically rely on static or first-order causal structures, overlooking the multi-order and time-varying nature of causal relationships. In this paper, we propose MOCHA, a novel framework for discovering multi-order dynamic causality in TPPs. MOCHA characterizes multi-order influences as multi-hop causal paths over a latent time-evolving graph. To model…

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Stav Armoni-Friedmann, Hana Chockler, David A. Kelly
https://arxiv.org/abs/2509.09982 https:/…

Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use case of AI models predicting the values of Boolean functions. We extend the previous work in this domain by proposing a formal and precise measure of importance of variables based on actual causality, and we evaluate state-of-the-art XAI tools against this measure. We also present a novel XAI tool B-ReX, based on the e…

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

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

Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
Marc Braun, Jose M. Pe\~na, Adel Daoud
https://arxiv.org/abs/2508.01321 https://

Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this paper, we make progress on this task by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects. While IV methods have been extended to non-separable structural models at the population level, existing approaches to c…

Teamwork as Linear Interpersonal Dynamics
Andrew Jun Lee, Grace Qiyuan Miao, Rick Dale, Alexia Galati, Hongjing Lu
https://arxiv.org/abs/2509.08811 https://

Teamwork as Linear Interpersonal Dynamics
Successful teamwork depends on interpersonal dynamics, the ways in which individuals coordinate, influence, and adapt to one another over time. Existing measures of interpersonal dynamics, such as CRQA, correlation, Granger causality, and transfer entropy, typically capture only a single dimension: either the synchrony/coordination or the direction of influence between individuals. What is missing is a psychologically meaningful representation that unifies these dimensions and varies systematic…

Twenty years of blazar monitoring with the INAF radio telescopes
N. Marchili (INAF Istituto di Radioastronomia), S. Righini (INAF Istituto di Radioastronomia - Stazione di Medicina), M. Giroletti (INAF Istituto di Radioastronomia), C. M. Raiteri (INAF Osservatorio Astrofisico di Torino), R. P. Giri (INAF Istituto di Radioastronomia, Dipartimento di Fisica e Astronomia 'Augusto Righi', Universita' di Bologna), M. I. Carnerero (INAF Osservatorio Astrofisico di Torino), M. Vil…

Twenty years of blazar monitoring with the INAF radio telescopes
The extreme variability of blazars, in both timescale and amplitude, is generally explained as the effect of a relativistic jet closely aligned to the observer's line-of-sight. Due to causality arguments, variability characteristics translate into spatial information about the emitting region of blazars. Since radiation at different wavelengths is emitted in different parts of the jet, multi-frequency observations provide us with a virtual view of the structure of the jet on different scales. R…

Nonlinear analysis of causality for heat flow in heavy-ion collisions: constraints from equation of state
Victor Roy
https://arxiv.org/abs/2508.03265 https://

Nonlinear analysis of causality for heat flow in heavy-ion collisions: constraints from equation of state
We explore the causal parameter space of the Mueller-Israel-Stewart second-order theory for heat-conducting fluids in nonlinear regimes for one-dimensional fluid flow. We show that this parameter space is highly constrained and particularly sensitive to the equation of state and second-order transport coefficients. Through numerical analysis of the characteristic equations, we identify regions of strong hyperbolicity, weak hyperbolicity, and non-hyperbolicity, mapping the boundaries of causalit…

Causality and Interpretability for Electrical Distribution System faults
Karthik Peddi, Sai Ram Aditya Parisineni, Hemanth Macharla, Mayukha Pal
https://arxiv.org/abs/2508.02524

Causality and Interpretability for Electrical Distribution System faults
Causal analysis helps us understand variables that are responsible for system failures. This improves fault detection and makes system more reliable. In this work, we present a new method that combines causal inference with machine learning to classify faults in electrical distribution systems (EDS) using graph-based models. We first build causal graphs using transfer entropy (TE). Each fault case is represented as a graph, where the nodes are features such as voltage and current, and the edges…

RCA Copilot: Transforming Network Data into Actionable Insights via Large Language Models
Alexander Shan, Jasleen Kaur, Rahul Singh, Tarun Banka, Raj Yavatkar, T. Sridhar
https://arxiv.org/abs/2507.03224

RCA Copilot: Transforming Network Data into Actionable Insights via Large Language Models
Ensuring the reliability and availability of complex networked services demands effective root cause analysis (RCA) across cloud environments, data centers, and on-premises networks. Traditional RCA methods, which involve manual inspection of data sources such as logs and telemetry data, are often time-consuming and challenging for on-call engineers. While statistical inference methods have been employed to estimate the causality of network events, these approaches alone are similarly challengi…

Observer Time from Causality in Perturbative Quantum Gravity
Allic Sivaramakrishnan
https://arxiv.org/abs/2506.16109 https://arxiv.or…

Observer Time from Causality in Perturbative Quantum Gravity
It is unclear whether an observable notion of time exists in quantum gravity even in principle because spacetime itself fluctuates. We propose a form of observable time in perturbative quantum gravity. First, we define an elapsed proper time in curved space using intersections of worldlines and future light cones. Here, time arises from causality via the dependence on light cones. We then propose that performing the gravitational path integral describes quantum gravity corrections to this notio…

A causality inspired acceleration method for the fast temporal superposition of the finite line source solutions
Marc Basquens, Alberto Lazzarotto
https://arxiv.org/abs/2507.18200

A causality inspired acceleration method for the fast temporal superposition of the finite line source solutions
We present a novel, fast method to compute thermal interactions in solids, useful for time-dependent problems involving several sources and several time and space scales such as the ones encountered in the physics of fields of closed loop borehole heat exchangers. The new method is based on the non-history temporal superposition acceleration algorithm, but presents better performance compared to the originally proposed scheme. The main idea behind it is to leverage the propagation properties of…

Information Propagation in Predator-Prey Dynamics of Turbulent Plasma
Tomohiro Tanogami, Makoto Sasaki, Tatsuya Kobayashi
https://arxiv.org/abs/2508.05127 https://

Information Propagation in Predator-Prey Dynamics of Turbulent Plasma
Magnetically confined fusion plasmas exhibit predator-prey-like cyclic oscillations through the self-regulating interaction between drift-wave turbulence and zonal flow. To elucidate the detailed mechanism and causality underlying this phenomenon, we construct a simple stochastic predator-prey model that incorporates intrinsic fluctuations and analyze its statistical properties from an information-theoretic perspective. We first show that the model exhibits persistent fluctuating cyclic oscilla…

Causality Detection via Symplectic Quandles
Amirbek Baxshilloyev
https://arxiv.org/abs/2508.18323 https://arxiv.org/pdf/2508.18323

Causality Detection via Symplectic Quandles
We study whether symplectic quandle colorings can reveal causal structure encoded by "sky links" - i.e. links consisting of spheres of all light rays through two points in the space of all light rays of a spacetime. Building on the known limitations of the Alexander-Conway polynomial, we compare the connected sum of two Hopf links (which represents all causally unrelated situations) with the first two Allen-Swenberg links (that are the only known examples when this polynomial does not work). Fo…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Deciphering interventional dynamical causality from non-intervention complex systems
Jifan Shi, Yang Li, Juan Zhao, Siyang Leng, Rui Bao, Kazuyuki Aihara, Luonan Chen, Wei Lin

Shear viscosity to entropy density ratio: A powerful tool for gravity theories and strongly coupled fluids
Maril\`u Chiofalo, Dario Grasso, Stefano Liberati, Massimo Mannarelli
https://arxiv.org/abs/2509.06178

Shear viscosity to entropy density ratio: A powerful tool for gravity theories and strongly coupled fluids
In this perspective review, we present a concise yet multifaceted overview of the pivotal role played by the the shear viscosity to entropy density ratio across various physical contexts. After summarizing some of the main aspects of the bound obtained by Kovtun, Son and Starinets, we examine potential sources of its violation, exploring the insights these may offer and their connections to fundamental causality conditions. We also review a range of experimental tests conducted in diverse, yet …

Geometry of effective field theory positivity cones
Quentin Bonnefoy, Vicente Cort\'es, Emanuele Gendy, Christophe Grojean, Karim Ritter von Merkl, Paula Naomi Pilatus
https://arxiv.org/abs/2508.18165

Geometry of effective field theory positivity cones
Positivity bounds are theoretical constraints on the Wilson coefficients of an effective field theory. These bounds emerge from the requirement that a given effective field theory must be the low-energy limit of a relativistic quantum theory that satisfies the fundamental principles of unitarity, locality, and causality. The task of deriving these bounds can be reformulated as the geometric problem of finding the extremal representation of a closed convex cone~$\mathcal C_W$. More precisely, in…

Neuro-Spectral Architectures for Causal Physics-Informed Networks
Arthur Bizzi, Leonardo M. Moreira, M\'arcio Marques, Leonardo Mendon\c{c}a, Christian J\'unior de Oliveira, Vitor Balestro, Lucas dos Santos Fernandez, Daniel Yukimura, Pavel Petrov, Jo\~ao M. Pereira, Tiago Novello, Lucas Nissenbaum
https://arxiv.org/abs/2509.04966

Neuro-Spectral Architectures for Causal Physics-Informed Networks
Physics-Informed Neural Networks (PINNs) have emerged as a powerful neural framework for solving partial differential equations (PDEs). However, standard MLP-based PINNs often fail to converge when dealing with complex initial-value problems, leading to solutions that violate causality and suffer from a spectral bias towards low-frequency components. To address these issues, we introduce NeuSA (Neuro-Spectral Architectures), a novel class of PINNs inspired by classical spectral methods, designe…

Maximum Reachability Orientation of Mixed Graphs
Florian H\"orsch
https://arxiv.org/abs/2506.16171 https://arxiv.org/pdf/2506.16…

Maximum Reachability Orientation of Mixed Graphs
We aim to find orientations of mixed graphs optimizing the total reachability, a problem that has applications in causality and biology. For given a digraph $D$, we use $P(D)$ for the set of ordered pairs of distinct vertices in $V(D)$ and we define $κ_D:P(D)\rightarrow \{0,1\}$ by $κ_D(u,v)=1$ if $v$ is reachable from $u$ in $D$, and $κ_D(u,v)=0$, otherwise. We use $R(D)=\sum_{(u,v)\in P(D)}κ_D(u,v)$. Now, given a mixed graph $G$, we aim to find an orientation $\vec{G}$ of $G$ that maxim…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Observational causality by states and interaction type for scientific discovery
\'Alvaro Mart\'inez-S\'anchez, Adri\'an Lozano-Dur\'an

Identifying Causal Direction via Dense Functional Classes
Katerina Hlavackova-Schindler, Suzana Marsela
https://arxiv.org/abs/2509.00538 https://arxiv.org/…

Identifying Causal Direction via Dense Functional Classes
We address the problem of determining the causal direction between two univariate, continuous-valued variables, X and Y, under the assumption of no hidden confounders. In general, it is not possible to make definitive statements about causality without some assumptions on the underlying model. To distinguish between cause and effect, we propose a bivariate causal score based on the Minimum Description Length (MDL) principle, using functions that possess the density property on a compact real in…

Nonlinear causality of Israel-Stewart theory with diffusion
Ian Cordeiro, F\'abio S. Bemfica, Enrico Speranza, Jorge Noronha
https://arxiv.org/abs/2507.20064 https://…

Nonlinear causality of Israel-Stewart theory with diffusion
We present the first fully nonlinear causality constraints in $D = 3 + 1$ dimensions for Israel-Stewart theory in the presence of energy and number diffusion in the Eckart and Landau hydrodynamic frames, respectively. These constraints are algebraic inequalities that make no assumption on the underlying geometry of the spacetime, or the equation of state. In order to highlight the distinct physical and structural behavior of the two hydrodynamic frames, we discuss the special ultrarelativistic …

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks
Qi Chai, Zhang Zheng, Junlong Ren, Deheng Ye, Zichuan Lin, Hao Wang
https://arxiv.org/abs/2508.18797 …

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks
Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In …

Inferring Diffusion Structures of Heterogeneous Network Cascade
Yubai Yuan, Siyu Huang, Abdul Basit Adeel
https://arxiv.org/abs/2506.19142 https://

Inferring Diffusion Structures of Heterogeneous Network Cascade
Network cascade refers to diffusion processes in which outcome changes within part of an interconnected population trigger a sequence of changes across the entire network. These cascades are governed by underlying diffusion networks, which are often latent. Inferring such networks is critical for understanding cascade pathways, uncovering Granger causality of interaction mechanisms among individuals, and enabling tasks such as forecasting or maximizing information propagation. In this project, …

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

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

Causal Decompositions of 1D Quantum Cellular Automata
Augustin Vanrietvelde, Octave Mestoudjian, Pablo Arrighi
https://arxiv.org/abs/2506.22219 https://

Causal Decompositions of 1D Quantum Cellular Automata
Understanding quantum theory's causal structure stands out as a major matter, since it radically departs from classical notions of causality. We present advances in the research program of causal decompositions, which investigates the existence of an equivalence between the causal and the compositional structures of unitary channels. Our results concern one-dimensional Quantum Cellular Automata (1D QCAs), i.e. unitary channels over a line of $N$ quantum systems (with or without periodic boundar…

Measurement-Induced Temporal Geometry
James C. Hateley
https://arxiv.org/abs/2507.04514 https://arxiv.org/pdf/2507.04514

Measurement-Induced Temporal Geometry
We propose a unified theoretical framework, Measurement-Induced Temporal Geometry (MTG), in which time, causality, and spacetime geometry emerge from quantum measurement acting on a fiber-valued internal time field. Each spacetime point supports a local degree of freedom $τ$, modeled as a smooth section of a fiber bundle $π: E \to M$, with projection events $μ[τ]$ generating classical temporal flow. Quantum coherence and entanglement are encoded in the curvature $F = \nabla^2$ of a connecti…

Controlling Enhancement of Transmitted Goos-H\"anchen Shifts: From Symmetric to Unidirectional
Zhuolin Wu, Weiming Zhen, Zhi-Cheng Ren, Xi-Lin Wang, Hui-Tian Wang, Jianping Ding
https://arxiv.org/abs/2506.16913

Controlling Enhancement of Transmitted Goos-Hänchen Shifts: From Symmetric to Unidirectional
Since the discovery of the Goos-Hänchen (GH) shift in the 1940s, its deep connections to Fourier transforms and causality have led to widespread interest and applications in optics, acoustics, and quantum mechanics. Control of the shift involves both its magnitude and direction. Although resonance-enhanced GH shift under reflection has significantly expanded and facilitated its observation and application, implementations in transmission scenarios remain scarce. More importantly, discussions o…

Nonlinear Network Reconstruction by Pairwise Time-delayed Transfer Entropy
Kai Chen, Zhong-qi K. Tian, Yifei Chen, Songting Li, Douglas Zhou
https://arxiv.org/abs/2507.02304

Nonlinear Network Reconstruction by Pairwise Time-delayed Transfer Entropy
Analyzing network structural connectivity is crucial for understanding dynamics and functions of complex networks across disciplines. In many networks, structural connectivity is not observable, which requires to be inferred via causal inference methods. Among them, transfer entropy (TE) is one of the most broadly applied causality measure due to its model-free property. However, TE often faces the curse of dimensionality in high-dimensional probability estimation, and the relation between the …

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Is Causality Necessary for Efficient Portfolios? A Computational Perspective on Predictive Validi...
Alejandro Rodriguez Dominguez

From Data Acquisition to Lag Modeling: Quantitative Exploration of A-Share Market with Low-Coupling System Design
Jianyong Fang, Sitong Wu, Junfan Tong
https://arxiv.org/abs/2506.19255

From Data Acquisition to Lag Modeling: Quantitative Exploration of A-Share Market with Low-Coupling System Design
We propose a novel two-stage framework to detect lead-lag relationships in the Chinese A-share market. First, long-term coupling between stocks is measured via daily data using correlation, dynamic time warping, and rank-based metrics. Then, high-frequency data (1-, 5-, and 15-minute) is used to detect statistically significant lead-lag patterns via cross-correlation, Granger causality, and regression models. Our low-coupling modular system supports scalable data processing and improves reprodu…

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Haoang Chi, He Li, Wenjing Yang, Feng Liu, Long Lan, Xiaoguang Ren, Tongliang Liu, Bo Han
https://arxiv.org/abs/2506.21215

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
Causal reasoning capability is critical in advancing large language models (LLMs) toward strong artificial intelligence. While versatile LLMs appear to have demonstrated capabilities in understanding contextual causality and providing responses that obey the laws of causality, it remains unclear whether they perform genuine causal reasoning akin to humans. However, current evidence indicates the contrary. Specifically, LLMs are only capable of performing shallow (level-1) causal reasoning, prim…

Scaling, Fractal Dynamics and Critical Exponents{: Application} in a non-integer dimensional Ising model
Henrique A. de Lima, Ismael S. S. Carrasco, Marcio Santos, Fernando A. Oliveira
https://arxiv.org/abs/2507.00956

Scaling, Fractal Dynamics and Critical Exponents{: Application} in a non-integer dimensional Ising model
Moving beyond simple associations, researchers need tools to quantify how variables influence each other in space and time. Correlation functions provide a mathematical framework for characterizing these essential dependencies, revealing insights into causality, structure, and hidden patterns within complex systems. In physical systems with many degrees of freedom, such as gases, liquids, and solids, a statistical analysis of these correlations is essential. For a field $Ψ(\vec{x},t)$ that dep…

Time series analysis of coupled slow-fast neuron models: From Hurst exponent to Granger causality
Indranil Ghosh, Hammed O. Fatoyinbo, Sishu S. Muni
https://arxiv.org/abs/2507.13570

Time series analysis of coupled slow-fast neuron models: From Hurst exponent to Granger causality
We perform time series analysis of small networks where every node is the slow-fast version of the denatured Morris--Lecar neuron proposed by Schaeffer and Cain. We choose popular coupling strategies from the literature and provide a detailed account of how varying their strength drives the dynamics of the small networks. Algorithms for time series analysis range from measuring their persistence (ability to remember past values), irregularity, chaos and quasiperiodicity, to synchronization betw…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Non-Sequential Decentralized Stochastic Control Revisited: Causality and Static Reducibility
Omar Mrani-Zentar, Ryan Simpson, Serdar Y\"uksel

Crosslisted article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[1/1]:
- Detecting Causality with Conjugation Quandles over Dihedral Groups
Zining Fan
ht…

Causality and Stability in Relativistic Hydrodynamics
Sukanya Mitra
https://arxiv.org/abs/2508.16127 https://arxiv.org/pdf/2508.16127

Causality and Stability in Relativistic Hydrodynamics
The causality and stability of a relativistic hydrodynamic theory is shown to require a consensus between, either (i) newer degrees of freedom apart from the fundamental fluid fields, or (ii) a general hydrodynamic frame other than the Landau or Eckart compromising the field's first principle definition, unless the non-equilibrium derivative correction goes to infinity. Any finitely truncated derivative correction (no matter how high it is) is shown to lead to an acausal theory, unless the corr…

Axion contribution to the mass-radius relation of neutron stars
Momchil Naydenov, Alberto Salvio
https://arxiv.org/abs/2506.16932 https://

Axion contribution to the mass-radius relation of neutron stars
A phenomenological Lagrangian for the interaction between a pseudo-scalar (axion-like) field and massive fermions is considered. At high density and non-zero temperature axions can be produced thermally through bremsstrahlung. We compute the equation of state for a static spherical cold compact object and investigate the numerical solutions of the Tolman-Oppenheimer-Volkoff equations. Causality of the obtained solutions sets a strict limitation on the possible values for the interaction constan…

State updates and useful qubits in relativistic quantum information
Jos\'e Polo-G\'omez, T. Rick Perche, Eduardo Mart\'in-Mart\'inez
https://arxiv.org/abs/2506.18906

State updates and useful qubits in relativistic quantum information
We address the longstanding challenge of consistently updating quantum states after selective measurements in a relativistic spacetime. Standard updates along the future lightcones preserve causality but break correlations between causally disconnected parties, whereas updates along the past lightcone either imply retrocausality or do not respect the causal propagation of information. We introduce a minimal extension of multipartite states to encode subsystem-specific contextual information. Th…

Causality of polarizeable dissipative fluids from Lagrangian hydrodynamics
David Montenegro, Giorgio Torrieri
https://arxiv.org/abs/2506.17327 https://

Causality of polarizeable dissipative fluids from Lagrangian hydrodynamics
We perform a causality analysis on the dispersion relation of hydrodynamics with spin well as shear and bulk viscosity, including the relaxation times for all these quantities. We find that the interplay of the three relaxational scales, for shear and bulk viscosity as well as polarization, leads to non-trivial effects on the dispersion relation. Unexpectedly, the presence of polarization leads to lower effective viscosity and a longer relaxation time, and the presence of viscosity leads to low…

Domain Generalization and Adaptation in Intensive Care with Anchor Regression
Malte Londschien, Manuel Burger, Gunnar R\"atsch, Peter B\"uhlmann
https://arxiv.org/abs/2507.21783

Domain Generalization and Adaptation in Intensive Care with Anchor Regression
The performance of predictive models in clinical settings often degrades when deployed in new hospitals due to distribution shifts. This paper presents a large-scale study of causality-inspired domain generalization on heterogeneous multi-center intensive care unit (ICU) data. We apply anchor regression and introduce anchor boosting, a novel, tree-based nonlinear extension, to a large dataset comprising 400,000 patients from nine distinct ICU databases. The anchor regularization consistently im…

Dynamical quasinormal mode excitation
Marina De Amicis, Enrico Cannizzaro, Gregorio Carullo, Laura Sberna
https://arxiv.org/abs/2506.21668 https://

Dynamical quasinormal mode excitation
We study the dynamical excitation of quasinormal modes (QNMs) through the plunge, merger and ringdown of an extreme-mass-ratio-inspiral into a Schwarzschild black hole, for generic orbital configurations. We work out the QNM causality condition, crucial to eliminate amplitude divergences and to incorporate horizon redshift effects. We then use it to derive a model of the time-dependent QNM excitation via a Green's function approach, driven by the point-particle source on a given trajectory. Our…

Testing Realism in Quantum Mechanics Through Charge Conservation
Victor Atanasov
https://arxiv.org/abs/2507.01965 https://arxiv.org/p…

Testing Realism in Quantum Mechanics Through Charge Conservation
The universe is not locally realistic. Abandoning causality often appears more palatable than giving up on realism. This paper proposes two novel experimental setups to test realism's failure using the conservation of electric charge. The first employs weak measurements of charge density in a double-slit interference setup. The second uses entangled charged particles in a Bell-type experiment, measuring electric field correlations to detect non-local charge distribution. Both leverage charge co…

A regression-based approach for bidirectional proximal causal inference in the presence of unmeasured confounding
Jiaqi Min, Xueyue Zhang, Shanshan Luo
https://arxiv.org/abs/2507.13965

A regression-based approach for bidirectional proximal causal inference in the presence of unmeasured confounding
Proxy variables are commonly used in causal inference when unmeasured confounding exists. While most existing proximal methods assume a unidirectional causal relationship between two primary variables, many social and biological systems exhibit complex feedback mechanisms that imply bidirectional causality. In this paper, using regression-based models, we extend the proximal framework to identify bidirectional causal effects in the presence of unmeasured confounding. We establish the identifica…

Exploring density dependent B as a suitable parameter in higher dimensional approach with a non-linear equation of state
Koushik Ballav Goswami, Debadri Bhattacharjee, Anirban Saha, Pradip Kumar Chattopadhyay
https://arxiv.org/abs/2508.20634

Exploring density dependent B as a suitable parameter in higher dimensional approach with a non-linear equation of state
In this investigation, we present a singularity free interior solution of the Einstein field equation for a class of anisotropic compact objects in dimensions $D\geq4$. In accordance with the concept of Vaidya and Tikekar, the geometry of the physical $(D-1)$-space of a star corresponding to $t=constant$ hypersurface is assumed to be of a $(D-1)$ spheroid. For the fulfilment of causality condition, a limit of the spheroidal parameter ($λ$) is noted depending on the values of amount of anisotro…

A Resummed Hydrodynamic Description of Relativistic Heavy-ion Collisions
Cheng Chiu, Gabriel Denicol, Matthew Luzum, Chun Shen
https://arxiv.org/abs/2508.05292 https://

A Resummed Hydrodynamic Description of Relativistic Heavy-ion Collisions
We introduce a resummed hydrodynamic scheme for evolving the viscous stress tensors in relativistic viscous hydrodynamics, based on which the necessary non-linear causality conditions can be imposed. When the magnitudes of the shear and bulk viscous stress tensors are small relative to the ideal part energy-momentum tensor, this new resummed scheme reduces to the standard second-order relativistic hydrodynamic theories. Nontrivial nonlinear corrections from high-order gradient terms retain the …

Topological Regularization
Sebasti\'an Al\'i Sacasa-C\'espedes
https://arxiv.org/abs/2508.00885 https://arxiv.org/pdf/2508.00885

Topological Regularization
This work introduces topological regularization as a framework for handling ultraviolet divergences in quantum field theory, reinterpreting infinities as topological obstructions at spacetime boundaries. Through geometric compactification via stereographic projection, singularities are reframed as boundary artifacts. The framework employs causal embeddings and the causality group to preserve Lorentz invariance and unitarity, while homotopy-equivalent defect structures guarantee regularization i…

Crosslisted article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[1/1]:
- Causality Detection via Symplectic Quandles
Amirbek Baxshilloyev
https://…

Causal Dynamic Resonance
Claudia Lainscsek, Pariya Salami, Simon Draeger, Sydney S. Cash, Terrence J. Sejnowski
https://arxiv.org/abs/2508.16733 https://ar…

Causal Dynamic Resonance
Natural dynamical systems, including the brain and climate, are highly nonlinear and complex. Determining information flow among the components that make up these dynamical systems is challenging. If the components are the result of a common process or become synchronized, causality measures typically fail, including Cross-Dynamical Delay Differential Analysis (CD-DDA). We previously introduced Dynamical Ergodicity Delay Differential Analysis (DE-DDA) to assess dynamical similarity. Here, we fu…

The perfect spinfluid
Nick Abboud, Lorenzo Gavassino, Rajeev Singh, Enrico Speranza
https://arxiv.org/abs/2506.19786 https://arxiv.or…

The perfect spinfluid
We present a new formulation of non-dissipative relativistic spin hydrodynamics that incorporates spin degrees of freedom into the divergence-type theory framework. Due to the divergence-type structure, it is straightforward to enforce non-linear causality and symmetric hyperbolicity of the equations of motion, ensuring local well-posedness of the initial-value problem and stability of the theory. Furthermore, in a specific realization based on spin kinetic theory, we prove that the equations o…

Validity of relativistic hydrodynamics beyond local equilibrium
Reghukrishnan Gangadharan
https://arxiv.org/abs/2508.17543 https://arxiv.org/pdf/2508.17543…

Validity of relativistic hydrodynamics beyond local equilibrium
We examine the applicability of relativistic hydrodynamics far from equilibrium by constructing formal solutions of the Boltzmann moment equations in the relaxation time approximation. These solutions naturally decompose into a divergent gradient series and exponentially decaying non-perturbative modes that encode initial conditions. The non-perturbative contributions are essential for understanding causality, the divergence of the gradient series, and the unexpected effectiveness of relativist…