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2512.21887
|
Aerial World Model for Long-horizon Visual Generation and Navigation in 3D Space
|
Unmanned aerial vehicles (UAVs) have emerged as powerful embodied agents. One of the core abilities is autonomous navigation in large-scale three-dimensional environments. Existing navigation policies, however, are typically optimized for low-level objectives such as obstacle avoidance and trajectory smoothness, lacking the ability to incorporate high-level semantics into planning. To bridge this gap, we propose ANWM, an aerial navigation world model that predicts future visual observations conditioned on past frames and actions, thereby enabling agents to rank candidate trajectories by their semantic plausibility and navigational utility. ANWM is trained on 4-DoF UAV trajectories and introduces a physics-inspired module: Future Frame Projection (FFP), which projects past frames into future viewpoints to provide coarse geometric priors. This module mitigates representational uncertainty in long-distance visual generation and captures the mapping between 3D trajectories and egocentric observations. Empirical results demonstrate that ANWM significantly outperforms existing world models in long-distance visual forecasting and improves UAV navigation success rates in large-scale environments.
| 2025-12-26
| 2025-12-29
|
[
"cs.RO",
"cs.AI"
] |
Weichen Zhang, Peizhi Tang, Xin Zeng, Fanhang Man, Shiquan Yu, Zichao Dai, Baining Zhao, Hongjin Chen, Yu Shang, Wei Wu, Chen Gao, Xinlei Chen, Xin Wang, Yong Li, Wenwu Zhu
|
2406.14138
|
Classification of orientable torus bundles over closed orientable surfaces
|
Let $g$ be a non-negative integer, $Σ_g$ a closed orientable surface of genus $g$, and $\mathcal{M}_g$ its mapping class group. We classify all the group homomorphisms $Ï_1(Σ_g)\to G$ up to the action of $\mathcal{M}_g$ on $Ï_1(Σ_g)$ in the following cases; (1) $G=PSL(2;\mathbb{Z})$, (2) $G=SL(2;\mathbb{Z})$. As an application of the case (2), we completely classify orientable $T^2$-bundles over closed orientable surfaces up to bundle isomorphisms. In particular, we show that any orientable $T^2$-bundle over $Σ_g$ with $g\geq 1$ is isomorphic to the fiber connected sum of $g$ pieces of $T^2$-bundles over $T^2$. Moreover, the classification result in the case (1) can be generalized into the case where $G$ is the free product of finite number of finite cyclic groups. We also apply it to an extension problem of maps from a closed surface to the connected sum of lens spaces.
| 2025-12-26
| 2025-12-29
|
[
"math.GT",
"math.AT",
"math.GR"
] |
Naohiko Kasuya, Issei Noda
|
2508.20987
|
Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation
|
Images manipulated by image editing tools can mislead viewers and pose significant risks to social security. However, accurately localizing manipulated image regions remains challenging due to the severe scarcity of high-quality annotated data, which is laborious to create. To address this, we propose a novel approach that mitigates data scarcity by leveraging readily available web data. We utilize a large collection of manually forged images from the web, as well as automatically generated annotations derived from a simpler auxiliary task, constrained image manipulation localization.Specifically, we introduce CAAAv2, a novel auto-annotation framework that operates on a category-aware, prior-feature-denoising paradigm that notably reduces task complexity. To further ensure annotation reliability, we propose QES, a novel metric that filters out low-quality annotations. Combining CAAAv2 and QES, we construct MIMLv2, a large-scale, diverse, and high-quality dataset containing 246,212 manually forged images with pixel-level mask annotations. This is over 120 times larger than existing handcrafted datasets like IMD20. Additionally, we introduce Object Jitter, a technique that further enhances model training by generating high-quality manipulation artifacts. Building on these advances, we develop Web-IML, a new model designed to effectively leverage web-scale supervision for the task of image manipulation localization. Extensive experiments demonstrate that our approach substantially alleviates the data scarcity problem and significantly improves the performance of various models on multiple real-world forgery benchmarks. With the proposed web supervision, our Web-IML achieves a striking performance gain of 31% and surpasses the previous state-of-the-art SparseViT by 21.6 average IoU points. The dataset and code will be released at https://github.com/qcf-568/MIML.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Chenfan Qu, Yiwu Zhong, Huiguo He, Bin Li, Lianwen Jin
|
2511.08882
|
On the existence, uniqueness and stability of solutions of SDEs with state-dependent variable exponent
|
We study a time-inhomogeneous nonlinear SDE with drift and diffusion governed by state-dependent variable exponents. This framework generalizes models like the geometric Brownian motion (GBM) and the constant elasticity of variance (CEV), offering flexibility to capture complex dynamics while posing analytical challenges. Using a fixed-point approach, we prove existence and uniqueness, analyze higher-order moments, derive asymptotic estimates, and assess stability. Finally, we illustrate an application where the Poisson equation admits a probabilistic representation via a time-homogeneous nonlinear SDE with state-dependent variable exponents.
| 2025-12-26
| 2025-12-29
|
[
"math.PR",
"math.AP"
] |
Mustafa Avci
|
2512.22340
|
Twisted Trilayer Graphene, Quasiperiodic Superconductor
|
Twisted multilayer moiré materials are generically quasiperiodic on the moiré scale due to the interference of different misaligned moiré periodicities. Spatial inhomogeneities such as these can be detrimental to superconductivity; nonetheless, superconductivity has been observed in quasiperiodic twisted trilayer graphene (TTG). Here, we systematically study the superconducting properties of TTG. We reveal that an interplay between quasiperiodicity and topology drives TTG into a critical regime, enabling it to host superconductivity with rigid phase stiffness for a wide range of twist angles, rather than at a fine-tuned value. The criticality in the normal state is due to the Dirac fermions coupled by quasiperiodic tunneling simulating 3D topological superconductor surface states. This critical-metal regime is marked by multifractal wave functions across the spectrum and scale-invariant transport reminiscent of the integer quantum Hall plateau transition. We demonstrate this with large-scale wave function and Kubo conductivity calculations. These observations lead to a clear experimental implication: stronger interlayer coupling in TTG further stabilizes both the criticality and superconductivity, allowing superconductivity to be seen across a wider range of angles with experimentally accessible pressures.
| 2025-12-26
| 2025-12-30
|
[
"cond-mat.mes-hall",
"cond-mat.dis-nn",
"cond-mat.supr-con"
] |
Xinghai Zhang, Ziyan Zhu, Justin H. Wilson, Matthew S. Foster
|
2512.22106
|
Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks
|
Neural network pruning is widely used to reduce model size and computational cost. Yet, most existing methods treat sparsity as an externally imposed constraint, enforced through heuristic importance scores or training-time regularization. In this work, we propose a fundamentally different perspective: pruning as an equilibrium outcome of strategic interaction among model components. We model parameter groups such as weights, neurons, or filters as players in a continuous non-cooperative game, where each player selects its level of participation in the network to balance contribution against redundancy and competition. Within this formulation, sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium. We analyze the resulting game and show that dominated players collapse to zero participation under mild conditions, providing a principled explanation for pruning behavior. Building on this insight, we derive a simple equilibrium-driven pruning algorithm that jointly updates network parameters and participation variables without relying on explicit importance scores. This work focuses on establishing a principled formulation and empirical validation of pruning as an equilibrium phenomenon, rather than exhaustive architectural or large-scale benchmarking. Experiments on standard benchmarks demonstrate that the proposed approach achieves competitive sparsity-accuracy trade-offs while offering an interpretable, theory-grounded alternative to existing pruning methods.
| 2025-12-26
| 2025-12-29
|
[
"cs.AI"
] |
Zubair Shah, Noaman Khan
|
2512.22378
|
Towards Efficient Post-Training via Fourier-Driven Adapter Architectures
|
We propose a novel framework, termed Fourier-Activated Adapter (FAA), for parameter-efficient fine-tuning of large pre-trained language models. By incorporating random Fourier features into lightweight adapter modules, FAA decomposes intermediate representations into complementary low- and high-frequency components, enabling frequency-aware modulation of semantic information. This design allows the model to selectively emphasize informative frequency bands during adaptation while preserving the representational capacity of the frozen backbone. Extensive experiments on GLUE, E2E NLG, and instruction-tuning benchmarks demonstrate that FAA consistently achieves competitive or superior performance compared to existing parameter-efficient fine-tuning methods, while maintaining low computational and memory overhead. Ablation studies further verify the effectiveness of frequency-aware activation and adaptive weighting mechanisms, highlighting FAA as a robust and efficient approach for post-training large language models.
| 2025-12-26
| 2025-12-30
|
[
"cs.CL",
"cs.AI"
] |
Donggyun Bae, Jongil Park
|
2510.00447
|
Representations of Josephson junction on the unit circle and the derivations of Mathieu operators and Fraunhofer patterns
|
The Hamiltonian J of the Josephson junction is introduced as a self-adjoint operator on l2 tensor l2. It is shown that J can also be realized as a self-adjoint operator HS1 on L2(S1) tensor L2(S1), from which a Mathieu operator given by "-d^2/dθ^2 - 2α cos θ" is derived. A fiber decomposition of HS1 with respect to the total particle number is established, and the action on each fiber is analyzed. In the presence of a magnetic field, a phase shift defines the magnetic Josephson junction Hamiltonian HS1(Φ) and the Josephson current IS1(Φ). For a constant magnetic field inducing a local phase shift Φ(x), the corresponding local current IS1(Φ(x)) is computed, and it is proved that the Fraunhofer pattern arises naturally.
| 2025-12-26
| 2025-12-29
|
[
"math-ph",
"math.MP"
] |
Toshiyuki Fujii, Fumio Hiroshima, Satoshi Tanda
|
2512.22314
|
Skands and coskands (The non-founded set theory with individuals and its model in the Field of all Conway numbers)
|
The basic one in this work is the axiomatic set theory $NBG$ (von Neumann-Bernays-G{ö}del), which is a first-order theory with its own axioms, including in particular the axiom of choice ${\bf AC}$ and the axiom of regularity ${\bf RA}$. The universal class ${\bf V}$ of all sets in this theory exactly coincides with the class of all founded sets, i.e., such $X\in{\bf V}$ that {\it does not exist} an infinitely descending $\in$-sequence $X\ni X_1\ni X_2\ni...\ni X_n\ni...$ of sets $X_n$, $n=1,2,3,...\,\,$. In the first part of the paper, a new concept of {\it skand} is introduced -- a random aggregate, or \grqq decreasing\grqq\, tuple composed of founded sets, e.g., $X=\{1,\{2,\{3,\{...\,\,\,...\}\}\}\}$, and the theory of $NBG^-=NBG-{\bf RA}$, i.e., the theory of $NBG$ without the axiom of regularity ${\bf RA}$, to which is added the new axiom ${\bf SEA}$ of the existence of infinite-length skands and the pseudo-founding axiom ${\bf PFA}$. These new axioms are a negation of the axiom of regularity and are thus less restrictive than the axiom of regularity ${\bf RA}$ in the sense that they admit the existence of non-founded sets, and the axiom of regularity excludes the existence of such sets. At the same time of course the axiom of extensionality ${\bf EA}$ is replaced by a more accurate axiom of extensionality ${\bf EEA}$, since it takes into account the equality of new objects. In the second part of the paper, a new concept of {\it coskand} is introduced, which is dual to a notion of skand and is a random aggregate, or \grqq increasing\grqq\, tuple composed of founded sets and the theory of $NBG$ and actually is a theory $NBG[\cal U]$ with individuals as limiting coskands, e.g., $X=...\{3,\{2,\{1,\{0\}\}\}\}...\,\,$.
| 2025-12-26
| 2025-12-30
|
[
"math.LO"
] |
Ju. T. Lisica
|
2512.22038
|
Mean-Field Analysis and Optimal Control of a Dynamic Rating and Matchmaking System
|
Large-scale competitive platforms are interacting multi-agent systems in which latent skills drift over time and pairwise interactions are shaped by matchmaking. We study a controlled rating dynamics in the mean-field limit and derive a kinetic description for the joint evolution of skills and ratings. In the Gaussian regime, we prove an exact moment closure and obtain a low-dimensional deterministic state dynamics for rating accuracy. This yields three main insights. First, skill drift imposes an intrinsic ceiling on long-run accuracy (the ``Red Queen'' effect). Second, with period-by-period scale control, the information content of interactions satisfies an invariance principle: under signal-matched scaling, the one-step accuracy transition is independent of matchmaking intensity. Third, the optimal platform policy separates: filtering is implemented by a greedy choice of the gain and rating scale, while matchmaking reduces to a static trade-off between match utility and sorting costs.
| 2025-12-26
| 2025-12-29
|
[
"math.OC"
] |
Wataru Nozawa
|
2507.06325
|
Optimization of Fractal Image Compression
|
Fractal Image Compression (FIC) is a lossy image compression technique that leverages self-similarity within an image to achieve high compression ratios. However, the process of compressing the image is computationally expensive. This paper investigates optimization techniques to improve the efficiency of FIC, focusing on increasing compression ratio and reducing computational time. The paper explores a novel approach named the Box Counting Method for estimating fractal dimensions, which is very simple to integrate into FIC compared to other algorithms. The results show that implementing these optimization techniques enhances both the compression ratio and the compression time.
| 2025-12-26
| 2025-12-30
|
[
"eess.IV"
] |
Nastaran Pourshab Mohsen Bagheritabar
|
2512.22062
|
Existence of spectral submanifolds in time delay systems
|
Spectral submanifolds (SSMs) are invariant manifolds of a dynamical system, defined by the property of being tangent to a spectral subspace of the linearized dynamics at a steady state. We show existence, along with certain desirable properties such as smoothness, attractivity and conditional uniqueness, of SSMs associated to a large class of spectral subspaces in time delay systems. Building on these results, we generalize the criteria for existence of inertial manifolds -- defined as globally exponentially attracting Lipschitz invariant manifolds of finite dimension -- and show that they need not have dimension equal to that of the physical configuration, in contrast to previous accounts. We then demonstrate the applicability of these results on a few simple examples.
| 2025-12-26
| 2025-12-29
|
[
"math.DS",
"nlin.CD"
] |
Gergely Buza, George Haller
|
2512.20068
|
Change Point Detection and Mean-Field Dynamics of Variable Productivity Hawkes Processes
|
Many self-exciting systems change because endogenous amplification, as opposed to exogenous forcing, varies. We study a Hawkes process with fixed background rate and kernel, but piecewise time-varying productivity. For exponential kernels we derive closed-form mean-field relaxation after a change and a deterministic surrogate for post-change Fisher information, revealing a boundary layer in which change time information localises and saturates, while post-change level information grows linearly beyond a short transient. These results motivate a Bayesian change point procedure that stabilizes inference on finite windows. We illustrate the method on invasive pneumococcal disease incidence in The Gambia, identifying a decline in productivity aligned with pneumococcal conjugate vaccine rollout.
| 2025-12-26
| 2025-12-30
|
[
"stat.OT",
"math.ST",
"stat.TH"
] |
Conor Kresin, Boris Baeumer, Sophie Phillips
|
2510.02781
|
GCVAMD: A Modified CausalVAE Model for Causal Age-related Macular Degeneration Risk Factor Detection and Prediction
|
Age Related Macular Degeneration(AMD) has been one of the most leading causes of permanent vision impairment in ophthalmology. Though treatments, such as anti VEGF drugs or photodynamic therapies, were developed to slow down the degenerative process of AMD, there is still no specific cure to reverse vision loss caused by AMD. Thus, for AMD, detecting existence of risk factors of AMD or AMD itself within the patient retina in early stages is a crucial task to reduce the possibility of vision impairment. Apart from traditional approaches, deep learning based methods, especially attention mechanism based CNNs and GradCAM based XAI analysis on OCT scans, exhibited successful performance in distinguishing AMD retina from normal retinas, making it possible to use AI driven models to aid medical diagnosis and analysis by ophthalmologists regarding AMD. However, though having significant success, previous works mostly focused on prediction performance itself, not pathologies or underlying causal mechanisms of AMD, which can prohibit intervention analysis on specific factors or even lead to less reliable decisions. Thus, this paper introduces a novel causal AMD analysis model: GCVAMD, which incorporates a modified CausalVAE approach that can extract latent causal factors from only raw OCT images. By considering causality in AMD detection, GCVAMD enables causal inference such as treatment simulation or intervention analysis regarding major risk factors: drusen and neovascularization, while returning informative latent causal features that can enhance downstream tasks. Results show that through GCVAMD, drusen status and neovascularization status can be identified with AMD causal mechanisms in GCVAMD latent spaces, which can in turn be used for various tasks from AMD detection(classification) to intervention analysis.
| 2025-12-26
| 2025-12-29
|
[
"eess.IV",
"cs.CV"
] |
Daeyoung Kim
|
2404.17641
|
Patterns of active dipolar particles in external magnetic fields
|
Active particles with a (magnetic) dipole moment are of interest for steering self-propelled motion, but also result in novel collective effects due to their dipole-dipole interaction. Here systems of active dipolar particles are studied with Brownian dynamics simulations to systematically characterize the different patterns they form, specifically in the presence of an external (magnetic) field. The combination of three types of order - clustering, orientational alignment and chain formation - is used to classify the patterns observed in these systems. In the presence of an external field, oriented chains and bands are found to be dominant. These structures show some similarities with columnar cluster seen in (passive) ferrofluids and display columnar spacing and number of lanes per cluster that both decrease with increasing field strength.
| 2025-12-26
| 2025-12-30
|
[
"cond-mat.soft",
"cond-mat.stat-mech"
] |
Vitali Telezki, Stefan Klumpp
|
2501.02323
|
Encoding Sequences in Intuitionistic Real Algebra
|
We show that in the presence of random Kripke's schema choice sequences can be recursively encoded in intuitionistic real algebra.
| 2025-12-26
| 2025-12-29
|
[
"math.LO"
] |
Miklós Erdélyi-Szabó
|
2512.16917
|
Generative Adversarial Reasoner: Enhancing LLM Reasoning with Adversarial Reinforcement Learning
|
Large language models (LLMs) with explicit reasoning capabilities excel at mathematical reasoning yet still commit process errors, such as incorrect calculations, brittle logic, and superficially plausible but invalid steps. In this paper, we introduce Generative Adversarial Reasoner, an on-policy joint training framework designed to enhance reasoning by co-evolving an LLM reasoner and an LLM-based discriminator through adversarial reinforcement learning. A compute-efficient review schedule partitions each reasoning chain into logically complete slices of comparable length, and the discriminator evaluates each slice's soundness with concise, structured justifications. Learning couples complementary signals: the LLM reasoner is rewarded for logically consistent steps that yield correct answers, while the discriminator earns rewards for correctly detecting errors or distinguishing traces in the reasoning process. This produces dense, well-calibrated, on-policy step-level rewards that supplement sparse exact-match signals, improving credit assignment, increasing sample efficiency, and enhancing overall reasoning quality of LLMs. Across various mathematical benchmarks, the method delivers consistent gains over strong baselines with standard RL post-training. Specifically, on AIME24, we improve DeepSeek-R1-Distill-Qwen-7B from 54.0 to 61.3 (+7.3) and DeepSeek-R1-Distill-Llama-8B from 43.7 to 53.7 (+10.0). The modular discriminator also enables flexible reward shaping for objectives such as teacher distillation, preference alignment, and mathematical proof-based reasoning.
| 2025-12-26
| 2025-12-29
|
[
"cs.AI",
"cs.CL",
"cs.LG"
] |
Qihao Liu, Luoxin Ye, Wufei Ma, Yu-Cheng Chou, Alan Yuille
|
2507.21550
|
Hierarchies within TFNP: building blocks and collapses
|
In all well-studied $\mathsf{TFNP}$ subclasses (e.g. $\mathsf{PPA}, \mathsf{PPP}$ etc.), the canonical complete problem takes as input a polynomial-size circuit $C: \{ 0, 1\}^n \rightarrow \{ 0, 1\}^m$ whose input-output behavior implicitly encodes an exponentially large object $G$, i.e. $C$ is the succinct (polynomial-size) representation of the exponential size object $G$. The goal is to find some particular substructure in $G$ which can be confirmed in polynomial time using queries to $C$.
We initiate the study of classes of the form $\mathsf{A}^{\mathsf{B}}$ where both $\mathsf{A}$ and $\mathsf{B}$ are $\mathsf{TFNP}$ subclasses. In particular, we define complete problems for these classes that take as input a circuit $C$ which is allowed oracle gates to another $\mathsf{TFNP}$ class.
Beyond introducing definitions for $\mathsf{TFNP}$ oracle problems, our specific technical contributions include showing that several $\mathsf{TFNP}$ subclasses are self-low and hence their corresponding hierarchies collapse. In particular, $\mathsf{PPA^{PPA}} = \mathsf{PPA}$, $\mathsf{PLS^{PLS}} = \mathsf{PLS}$, and $\mathsf{LOSSY^{LOSSY}} = \mathsf{LOSSY}$. As an immediate consequence, we derive that when reducing to $\mathsf{PPA}$, one can always assume access to $\mathsf{PPA}$ -- and therefore factoring -- oracle gates.
In addition to introducing a variety of hierarchies within $\mathsf{TFNP}$ that merit study in their own right, these ideas introduce a novel approach for classifying computational problems within $\mathsf{TFNP}$ and proving black-box separations. For example, we observe that the problem of deterministically generating large prime numbers, which has long resisted classification in a $\mathsf{TFNP}$ subclass, is in $\mathsf{PPP^{\mathsf{PPP}}}$ under the Generalized Riemann Hypothesis.
| 2025-12-26
| 2025-12-29
|
[
"cs.CC"
] |
Surendra Ghentiyala, Zeyong Li
|
2508.07468
|
CP-Agent: Agentic Constraint Programming
|
Translating natural language into formal constraint models requires expertise in the problem domain and modeling frameworks. To investigate whether constraint modeling benefits from agentic workflows, we introduce CP-Agent, a Python coding agent using the ReAct framework with a persistent IPython kernel. Domain knowledge is provided through a project prompt of under 50 lines. The agent iteratively executes code, observes the solver's feedback, and refines models based on the execution results.
We evaluate CP-Agent on CP-Bench's 101 constraint programming problems. We clarified the benchmark to address systematic ambiguities in problem specifications and errors in ground-truth models. On the clarified benchmark, CP-Agent solves all 101 problems. Ablation studies indicate that minimal guidance outperforms detailed procedural scaffolding, and that explicit task management tools have mixed effects on focused modeling tasks.
| 2025-12-26
| 2025-12-29
|
[
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SE"
] |
Stefan Szeider
|
2512.22369
|
A scanning probe microscopy approach for identifying defects in aluminum oxide
|
The coherence of quantum dot qubits fabricated in semiconductors is often limited by charge noise from defects in gate dielectrics, which are material- and process-dependent. Characterizing these defects is an important step towards reducing their impact and improving qubit coherence. The identification of individual defects requires atomic-scale spatial resolution, however, and sufficient spectral sensitivity to determine their electronic structure. Electrostatic force microscopy (EFM) provides highly resolved maps of the surface potential of dielectrics, and importantly, is also sensitive to single-electron charging processes that reflect the spectral structure of underlying defects. In this work, we use cryogenic EFM to characterize aluminum oxide grown by atomic layer deposition (ALD) on bulk silicon. These measurements reveal defects close to the surface that exchange electrons with the EFM tip as they transition through different charge states. Detailed electrostatic modeling opens the door to powerful techniques for mapping tip-backgate charging voltages onto defect transition energies, allowing defects such as aluminum vacancies, and carbon, oxygen, or hydrogen impurities to be identified, by comparing to density functional theory (DFT). These results point towards EFM as a powerful tool for exploring defect structures in solid-state qubits.
| 2025-12-26
| 2025-12-30
|
[
"cond-mat.mes-hall"
] |
Leah Tom, Zachary J. Krebs, Joel B. Varley, E. S. Joseph, Wyatt A. Behn, M. A. Eriksson, Keith G. Ray, Vincenzo Lordi, S. N. Coppersmith, Victor W. Brar, Mark Friesen
|
2405.07375
|
Lie superalgebra invariants and almost classical knots
|
A virtual link is said to be almost classical (AC) if it has a homologically trivial representative in some thickened surface $Σ\times [0,1]$, where $Σ$ is a closed orientable surface. AC links provide a useful window for observing the geometric topology of virtual knots. Here we take a different approach and look at AC links through the lens of quantum topology. Two adjustments are needed to the existing theory. First, it is necessary to generalize the definition of AC to include virtual tangles and, in particular, virtual braids. Secondly, to distinguish AC and non-AC tangles, the additional structure of quantum supergroups is required. For each Lie superalgebra $\mathfrak{gl}(m|n)$, we define a pair of $U_q(\mathfrak{gl}(m|n))$ Reshetikhin-Turaev functors $Q^{m|n}$, $\widetilde{Q}^{m|n} \circ Zh$ on framed virtual tangles. Here $Zh$ denotes the Bar-Natan $Zh$ construction. These functors unify the Alexander polynomial (AP) of AC links and the generalized Alexander polynomial (GAP) of all virtual links into a single quantum model: $Q^{1|1}$ recovers the AP of an AC link and for any virtual link $K$, $\widetilde{Q}^{1|1}\circ Zh(K)$ is the 2-variable GAP. However, when $(m,n) \ne (1,1)$, these invariants are generally distinct from the AP and GAP. Furthermore, in contrast to the classical case, they are not determined by $m-n$. For example, there are virtual knots with trivial GAP but nontrivial $U_q(\mathfrak{gl}(2|2))$ and $U_q(\mathfrak{gl}(3|3))$ invariants.
Silver and Williams proved that the GAP vanishes on all AC links. Our main result is a generalization of this theorem to almost classical tangles and the $U_q(\mathfrak{gl}(m|n))$ Reshetikhin-Turaev functors. We prove that if $T$ is an almost classical tangle, then $\widetilde{Q}^{m|n}\circ Zh(T)$ is conjugate to $Q^{m|n}(T)$, with conjugation determined by an Alexander numbering of $T$.
| 2025-12-26
| 2025-12-30
|
[
"math.GT"
] |
Micah Chrisman, Anup Poudel
|
2511.02845
|
AI-Enhanced Real-Time Wi-Fi Sensing Through Single Transceiver Pair
|
The advancement of next-generation Wi-Fi technology heavily relies on sensing capabilities, which play a pivotal role in enabling sophisticated applications. In response to the growing demand for large-scale deployments, contemporary Wi-Fi sensing systems strive to achieve high-precision perception while maintaining minimal bandwidth consumption and antenna count requirements. Remarkably, various AI-driven perception technologies have demonstrated the ability to surpass the traditional resolution limitations imposed by radar theory. However, the theoretical underpinnings of this phenomenon have not been thoroughly investigated in existing research. In this study, we found that under hardware-constrained conditions, the performance gains brought by AI to Wi-Fi sensing systems primarily originate from two aspects: prior information and temporal correlation. Prior information enables the AI to generate plausible details based on vague input, while temporal correlation helps reduce the upper bound of sensing error. Building on these insights, we developed a real-time, AI-based Wi-Fi sensing and visualization system using a single transceiver pair, and designed experiments focusing on human pose estimation and indoor localization. The system operates in real time on commodity hardware, and experimental results confirm our theoretical findings.
| 2025-12-26
| 2025-12-29
|
[
"eess.SP",
"cs.AI",
"physics.ins-det"
] |
Yuxuan Liu, Chiya Zhang, Yifeng Yuan, Chunlong He, Weizheng Zhang, Gaojie Chen
|
2507.20908
|
Probabilistic Link Budget Analysis for Low Earth Orbit Satellites in the Optical Regime
|
Low Earth Orbit (LEO) optical satellite communication systems face performance challenges due to atmospheric effects such as scintillation, turbulence, wavefront distortion, beam spread, and jitter. This paper presents a comprehensive mathematical model to characterize these effects and their impact on signal propagation. We develop a methodology for dynamically calculating link budgets at any location and time by integrating these models into a probabilistic framework. The approach accounts for spatial and temporal variations in atmospheric conditions, enabling accurate estimation of link loss probabilities. Simulations validate the model's accuracy and applicability to real-world LEO satellite systems. This work offers a robust tool for optimizing link performance and enhancing the reliability of satellite networks, providing valuable insights for system designers and operators.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.IM",
"physics.ao-ph",
"physics.optics"
] |
Dhruv Shivkant, Shreyaans Jain, Rohit K Ramakrishnan
|
2512.22398
|
Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings
|
Foundation models for knowledge graphs (KGs) achieve strong cohort-level performance in link prediction, yet fail to capture individual user preferences; a key disconnect between general relational reasoning and personalized ranking. We propose GatedBias, a lightweight inference-time personalization framework that adapts frozen KG embeddings to individual user contexts without retraining or compromising global accuracy. Our approach introduces structure-gated adaptation: profile-specific features combine with graph-derived binary gates to produce interpretable, per-entity biases, requiring only ${\sim}300$ trainable parameters. We evaluate GatedBias on two benchmark datasets (Amazon-Book and Last-FM), demonstrating statistically significant improvements in alignment metrics while preserving cohort performance. Counterfactual perturbation experiments validate causal responsiveness; entities benefiting from specific preference signals show 6--30$\times$ greater rank improvements when those signals are boosted. These results show that personalized adaptation of foundation models can be both parameter-efficient and causally verifiable, bridging general knowledge representations with individual user needs.
| 2025-12-26
| 2025-12-30
|
[
"cs.AI"
] |
Ozan Oguztuzun, Cerag Oguztuzun
|
2512.07343
|
Linear codes over a mixed-alphabet ring and their Gray images with applications to projective and locally repairable codes
|
Let $m \geq 2$ be an integer, and let $\mathbb{F}_q$ be the finite field of prime power order $q.$ Let $\mathcal{R}=\frac{\mathbb{F}_q[u]}{\langle u^2 \rangle}\times \mathbb{F}_q$ be the mixed-alphabet ring, where $\frac{\mathbb{F}_q[u]}{\langle u^2 \rangle}$ is the quasi-Galois ring with maximal ideal $\langle u\rangle$ of nilpotency index $2$ and residue field $\mathbb{F}_q.$ In this paper, we construct four infinite families of linear codes over the ring $\frac{\mathbb{F}_q[u]}{\langle u^2 \rangle}$ whose defining sets are certain non-empty subsets of $\mathcal{R}^m$ associated with three simplicial complexes of $\mathbb{F}_q^m,$ each possessing a single maximal element. We explicitly determine the parameters and Lee weight distributions of these codes. We also study their Gray images and identify several infinite families of few-weight codes over $\mathbb{F}_q,$ as well as an infinite family of minimal, near-Griesmer and distance-optimal codes over $\mathbb{F}_q.$ We also observe that their Gray images are self-orthogonal codes for $q=2$ or $3.$ We determine spanning matrices of these codes. Leveraging this result, we provide two constructions of infinite families of projective few-weight codes over $\mathbb{F}_q$ with new parameters. As an application of our newly constructed minimal codes over $\mathbb{F}_q,$ we examine the minimal access structures of Masseys secret sharing schemes based on their duals and determine the number of dictatorial participants in these schemes. Finally, we investigate the locality properties of our newly constructed projective codes and show that these codes have locality either $2$ or $3.$ As a consequence, we obtain four infinite families of $q$-ary locally repairable codes (LRCs) with locality $2,$ and two infinite families of binary LRCs with locality $3.$
| 2025-12-26
| 2025-12-29
|
[
"cs.IT",
"math.IT"
] |
Leijo Jose, Lavanya G., Anuradha Sharma
|
2601.00834
|
Intrinsic-Metric Physics-Informed Neural Networks (IM-PINN) for Reaction-Diffusion Dynamics on Complex Riemannian Manifolds
|
Simulating nonlinear reaction-diffusion dynamics on complex, non-Euclidean manifolds remains a fundamental challenge in computational morphogenesis, constrained by high-fidelity mesh generation costs and symplectic drift in discrete time-stepping schemes. This study introduces the Intrinsic-Metric Physics-Informed Neural Network (IM-PINN), a mesh-free geometric deep learning framework that solves partial differential equations directly in the continuous parametric domain. By embedding the Riemannian metric tensor into the automatic differentiation graph, our architecture analytically reconstructs the Laplace-Beltrami operator, decoupling solution complexity from geometric discretization. We validate the framework on a "Stochastic Cloth" manifold with extreme Gaussian curvature fluctuations ($K \in [-2489, 3580]$), where traditional adaptive refinement fails to resolve anisotropic Turing instabilities. Using a dual-stream architecture with Fourier feature embeddings to mitigate spectral bias, the IM-PINN recovers the "splitting spot" and "labyrinthine" regimes of the Gray-Scott model. Benchmarking against the Surface Finite Element Method (SFEM) reveals superior physical rigor: the IM-PINN achieves global mass conservation error of $\mathcal{E}_{mass} \approx 0.157$ versus SFEM's $0.258$, acting as a thermodynamically consistent global solver that eliminates mass drift inherent in semi-implicit integration. The framework offers a memory-efficient, resolution-independent paradigm for simulating biological pattern formation on evolving surfaces, bridging differential geometry and physics-informed machine learning.
| 2025-12-26
| 2026-01-06
|
[
"cs.LG",
"cs.AI"
] |
Julian Evan Chrisnanto, Salsabila Rahma Alia, Nurfauzi Fadillah, Yulison Herry Chrisnanto
|
2408.07139
|
The spectral gap and principle eigenfunction of the random conductance model in a line segment
|
In this paper, we study the spectral gap and principle eigenfunction of the random walk in the line segment $[1, N]$ with conductances $c^{(N)}(x, x+1)_{1\le x<N}$ where $c^{(N)}(x, x+1)>0$ is the rate of the random walk jumping from site $x$ to site $x+1$ and vice versa. Writing $r^{(N)}(x, x+1) := 1/c^{(N)}(x, x+1)$, under the assumption \begin{equation*}
\limsup_{N\to \infty}\, \frac{1}{N}\sup_{1< m \le N}\, \left| \sum_{x=2}^m r^{(N)}(x-1, x)- (m-1) \right|\;=\;0\,, \end{equation*} we prove that the spectral gap, denoted by $\mathrm{gap}_{N}$, of the process satisfies $\mathrm{gap}_{N}=(1+o(1))Ï^2/N^2$ and the principle eigenfunction $g_N$ with $g_N(1)=1$ corresponding to the spectral gap is well approximated by $h_N(x) := \cos\left( (x-1/2)Ï/N \right)$.
| 2025-12-26
| 2025-12-29
|
[
"math.PR",
"math-ph",
"math.MP"
] |
Shangjie Yang
|
2512.22339
|
Gravitational waves from seesaw assisted collapsing domain walls
|
Spontaneous breaking of discrete symmetries like $Z_2$ leads to the formation of stable topological defects such as domain walls which, if allowed to dominate, can potentially be in conflict with cosmological observations. Incorporating explicit $Z_2$-breaking bias terms can lead to annihilation of such walls while also emitting stochastic gravitational wave (GW). We study the role of heavy right-handed neutrinos present in type-I seesaw origin of light neutrino masses to generate such bias term via quantum corrections. This offers interesting correlation among the seesaw scale, GW peak amplitude and peak frequency which can be probed at present and future experiments related to GW as well as precision measurements of the cosmic microwave background (CMB). In flavor symmetric UV complete scenarios with degenerate RHNs at leading order, such tiny coupling of RHNs to a $Z_2$-odd scalar can also lead to small mass splittings suitable for explaining the observed baryon asymmetry of the universe via resonant leptogenesis.
| 2025-12-26
| 2025-12-30
|
[
"hep-ph",
"astro-ph.CO"
] |
Debasish Borah, Indrajit Saha
|
2512.21913
|
GQ-VAE: A gated quantized VAE for learning variable length tokens
|
While most frontier models still use deterministic frequency-based tokenization algorithms such as byte-pair encoding (BPE), there has been significant recent work to design learned neural tokenizers. However, these schemes generally add to underlying language model complexity and force large changes to architecture, making them hard to implement at large scales. To overcome these challenges, we propose the gated quantized variational autoencoder (GQ-VAE), a novel architecture that can be independently pre-trained to serve as a drop-in replacement for existing tokenizers. The key innovation of the architecture is to learn to encode variable-length discrete tokens. GQ-VAE improves compression and language modeling performance over a standard VQ-VAE tokenizer, and approaches the compression rate and language modeling performance of BPE. Interestingly, if we use BPE with a smaller vocabulary, such that the compression is equivalent between GQ-VAE and BPE, we find that GQ-VAE improves downstream language model learning. We conclude with a discussion of several exciting avenues for future work. Code can be found at https://github.com/Theo-Datta-115/gq-vae.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Theo Datta, Kayla Huang, Sham Kakade, David Brandfonbrener
|
2501.16587
|
HopCast: Calibration of Autoregressive Dynamics Models
|
Deep learning models are often trained to approximate dynamical systems that can be modeled using differential equations. Many of these models are optimized to predict one step ahead; such approaches produce calibrated one-step predictions if the predictive model can quantify uncertainty, such as Deep Ensembles. At inference time, multi-step predictions are generated via autoregression, which needs a sound uncertainty propagation method to produce calibrated multi-step predictions. This work introduces an alternative Predictor-Corrector approach named \hop{} that uses Modern Hopfield Networks (MHN) to learn the errors of a deterministic Predictor that approximates the dynamical system. The Corrector predicts a set of errors for the Predictor's output based on a context state at any timestep during autoregression. The set of errors creates sharper and well-calibrated prediction intervals with higher predictive accuracy compared to baselines without uncertainty propagation. The calibration and prediction performances are evaluated across a set of dynamical systems. This work is also the first to benchmark existing uncertainty propagation methods based on calibration errors.
| 2025-12-26
| 2025-12-29
|
[
"cs.LG"
] |
Muhammad Bilal Shahid, Cody Fleming
|
2601.02395
|
On (Newcomb-)Benford's law: a tale of two papers and of their disproportionate citations. How citation counts can become biased
|
The first digit (FD) phenomenon i.e., the significant digits of numbers in large data are often distributed according to a logarithmically decreasing function was first reported by S. Newcomb and then many decades later independently by F. Benford. After its century long neglect the last three decades have seen huge growth in the number of relevant publications. However, notwithstanding the rising popularity the two independent proponents of the phenomenon are not equally acknowledged an indication of which is disproportionate number of citations accumulated by Newcomb (1881) and Benford (1938). In the present study use citation analysis to show that the formalization of the eponym Benford's law, a name questionable itself for overlooking Newcomb's contribution, by Raimi (1976) had a strong adverse effect on the future citations of Newcomb (1881). Furthermore, we identify the papers published over various decades of the developmental history of the FD phenomenon, which latter turned out to be amongst the most cited ones in the field. We find that lack of its consideration, intentional or occasionally out of ignorance for referencing by the prominent papers, is responsible for a far lesser number of citations of Newcomb (1881) in comparison to Benford (1938).
| 2025-12-26
| 2026-01-07
|
[
"physics.soc-ph",
"cs.DL"
] |
Tariq Ahmad Mir, Marcel Ausloos
|
2512.22118
|
ProEdit: Inversion-based Editing From Prompts Done Right
|
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the sampling process to maintain editing consistency. However, this sampling strategy overly relies on source information, which negatively affects the edits in the target image (e.g., failing to change the subject's atributes like pose, number, or color as instructed). In this work, we propose ProEdit to address this issue both in the attention and the latent aspects. In the attention aspect, we introduce KV-mix, which mixes KV features of the source and the target in the edited region, mitigating the influence of the source image on the editing region while maintaining background consistency. In the latent aspect, we propose Latents-Shift, which perturbs the edited region of the source latent, eliminating the influence of the inverted latent on the sampling. Extensive experiments on several image and video editing benchmarks demonstrate that our method achieves SOTA performance. In addition, our design is plug-and-play, which can be seamlessly integrated into existing inversion and editing methods, such as RF-Solver, FireFlow and UniEdit.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Zhi Ouyang, Dian Zheng, Xiao-Ming Wu, Jian-Jian Jiang, Kun-Yu Lin, Jingke Meng, Wei-Shi Zheng
|
2512.21886
|
Online Inertia Parameter Estimation for Unknown Objects Grasped by a Manipulator Towards Space Applications
|
Knowing the inertia parameters of a grasped object is crucial for dynamics-aware manipulation, especially in space robotics with free-floating bases. This work addresses the problem of estimating the inertia parameters of an unknown target object during manipulation. We apply and extend an existing online identification method by incorporating momentum conservation, enabling its use for the floating-base robots. The proposed method is validated through numerical simulations, and the estimated parameters are compared with ground-truth values. Results demonstrate accurate identification in the scenarios, highlighting the method's applicability to on-orbit servicing and other space missions.
| 2025-12-26
| 2025-12-29
|
[
"cs.RO"
] |
Akiyoshi Uchida, Antonine Richard, Kentaro Uno, Miguel Olivares-Mendez, Kazuya Yoshida
|
2406.02978
|
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
|
Self-supervised learning (SSL), which aims to learn meaningful prior representations from unlabeled data, has been proven effective for skeleton-based action understanding. Different from the image domain, skeleton data possesses sparser spatial structures and diverse representation forms, with the absence of background clues and the additional temporal dimension, presenting new challenges for spatial-temporal motion pretext task design. Recently, many endeavors have been made for skeleton-based SSL, achieving remarkable progress. However, a systematic and thorough review is still lacking. In this paper, we conduct, for the first time, a comprehensive survey on self-supervised skeleton-based action representation learning. Following the taxonomy of context-based, generative learning, and contrastive learning approaches, we make a thorough review and benchmark of existing works and shed light on the future possible directions. Remarkably, our investigation demonstrates that most SSL works rely on the single paradigm, learning representations of a single level, and are evaluated on the action recognition task solely, which leaves the generalization power of skeleton SSL models under-explored. To this end, a novel and effective SSL method for skeleton is further proposed, which integrates versatile representation learning objectives of different granularity, substantially boosting the generalization capacity for multiple skeleton downstream tasks. Extensive experiments under three large-scale datasets demonstrate our method achieves superior generalization performance on various downstream tasks, including recognition, retrieval, detection, and few-shot learning.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
Jiahang Zhang, Lilang Lin, Shuai Yang, Jiaying Liu
|
2512.23749
|
Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents
|
Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more effectively. \vspace*{6pt}
Contribution: In this paper, we present the Coordinate Matrix Machine (CM$^2$). This purpose-built small model augments human intelligence by learning document structures and using this information to classify documents. While modern "Red AI" trends rely on massive pre-training and energy-intensive GPU infrastructure, CM$^2$ is designed as a Green AI solution. It achieves human-level concept learning by identifying only the structural "important features" a human would consider, allowing it to classify very similar documents using only one sample per class.
Advantage: Our algorithm outperforms traditional vectorizers and complex deep learning models that require larger datasets and significant compute. By focusing on structural coordinates rather than exhaustive semantic vectors, CM$^2$ offers: 1. High accuracy with minimal data (one-shot learning) 2. Geometric and structural intelligence 3. Green AI and environmental sustainability 4. Optimized for CPU-only environments 5. Inherent explainability (glass-box model) 6. Faster computation and low latency 7. Robustness against unbalanced classes 8. Economic viability 9. Generic, expandable, and extendable
| 2025-12-26
| 2026-01-01
|
[
"cs.LG",
"cs.AI"
] |
Amin Sadri, M Maruf Hossain
|
2512.21923
|
Determining Blockchain Transaction Timing and Fee with Observable Mempools
|
Transaction fee plays an important role in determining the priority of transaction processing in public blockchain systems. Owing to the observability of unconfirmed transactions, a strategic user can postpone his transaction broadcasting time and set a fee as low as possible by prying into his mempool that stores them. However, the stochastic mining interval may cause the delayed transaction to miss the next valid block. Meanwhile, a new feature (i.e. fee bumping) emerges that allows each user to increase his transaction fee before confirmation, making the fee setting more challenging. In this paper, we investigate a novel transaction policy from the perspective of a single strategic user that determines the broadcasting time and the transaction fee simultaneously. Two representative scenarios are considered, in which a number of coexisting ordinary users are mempool-oblivious that set their fees according to certain distribution, and are semi-strategic that check their mempools at a Poisson rate and update their fees. In the former, we compute the optimal broadcasting time and transaction fee that adapts to the arbitrary distribution of mining interval. When the block interval is exponentially distributed in Bitcoin-like PoW systems, the strategic user needs to broadcast his transaction immediately after its creation. And when the block interval is fixed in Ethereum-like PoS systems, he finds it profitable to wait until the last moment before block generation. In the latter, we formulate a continuous-time Markov chain to characterize the dynamics of mempool states, and derive the optimal fee adjusting frequency of the strategic user when the block interval is exponentially distributed. In both theory and simulations, we show that this strategic user should immediately increase his fee whenever it falls behind the minimum fee of being included.
| 2025-12-26
| 2025-12-29
|
[
"cs.GT"
] |
Qianlan Bai, Yuedong Xu, Zhijian Zhou, Xin Wang
|
2512.22095
|
Decay of Mass of the Solution to the Cauchy Problem of the p-Laplacian with Absorption on Infinite Graphs
|
We consider the Cauchy problem for the nonstationary discrete p-Laplacian with inhomogeneous density \r{ho}(x) on an infinite graph which supports the Sobolev inequality. For nonnegative solutions when p > 2, we prove the precise rate of stabilization in time, provided \r{ho}(x) is a non-power function. When p > 2 and \r{ho}(x) goes to zero fast enough, we prove the universal bound. Our technique relies on suitable energy inequalities and a new embedding result.
| 2025-12-26
| 2025-12-29
|
[
"math.AP"
] |
Alan A. Tedeev
|
2504.16956
|
Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity
|
Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but its complexity, which is marked by high dimensionality, sparsity, and batch effects, which poses major computational challenges. Transformer-based models have made significant advances in this domain but are often limited by their quadratic complexity and suboptimal handling of long-range dependencies. In this work, we introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling. Leveraging the Bi-Mamba architecture, GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines. The model is pretrained on nearly 30 million cells and incorporates biologically informed objectives, including pathway-aware contrastive loss and rank-based gene encoding. We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness. These results position GeneMamba as a practical and powerful alternative to transformer-based methods, advancing the development of biologically grounded, scalable tools for large-scale single-cell data analysis.
| 2025-12-26
| 2025-12-29
|
[
"cs.CL",
"cs.LG",
"q-bio.GN"
] |
Cong Qi, Hanzhang Fang, Tianxing Hu, Siqi Jiang, Wei Zhi
|
2512.21872
|
Compact Ca II K Brightenings Precede Solar Flares: A Dunn Solar Telescope Pilot Study
|
We present a uniform analysis of compact Ca II K (3934 Ã
) brightenings that occur near flare kernels and assess their value as short-lead indicators of solar flare onset. Using high-cadence imaging from the Rapid Oscillations in the Solar Atmosphere (ROSA) instrument at the Dunn Solar Telescope (DST), we examine eight flare sequences (seven C-class and one B-class) obtained between 2021 and 2025. Fixed, detector-coordinate regions of interest are used to generate mean-intensity light curves, which are detrended and smoothed to isolate impulsive brightenings. In every event, a compact Ca II K brightening is detected within or adjacent to the flaring region that peaks 10--45 min before the primary kernel and the corresponding rise in GOES 1--8 Ã
flux. The measured temporal offsets scale with the deprojected separation between the brightening and flare kernels, implying an apparent propagation speed of $\sim$30--35 km s$^{-1}$ that is consistent with chromospheric reconnection. Complementary Spectropolarimeter for Infrared and Optical Regions (SPINOR) spectropolarimetry for one event shows topological reconfiguration from closed to open or extended connectivity, supporting a reconnection-driven origin. These results demonstrate that compact Ca II K brightenings are reproducible, physically meaningful precursors to flare onset. Their simplicity and cadence make them attractive chromospheric indicators, and future work will evaluate their predictive skill alongside established UV/EUV and magnetic diagnostics.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.SR"
] |
Aman Priyadarshi M. Kumar, Juie Shetye, Sean G. Sellers, Damian J. Christian
|
2512.04587
|
Supramolecular approach-based intermolecular interaction energy calculations using quantum phase estimation algorithm
|
Accurate computation of non-covalent, intermolecular interaction energies is important to understand various chemical phenomena, and quantum computers are anticipated to accelerate it. Although the state-of-the-art quantum computers are still noisy and intermediate-scale ones, development of theoretical frameworks those are expected to work on a fault-tolerant quantum computer is an urgent issue. In this work, we explore resource-efficient implementation of the quantum phase estimation-based complete active space configuration interaction (QPE-CASCI) calculations, with the aid of the second-order Møller--Plesset perturbation theory (MP2)-based active space selection with Boys localized orbitals. We performed numerical simulations of QPE for the supramolecular approach-based intermolecular interaction energy calculations of the hydrogen-bonded water dimer, using 6 system and 6 ancilla qubits. With the aid of algorithmic error mitigation, the QPE-CASCI simulations achieved interaction energy predictions with an error of 0.02 kcal mol$^{-1}$ relative to the CASCI result, demonstrating the accuracy and efficiency of the proposed methodology. Preliminary results on quantum circuit compression for QPE are also presented to reduce the number of two-qubit gates and depth.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"physics.chem-ph"
] |
Yuhei Tachi, Akihiko Arakawa, Taisei Osawa, Masayoshi Terabe, Kenji Sugisaki
|
2512.22348
|
Reddit Deplatforming and Toxicity Dynamics on Generalist Voat Communities
|
Deplatforming, the permanent banning of entire communities, is a primary tool for content moderation on mainstream platforms. While prior research examines effects on banned communities or source platform health, the impact on alternative platforms that absorb displaced users remains understudied. We analyze four major Reddit ban waves (2015--2020) and their effects on generalist communities on Voat, asking how post-ban arrivals reshape community structure and through what mechanisms transformation occurs. Combining network analysis, toxicity detection, and dynamic reputation modeling, we identify two distinct regimes of migration impact: (1) Hostile Takeover (2015--2018), where post-ban arrival cohorts formed parallel social structures that bypassed existing community cores through sheer volume, and (2) Toxic Equilibrium (2018--2020), where the flattening of existing user hierarchy enabled newcomers to integrate into the now-dominant toxic community. Crucially, community transformation occurred through peripheral dynamics rather than hub capture: fewer than 5% of newcomers achieved central positions in most months, yet toxicity doubled. Migration structure also shaped outcomes: loosely organized communities dispersed into generalist spaces, while ideologically cohesive groups concentrated in dedicated enclaves. These findings suggest that receiving platforms face a narrow intervention window during the hostile takeover phase, after which toxic norms become self-sustaining.
| 2025-12-26
| 2025-12-30
|
[
"cs.SI",
"cs.CY",
"physics.soc-ph"
] |
Aleksandar TomaÅ¡eviÄ, Ana VraniÄ, Aleksandra AloriÄ, Marija MitroviÄ Dankulov
|
2512.22044
|
Inferring Eccentricity of Binary Black Holes from Spin-Orbit Misalignment
|
Orbital eccentricity remains one of the least accessible parameters in observations of binary black hole (BBH) systems, largely erased by gravitational radiation long before detection. We introduce a new method to recover this lost parameter by using a more accessible and routinely measurable quantity: spin-orbit misalignment. In isolated binary evolution, a natal kick from the second supernova both tilts the orbital plane and injects orbital eccentricity, forging a direct and quantifiable connection between spin-tilt and post-supernova eccentricity. By measuring this spin-tilt using gravitational waves, we can not only constrain the natal kick, but we can also reconstruct the binary's formation eccentricity. We apply this method to GW190412 and GW241011, assuming an isolated formation channel, and show how their eccentricity at formation can be constrained even in the absence of direct eccentricity measurements. As more advanced detectors come online, improved signal-to-noise ratios will tighten spin-tilt constraints, allowing more precise and reliable estimates of BBH formation eccentricity. Combining this method with multiband observations from LISA and next-generation (XG) detectors will allow us to recover not only eccentricity but also the binary's orbital separation and redshift at formation, offering a clearer picture of the birth environments of BBH systems and processes that drive their merger.
| 2025-12-26
| 2025-12-29
|
[
"astro-ph.HE",
"gr-qc"
] |
Vishal Baibhav
|
2601.00835
|
On the Diophantine problem related to power circuits
|
Myasnikov, Ushakov and Won introduced power circuits in 2012 to construct a polynomial algorithm for the word problem in the Baumslag group, which has a non-elementary Dehn function. Power circuits are circuits supporting addition and operation $(x,y) = x \cdot 2^y$ for integer numbers. Also they posed a question about decidability of the Diophantine problem over the structure $\langle \mathbb{N}_{>0}; +, x \cdot 2^y, \leq, 1 \rangle$, which is closely related to power circuits. In this paper we prove undecidability of the Diophantine problem over this structure.
| 2025-12-26
| 2026-01-06
|
[
"math.LO",
"math.GR",
"math.NT",
"math.RA"
] |
Alexander Rybalov
|
2512.22010
|
LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration
|
Unmanned aerial vehicles (UAVs) are crucial tools for post-disaster search and rescue, facing challenges such as high information density, rapid changes in viewpoint, and dynamic structures, especially in long-horizon navigation. However, current UAV vision-and-language navigation(VLN) methods struggle to model long-horizon spatiotemporal context in complex environments, resulting in inaccurate semantic alignment and unstable path planning. To this end, we propose LongFly, a spatiotemporal context modeling framework for long-horizon UAV VLN. LongFly proposes a history-aware spatiotemporal modeling strategy that transforms fragmented and redundant historical data into structured, compact, and expressive representations. First, we propose the slot-based historical image compression module, which dynamically distills multi-view historical observations into fixed-length contextual representations. Then, the spatiotemporal trajectory encoding module is introduced to capture the temporal dynamics and spatial structure of UAV trajectories. Finally, to integrate existing spatiotemporal context with current observations, we design the prompt-guided multimodal integration module to support time-based reasoning and robust waypoint prediction. Experimental results demonstrate that LongFly outperforms state-of-the-art UAV VLN baselines by 7.89\% in success rate and 6.33\% in success weighted by path length, consistently across both seen and unseen environments.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV",
"cs.AI"
] |
Wen Jiang, Li Wang, Kangyao Huang, Wei Fan, Jinyuan Liu, Shaoyu Liu, Hongwei Duan, Bin Xu, Xiangyang Ji
|
2509.17792
|
Degradation-Aware All-in-One Image Restoration via Latent Prior Encoding
|
Real-world images often suffer from spatially diverse degradations such as haze, rain, snow, and low-light, significantly impacting visual quality and downstream vision tasks. Existing all-in-one restoration (AIR) approaches either depend on external text prompts or embed hand-crafted architectural priors (e.g., frequency heuristics); both impose discrete, brittle assumptions that weaken generalization to unseen or mixed degradations. To address this limitation, we propose to reframe AIR as learned latent prior inference, where degradation-aware representations are automatically inferred from the input without explicit task cues. Based on latent priors, we formulate AIR as a structured reasoning paradigm: (1) which features to route (adaptive feature selection), (2) where to restore (spatial localization), and (3) what to restore (degradation semantics). We design a lightweight decoding module that efficiently leverages these latent encoded cues for spatially-adaptive restoration. Extensive experiments across six common degradation tasks, five compound settings, and previously unseen degradations demonstrate that our method outperforms state-of-the-art (SOTA) approaches, achieving an average PSNR improvement of 1.68 dB while being three times more efficient.
| 2025-12-26
| 2025-12-29
|
[
"cs.CV"
] |
S M A Sharif, Abdur Rehman, Fayaz Ali Dharejo, Radu Timofte, Rizwan Ali Naqvi
|
2505.13556
|
Learning collision risk proactively from naturalistic driving data at scale
|
Accurately and proactively alerting drivers or automated systems to emerging collisions is crucial for road safety, particularly in highly interactive and complex urban environments. Existing methods either require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are tailored to limited scenarios. Here we present the Generalised Surrogate Safety Measure (GSSM), a data-driven approach that learns collision risk from naturalistic driving without the need for crash or risk labels. Trained over multiple datasets and evaluated on 2,591 real-world crashes and near-crashes, a basic GSSM using only instantaneous motion kinematics achieves an area under the precision-recall curve of 0.9, and secures a median time advance of 2.6 seconds to prevent potential collisions. Incorporating additional interaction patterns and contextual factors provides further performance gains. Across interaction scenarios such as rear-end, merging, and turning, GSSM consistently outperforms existing baselines in accuracy and timeliness. These results establish GSSM as a scalable, context-aware, and generalisable foundation to identify risky interactions before they become unavoidable, supporting proactive safety in autonomous driving systems and traffic incident management. Code and experiment data are openly accessible at https://github.com/Yiru-Jiao/GSSM.
| 2025-12-26
| 2025-12-29
|
[
"cs.RO",
"cs.LG"
] |
Yiru Jiao, Simeon C. Calvert, Sander van Cranenburgh, Hans van Lint
|
2511.11342
|
Lorentz Transformation in Quantum Mechanics
|
The compatibility of special relativity and Quantum Mechanics has been questioned by several authors. The origin of this tension can be traced back mainly to the introduction of the measurement processes and the corresponding wave function reduction, which play a crucial role in Quantum Mechanics. We approach this problem with the help of a recent proposal for a model of Quantum Mechanics, where the measurement is explicitly described as a specific stochastic process. This implements ordinary Quantum Mechanics, where measurement and reduction are treated as phenomenological events of unknown origin without any physical justification. To state clearly the question in general, we first discuss and establish the effect of a Lorentz transformation on a generic wave function in space-time. Alongside the analysis we formulate the relativistic version of the model. We then consider few thought experiments in order to analyze to what extent Quantum Mechanics follows relativistic invariance and find the specific critical points where non invariance possibly occurs. The analysis can shade light to the interpretation of the existing experimental observations.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph"
] |
Marcello Baldo
|
2512.22099
|
Effect of Population Imbalance on Vortex Mass in Superfluid Fermi Gases
|
One of the fundamental parameters associated with quantized vortices in superfluids is the vortex mass, which is the inertia of a vortex. As of yet, this mass has not been observed in a superfluid. However, ultracold Fermi gases provide a promising platform in which recently much experimental progress was made, offering tunability of the interaction as well as control on the single-vortex level. Not only can the scattering length be freely tuned, allowing exploration of the BEC-BCS crossover, but also an imbalance between different pseudospin states can be introduced. We study the effect of introducing this imbalance on the vortex mass, using a method based on an effective field theory for superfluid Fermi gases. We find that it is crucial to consider the imbalance in conjunction with nonzero temperatures; at some temperatures, the vortex mass is significantly enhanced while at others, the vortex mass is diminished. This pronounced temperature dependence highlights the need for careful tuning of experimental conditions and identifies favorable parameter regimes in which the vortex mass is likely to be observed.
| 2025-12-26
| 2025-12-29
|
[
"cond-mat.quant-gas"
] |
Lucas Levrouw, Hiromitsu Takeuchi, Jacques Tempere
|
2512.21938
|
Optimal Convergence Estimate of the Limit from Inverse Power Potential to Hard Sphere Boltzmann Equation
|
The inverse power potential $U(r)=r^{-1/s}, 0<s<1$, generates the Boltzmann kernel $B^{s}=|v-v_*|^{1-4s} b_s(θ)$ with an angular singularity as $θ\to 0$. Jang-Kepka-Nota-Velázquez (2023) proved the limit $B^{s}\to \frac14|v-v_*|$ as $s\to 0$, as well as weak convergence of solutions based on this kernel convergence. In this work we establish the following sharp quantitative estimate: $$ |b_s(θ)-\tfrac14| \le C\, s\,θ^{-2-2s}. $$ In particular, this sharp estimate yields the optimal $O(s)$ convergence rate for solutions of the homogeneous Boltzmann equation with large initial data in suitable Sobolev spaces; i.e., for any $t\in[0,T]$, we have $$f^s(t)=f^0(t)+O(s),$$ quantified by the $L^1_k$ norm for $k\ge 2.$
| 2025-12-26
| 2025-12-29
|
[
"math.AP"
] |
Zheng-Nan Hu, Jin Woo Jang, Zheng-An Yao, Yu-Long Zhou
|
2512.22053
|
Local identifiability of a parameter function in a system of differential equations
|
In this paper, we consider the problem of local parameter identifiability of a parameter function in a system of ordinary differential equations. Previously, in this problem, the case where the dimensions of a parameter and a solution of a system coincide was considered, and a specific class of systems was identified, for which sufficient conditions for local parametric identifiability were obtained. We extend these results and consider a wider class of systems of differential equations, as well as the case where the dimension of a parameter is less than or equal to the dimension of a solution of a system. In both cases, sufficient conditions are derived for the local identifiability of a parameter function based on observations of a solution at a finite number of points.
| 2025-12-26
| 2025-12-29
|
[
"math.DS"
] |
V. S. Shalgin
|
2512.22071
|
Semileptonic B-decays at Belle and Belle II
|
The paper summarizes recent results from the Belle and Belle II collaborations on semileptonic $B$ decays measurements including inclusive and exclusive determination of Cabibbo-Kobayashi-Maskawa matrix elements $|V_{cb}|$ and $|V_{ub}|$ and lepton flavor universality tests studies. The results are based on the full Belle data and 361 fb$^{-1}$ Belle II data samples collected in 2018 - 2022.
| 2025-12-26
| 2025-12-29
|
[
"hep-ex"
] |
Nikolai Peters
|
2512.22116
|
General Construction of Quantum Error-Correcting Codes from Multiple Classical Codes
|
The hypergraph product (HGP) construction of quantum error-correcting codes (QECC) offers a general and explicit method for building a QECC from two classical codes, thereby paving the way for the discovery of good quantum low-density parity-check codes. In this letter, we propose a general and explicit construction recipe for QECCs from a total of D classical codes for arbitrary D. Following this recipe guarantees the obtainment of a QECC within the stabilizer formalism and nearly exhausts all possible constructions. As examples, we demonstrate that our construction recovers the HGP construction when D = 2 and leads to four distinct types of constructions for D = 3, including a previously studied case as one of them. When the input classical codes are repetition codes, our D = 3 constructions unify various three-dimensional lattice models into a single framework, encompassing the three-dimensional toric code model, a fracton model, and two other intriguing models not previously investigated. Among these, two types of constructions exhibit a trade-off between code distance and code dimension for a fixed number of qubits by adjusting the lengths of the different classical codes, and the optimal choice can simultaneously achieve relatively large values for both code distance and code dimension. Our general construction protocol provides another perspective for enriching the structure of QECCs and enables the exploration of richer possibilities for good codes.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph",
"cond-mat.quant-gas",
"cond-mat.str-el"
] |
Yue Wu, Meng-Yuan Li, Chengshu Li, Hui Zhai
|
2403.00336
|
Never-Ending Behavior-Cloning Agent for Robotic Manipulation
|
Relying on multi-modal observations, embodied robots (e.g., humanoid robots) could perform multiple robotic manipulation tasks in unstructured real-world environments. However, most language-conditioned behavior-cloning agents in robots still face existing long-standing challenges, i.e., 3D scene representation and human-level task learning, when adapting into a series of new tasks in practical scenarios. We here investigate these above challenges with NBAgent in embodied robots, a pioneering language-conditioned Never-ending Behavior-cloning Agent, which can continually learn observation knowledge of novel 3D scene semantics and robot manipulation skills from skill-shared and skill-specific attributes, respectively. Specifically, we propose a skill-shared semantic rendering module and a skill-shared representation distillation module to effectively learn 3D scene semantics from skill-shared attribute, further tackling 3D scene representation overlooking. Meanwhile, we establish a skill-specific evolving planner to perform manipulation knowledge decoupling, which can continually embed novel skill-specific knowledge like human from latent and low-rank space. Finally, we design a never-ending embodied robot manipulation benchmark, and expensive experiments demonstrate the significant performance of our method.
| 2025-12-26
| 2025-12-30
|
[
"cs.RO",
"cs.AI"
] |
Wenqi Liang, Gan Sun, Yao He, Yu Ren, Jiahua Dong, Yang Cong
|
2510.13626
|
LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
|
Visual-Language-Action (VLA) models report impressive success rates on robotic manipulation benchmarks, yet these results may mask fundamental weaknesses in robustness. We perform a systematic vulnerability analysis by introducing controlled perturbations across seven dimensions: objects layout, camera viewpoints, robot initial states, language instructions, light conditions, background textures and sensor noise. We comprehensively analyzed multiple state-of-the-art models and revealed consistent brittleness beneath apparent competence. Our analysis exposes critical weaknesses: models exhibit extreme sensitivity to perturbation factors, including camera viewpoints and robot initial states, with performance dropping from 95% to below 30% under modest perturbations. Surprisingly, models are largely insensitive to language variations, with further experiments revealing that models tend to ignore language instructions completely. Our findings challenge the assumption that high benchmark scores equate to true competency and highlight the need for evaluation practices that assess reliability under realistic variation.
| 2025-12-26
| 2025-12-29
|
[
"cs.RO",
"cs.CL",
"cs.CV"
] |
Senyu Fei, Siyin Wang, Junhao Shi, Zihao Dai, Jikun Cai, Pengfang Qian, Li Ji, Xinzhe He, Shiduo Zhang, Zhaoye Fei, Jinlan Fu, Jingjing Gong, Xipeng Qiu
|
2512.22110
|
Thermalization within a Stark manifold through Rydberg atom interactions
|
One explanation of the thermalization of an isolated quantum system is the eigenstate thermalization hypothesis, which posits that all energy eigenstates are thermal. Based on this idea, we use dynamical typicality to predict the thermal state of ultracold Rb atoms exchanging energy via long-range dipole-dipole interactions. In a magneto-optical trap, we excite the atoms to the center of a manifold of nearly harmonically spaced clusters of Stark energy levels and then allow them to equilibrate. Comparing the equilibrium state to our thermal prediction across a range of densities, we find that the atoms generally fail to thermalize, though they approach the thermal state at the highest tested density.
| 2025-12-26
| 2025-12-29
|
[
"quant-ph"
] |
Sarah E. Spielman, Sage M. Thomas, Maja Teofilovska, Annick C van Blerkom, Juniper J. Bauroth-Sherman, Nicolaus A. Chlanda, Hannah S. Conley, Philip A. Conte, Aidan D. Kirk, Thomas J. Carroll, Michael W. Noel
|
2512.22602
|
PTalker: Personalized Speech-Driven 3D Talking Head Animation via Style Disentanglement and Modality Alignment
|
Speech-driven 3D talking head generation aims to produce lifelike facial animations precisely synchronized with speech. While considerable progress has been made in achieving high lip-synchronization accuracy, existing methods largely overlook the intricate nuances of individual speaking styles, which limits personalization and realism. In this work, we present a novel framework for personalized 3D talking head animation, namely "PTalker". This framework preserves speaking style through style disentanglement from audio and facial motion sequences and enhances lip-synchronization accuracy through a three-level alignment mechanism between audio and mesh modalities. Specifically, to effectively disentangle style and content, we design disentanglement constraints that encode driven audio and motion sequences into distinct style and content spaces to enhance speaking style representation. To improve lip-synchronization accuracy, we adopt a modality alignment mechanism incorporating three aspects: spatial alignment using Graph Attention Networks to capture vertex connectivity in the 3D mesh structure, temporal alignment using cross-attention to capture and synchronize temporal dependencies, and feature alignment by top-k bidirectional contrastive losses and KL divergence constraints to ensure consistency between speech and mesh modalities. Extensive qualitative and quantitative experiments on public datasets demonstrate that PTalker effectively generates realistic, stylized 3D talking heads that accurately match identity-specific speaking styles, outperforming state-of-the-art methods. The source code and supplementary videos are available at: PTalker.
| 2025-12-27
| 2025-12-30
|
[
"cs.CV"
] |
Bin Wang, Yang Xu, Huan Zhao, Hao Zhang, Zixing Zhang
|
2511.06640
|
Starobinsky Inflation and the Latest CMB Data: A Subtle Tension?
|
We analyze the Starobinsky inflation model and the impact of curvature corrections, particularly a cubic $R^3$ term, to assess their behavior in light of the latest observational results from the Atacama Cosmology Telescope (ACT). With the recent sixth data release (DR6), the scalar spectral index was measured to be $n_s=0.9743 \pm 0.0034$, which appears to exclude the pure Starobinsky model at approximately the $2Ï$ level. In this paper, we implement the Starobinsky inflationary potential directly into the CLASS code, without relying on the slow-roll approximation, and we constrain the number of e-folds of inflation $N_k$ using a theoretically motivated range derived from reheating considerations and standard couplings between matter fields and gravity. We show that it is still possible to identify a significant region of parameter space where the Starobinsky model remains highly consistent with the latest observational data. While the pure Starobinsky model remains a compelling candidate for cosmic inflation, we explore how including a cubic $R^3$ term can shift its predictions to better align with the Planck and ACT measurements.
| 2025-12-27
| 2025-12-30
|
[
"astro-ph.CO",
"gr-qc"
] |
J. Bezerra-Sobrinho, L. G. Medeiros
|
2309.16824
|
The fork and its role in unification of closure algebras
|
We consider the two-pronged fork frame $F$ and the variety $\mathbf{Eq}(B_F)$ generated by its dual closure algebra $B_F$. We describe the finite projective algebras in $\mathbf{Eq}(B_F)$ and give a purely semantic proof that unification in $\mathbf{Eq}(B_F)$ is finitary and not unitary.
| 2025-12-27
| 2025-12-31
|
[
"cs.LO",
"math.RA"
] |
Ivo Düntsch, Wojciech Dzik
|
2512.22538
|
Isolating Compiler Faults via Multiple Pairs of Adversarial Compilation Configurations
|
Compilers are fundamental to modern software development, making the effective identification and resolution of compiler faults essential. However, localizing these faults to specific source files remains highly challenging due to the complexity and scale of modern compiler infrastructures. In this study, we propose MultiConf, a novel approach that automatically isolates compiler faults by constructing multiple pairs of adversarial compilation configurations. Each adversarial compilation configuration pair consists of a failing configuration and its corresponding passing configuration, which differ in only a small number of fine-grained options. MultiConf generates failing configurations through a lightweight construction process and derives the corresponding passing configurations by selectively disabling bug-related fine-grained options. We then employ a Spectrum-Based Fault Localization (SBFL) formula to rank the suspiciousness of compiler source files. Each adversarial configuration pair independently produces a ranking, which is subsequently aggregated using a weighted voting scheme to derive a final suspiciousness ranking, enabling more accurate and robust fault localization. We evaluate MultiConf on a benchmark of 60 real-world GCC compiler bugs. The results demonstrate that MultiConf significantly outperforms existing compiler fault localization techniques in both effectiveness and efficiency. In particular, MultiConf successfully localizes 27 out of 60 bugs at the Top-1 file level, representing improvements of 35.0% and 28.6% over the two state-of-the-art approaches, Odfl(20) and Basic(21), respectively.
| 2025-12-27
| 2025-12-30
|
[
"cs.SE"
] |
Qingyang Li, Yibiao Yang, Maolin Sun, Jiangchang Wu, Qingkai Shi, Yuming Zhou
|
2512.18850
|
InDRiVE: Reward-Free World-Model Pretraining for Autonomous Driving via Latent Disagreement
|
Model-based reinforcement learning (MBRL) can reduce interaction cost for autonomous driving by learning a predictive world model, but it typically still depends on task-specific rewards that are difficult to design and often brittle under distribution shift. This paper presents InDRiVE, a DreamerV3-style MBRL agent that performs reward-free pretraining in CARLA using only intrinsic motivation derived from latent ensemble disagreement. Disagreement acts as a proxy for epistemic uncertainty and drives the agent toward under-explored driving situations, while an imagination-based actor-critic learns a planner-free exploration policy directly from the learned world model. After intrinsic pretraining, we evaluate zero-shot transfer by freezing all parameters and deploying the pretrained exploration policy in unseen towns and routes. We then study few-shot adaptation by training a task policy with limited extrinsic feedback for downstream objectives (lane following and collision avoidance). Experiments in CARLA across towns, routes, and traffic densities show that disagreement-based pretraining yields stronger zero-shot robustness and robust few-shot collision avoidance under town shift and matched interaction budgets, supporting the use of intrinsic disagreement as a practical reward-free pretraining signal for reusable driving world models.
| 2025-12-27
| 2025-12-30
|
[
"cs.RO"
] |
Feeza Khan Khanzada, Jaerock Kwon
|
2512.22575
|
ParaMaP: Parallel Mapping and Collision-free Motion Planning for Reactive Robot Manipulation
|
Real-time and collision-free motion planning remains challenging for robotic manipulation in unknown environments due to continuous perception updates and the need for frequent online replanning. To address these challenges, we propose a parallel mapping and motion planning framework that tightly integrates Euclidean Distance Transform (EDT)-based environment representation with a sampling-based model predictive control (SMPC) planner. On the mapping side, a dense distance-field-based representation is constructed using a GPU-based EDT and augmented with a robot-masked update mechanism to prevent false self-collision detections during online perception. On the planning side, motion generation is formulated as a stochastic optimization problem with a unified objective function and efficiently solved by evaluating large batches of candidate rollouts in parallel within a SMPC framework, in which a geometrically consistent pose tracking metric defined on SE(3) is incorporated to ensure fast and accurate convergence to the target pose. The entire mapping and planning pipeline is implemented on the GPU to support high-frequency replanning. The effectiveness of the proposed framework is validated through extensive simulations and real-world experiments on a 7-DoF robotic manipulator. More details are available at: https://zxw610.github.io/ParaMaP.
| 2025-12-27
| 2025-12-30
|
[
"cs.RO"
] |
Xuewei Zhang, Bailing Tian, Kai Zheng, Yulin Hui, Junjie Lu, Zhiyu Li
|
2512.22654
|
Topological Mod(A)Max AdS black holes
|
In this work, we construct new classes of topological black hole solutions in anti-de Sitter (AdS) spacetime using a novel model of nonlinear electrodynamics called Modification Maxwell (ModMax) and Modification phantom or Modification anti-Maxwell (ModAMax). We then evaluate the thermodynamic quantities and verify the first law of thermodynamics. Our study examines how the parameters of the ModMax and ModAMax fields, as well as the topological constant, affect the black hole solutions, thermodynamic quantities, and local and global thermal stabilities. Furthermore, within the framework of extended phase space thermodynamics, we analyze the Joule-Thomson expansion process and determine the inversion curves. This analysis reveals that the ModMax and ModAMax parameters significantly alter the cooling and heating behavior of these AdS black holes, depending on their topology. Finally, by treating these topological Mod(A)Max AdS black holes as heat engines, we assess their efficiencies, demonstrating that the parameters of nonlinear electrodynamics and horizon topology play crucial roles in enhancing or suppressing the system's thermodynamic performance.
| 2025-12-27
| 2025-12-30
|
[
"gr-qc",
"hep-th"
] |
B. Eslam Panah, B. Hamil, Manuel E. Rodrigues
|
2512.22433
|
Scalar-hairy AdS Black Hole in the Einstein-Maxwell-Scalar Theory: first-order phase transition with a critical point
|
In asymptotically anti-de Sitter (AdS) spacetime, we consider a real massiver scalar field in the Einstein-Maxwell-scalar (EMS) model and examine both scalar-hairy black hole solutions induced by the nonminimal coupling to the Maxwell field and tachyonic-hairy solutions driven by the scalar potential. When the scalar potential vanishes, scalar-hairy black holes emerge with profiles and properties similar to those observed in flat spacetime. The presence of the scalar potential additionally induces tachyonic-hairy solutions, leading to the coexistence of these two distinct hairy phases in different regions of the parameter space. The phase diagram reveals a first-order phase transition line between the tachyonic-hairy and scalar-hairy phases, originating at a critical point in the extreme temperature and chemical potential regime. Our detailed analysis shows that this phase transition is directly associated with the self-overlap region of the scalar-hairy phase and its start point. Moreover, increasing the coupling strength $λ$ shifts the critical point to higher temperature and chemical potential.
| 2025-12-27
| 2025-12-30
|
[
"gr-qc"
] |
Hong Guo, Hang Liu, Yun Soo Myung
|
2510.25806
|
APThreatHunter: An automated planning-based threat hunting framework
|
Cyber attacks threaten economic interests, critical infrastructure, and public health and safety. To counter this, entities adopt cyber threat hunting, a proactive approach that involves formulating hypotheses and searching for attack patterns within organisational networks. Automating cyber threat hunting presents challenges, particularly in generating hypotheses, as it is a manually created and confirmed process, making it time-consuming. To address these challenges, we introduce APThreatHunter, an automated threat hunting solution that generates hypotheses with minimal human intervention, eliminating analyst bias and reducing time and cost. This is done by presenting possible risks based on the system's current state and a set of indicators to indicate whether any of the detected risks are happening or not. We evaluated APThreatHunter using real-world Android malware samples, and the results revealed the practicality of using automated planning for goal hypothesis generation in cyber threat hunting activities.
| 2025-12-27
| 2025-12-30
|
[
"cs.CR"
] |
Mustafa F. Abdelwahed, Ahmed Shafee, Joan Espasa
|
2512.22473
|
Gradient Dynamics of Attention: How Cross-Entropy Sculpts Bayesian Manifolds
|
Transformers empirically perform precise probabilistic reasoning in carefully constructed ``Bayesian wind tunnels'' and in large-scale language models, yet the mechanisms by which gradient-based learning creates the required internal geometry remain opaque. We provide a complete first-order analysis of how cross-entropy training reshapes attention scores and value vectors in a transformer attention head. Our core result is an \emph{advantage-based routing law} for attention scores, \[ \frac{\partial L}{\partial s_{ij}} = α_{ij}\bigl(b_{ij}-\mathbb{E}_{α_i}[b]\bigr), \qquad b_{ij} := u_i^\top v_j, \] coupled with a \emph{responsibility-weighted update} for values, \[ Îv_j = -η\sum_i α_{ij} u_i, \] where $u_i$ is the upstream gradient at position $i$ and $α_{ij}$ are attention weights. These equations induce a positive feedback loop in which routing and content specialize together: queries route more strongly to values that are above-average for their error signal, and those values are pulled toward the queries that use them. We show that this coupled specialization behaves like a two-timescale EM procedure: attention weights implement an E-step (soft responsibilities), while values implement an M-step (responsibility-weighted prototype updates), with queries and keys adjusting the hypothesis frame. Through controlled simulations, including a sticky Markov-chain task where we compare a closed-form EM-style update to standard SGD, we demonstrate that the same gradient dynamics that minimize cross-entropy also sculpt the low-dimensional manifolds identified in our companion work as implementing Bayesian inference. This yields a unified picture in which optimization (gradient flow) gives rise to geometry (Bayesian manifolds), which in turn supports function (in-context probabilistic reasoning).
| 2025-12-27
| 2025-12-30
|
[
"stat.ML",
"cs.AI",
"cs.LG"
] |
Naman Aggarwal, Siddhartha R. Dalal, Vishal Misra
|
2512.22648
|
Affine Symmetry and the Group-Theoretic Basis of the Unruh Effect
|
A massless scalar field in two spacetime dimensions splits into two independent sectors of left and right-moving modes on the light cone. At the quantum level, these two sectors carry a representation of the group of affine transformations of the real line, with translations corresponding to transformations generated by light-cone momenta and dilations given by light-cone Rindler momenta formed by a linear combination of generators of boosts and dilations. One-particle states for inertial observers are eigenvectors of translation generators belonging to irreducible representations of the affine group. Rindler one-particle states are related to eigenfunctions of the generator of dilations. We show that simple manipulations connecting these two representations involving the Mellin transform can be used to derive the thermal spectrum of Rindler particles observed by an accelerated observer. Beyond providing a representation-theoretic basis for vacuum thermal effects, our results suggest that analogous phenomena may arise in any quantum system admitting realizations of translation and dilation eigenstates.
| 2025-12-27
| 2025-12-30
|
[
"hep-th",
"gr-qc",
"quant-ph"
] |
M. Arzano, A. D'Alise, S. del Rosso, D. Frattulillo
|
2411.08372
|
Equitable list coloring of sparse graphs
|
A proper vertex coloring of a graph is equitable if the sizes of all color classes differ by at most $1$. For a list assignment $L$ of $k$ colors to each vertex of an $n$-vertex graph $G$, an equitable $L$-coloring of $G$ is a proper coloring of vertices of $G$ from their lists such that no color is used more than $\lceil n/k\rceil$ times. Call a graph equitably $k$-choosable if it has an equitable $L$-coloring for every $k$-list assignment $L$. A graph $G$ is $(a,b)$-sparse if for every $A\subseteq V(G)$, the number of edges in the subgraph $G[A]$ of $G$ induced by $A$ is at most $a|A|+b$.
Our first main result is that every $(\frac{7}{6},\frac{1}{3})$-sparse graph with minimum degree at least $2$ is equitably $3$-colorable and equitably $3$-choosable. This is sharp. Our second main result is that every $(\frac{5}{4},\frac{1}{2})$-sparse graph with minimum degree at least $2$ is equitably $4$-colorable and equitably $4$-choosable. This is also sharp.
One of the tools in the proof is the new notion of strongly equitable (SE) list coloring. This notion is both stronger and more natural than equitable list coloring; and our upper bounds are for SE list coloring.
| 2025-12-27
| 2025-12-30
|
[
"math.CO"
] |
H. A. Kierstead, Alexandr Kostochka, Zimu Xiang
|
2512.14598
|
Hybrid Machine-Learning Particle Identification for the ePIC Proximity-Focusing RICH
|
We present a machine-learning-based particle-identification study for the proximity-focusing Ring Imaging Cherenkov (pfRICH) detector of the ePIC experiment at the Electron-Ion Collider. Operating in the backward region ($-3.5 \lesssim η\lesssim -1.5$), the pfRICH is designed to achieve at least $3Ï$ separation among pions, kaons, and protons up to $7,\mathrm{GeV}/c$ for Semi-Inclusive Deep Inelastic Scattering measurements. Using a standalone Geant4 simulation of the pfRICH, we develop a hybrid machine-learning approach that combines convolutional neural-network-based feature extraction with gradient-boosted decision-tree classifiers. This method significantly enhances Cherenkov-ring pattern recognition and improves particle-separation performance, demonstrating the effectiveness of hybrid machine-learning techniques for next-generation Cherenkov detectors at the EIC.
| 2025-12-27
| 2025-12-30
|
[
"physics.ins-det",
"hep-ex"
] |
D. H. Dongwi, C. -J. Naïm, L. Rhode, A. Deshpande
|
2512.22686
|
Multistatic Radar Performance in the Presence of Distributed Wireless Synchronization
|
This paper proposes a multistatic radar (MSR) system utilizing a distributed wireless synchronization protocol. The wireless synchronization protocol uses a two-tone waveform exchange for frequency synchronization and a bi-directional waveform exchange for time synchronization, independent of GPS. A Bayesian Cramer-Rao lower bound (BCRLB) framework is developed to quantify the impact of synchronization offsets on joint delay and Doppler estimation, and consequently, on target localization and velocity estimation accuracy. Simulation results derived from the analytical expressions establish the extent to which the residual synchronization offsets degrade the MSR's performance. The performance of the synchronization links primarily depends on the synchronization-link channel and transmit parameters; optimizing these parameters enables the MSR configuration to surpass the monostatic performance and approach the ideal case. Furthermore, the simulated synchronization-link parameters suggest that practical implementation is feasible.
| 2025-12-27
| 2025-12-30
|
[
"eess.SP"
] |
Kumar Sai Bondada, Daniel J. Jakubisin, R. Michael Buehrer
|
2512.22443
|
Exploring the Vertical-Domain Reasoning Capabilities of Large Language Models
|
Large Language Models (LLMs) are reshaping learning paradigms, cognitive processes, and research methodologies across a wide range of domains. Integrating LLMs with professional fields and redefining the relationship between LLMs and domain-specific applications has become a critical challenge for promoting enterprise digital transformation and broader social development. To effectively integrate LLMs into the accounting domain, it is essential to understand their domain-specific reasoning capabilities. This study introduces the concept of vertical-domain accounting reasoning and establishes evaluation criteria by analyzing the training data characteristics of representative GLM-series models. These criteria provide a foundation for subsequent research on reasoning paradigms and offer benchmarks for improving accounting reasoning performance. Based on this framework, we evaluate several representative models, including GLM-6B, GLM-130B, GLM-4, and OpenAI GPT-4, on a set of accounting reasoning tasks. Experimental results show that different prompt engineering strategies lead to varying degrees of performance improvement across models, with GPT-4 achieving the strongest accounting reasoning capability. However, current LLMs still fall short of real-world application requirements. In particular, further optimization is needed for deployment in enterprise-level accounting scenarios to fully realize the potential value of LLMs in this domain.
| 2025-12-27
| 2025-12-30
|
[
"cs.CL"
] |
Jie Zhou, Xin Chen, Jie Zhang, Zhe Li
|
2512.22468
|
Quantum attomicroscopy: imaging quantum chemistry in action
|
How quantum electron and nuclei motions affect biomolecular chemical reactions remains a central challengeable question at the interface of quantum chemistry and biology. Ultrafast charge migration in deoxyribonucleic acid (DNA) has long been hypothesized to play a critical role in photochemistry, genome stability, and long-range biomolecular signaling, however, direct real-time observation of these electronic processes has remained elusive. Here, we present a theoretical investigation and propose the concept of future experimental measurements of laser-driven charge dynamics in the canonical DNA nucleobase pairs thymine_adenine and cytosine_guanine. Attosecond-resolved simulations employing high-level ab initio methods reveal base-dependent ionization mechanisms, directional charge migration pathways, and electronic coherences that govern sub-femtosecond redistribution of electron density across hydrogen-bonded nucleobase interfaces. Accordingly, we propose the concept of a quantum attosecond scanning electron microscope, termed the quantum attomicroscope (Q-attomicroscope), a capable of imaging photoinduced quantum chemistry reactions in attosecond temporal resolution and sub-nanometer spatial precision. As a proof of principle, we propose to image the charge migrations dynamics in DNA which we studied theoretically. Together, our preceptive bridges theory, instrumentation, and control, outlining a pathway toward laser mediated manipulation of DNA structure with implications for repair processes, chemical reactivity, and future personalized medicine.
| 2025-12-27
| 2025-12-30
|
[
"physics.chem-ph",
"physics.optics"
] |
Nikolay V. Golubev, Mohammed Th. Hassan
|
2512.22437
|
EmoCtrl: Controllable Emotional Image Content Generation
|
An image conveys meaning through both its visual content and emotional tone, jointly shaping human perception. We introduce Controllable Emotional Image Content Generation (C-EICG), which aims to generate images that remain faithful to a given content description while expressing a target emotion. Existing text-to-image models ensure content consistency but lack emotional awareness, whereas emotion-driven models generate affective results at the cost of content distortion. To address this gap, we propose EmoCtrl, supported by a dataset annotated with content, emotion, and affective prompts, bridging abstract emotions to visual cues. EmoCtrl incorporates textual and visual emotion enhancement modules that enrich affective expression via descriptive semantics and perceptual cues. The learned emotion tokens exhibit complementary effects, as demonstrated through ablations and visualizations. Quantatitive and qualatitive experiments demonstrate that EmoCtrl achieves faithful content and expressive emotion control, outperforming existing methods across multiple aspects. User studies confirm EmoCtrl's strong alignment with human preference. Moreover, EmoCtrl generalizes well to creative applications, further demonstrating the robustness and adaptability of the learned emotion tokens.
| 2025-12-27
| 2025-12-30
|
[
"cs.CV"
] |
Jingyuan Yang, Weibin Luo, Hui Huang
|
2509.14762
|
Thermoelectric power factors of defective scandium nitride nanostructures from first principles
|
The thermoelectric properties of scandium nitride are strongly influenced by structural and electronic factors arising from defects and impurities. Nevertheless, the mechanisms by which these microscopic features affect transport are not yet fully understood. Experiments show a large variability in the electronic transport properties, with a strong dependence on the experimental conditions, and attempts to improve thermoelectric efficiency often lead to conflicting effects. In this work, we employ the Landauer approach to analyze the effects of different kinds of structural defects and impurities on electronic transport in scandium nitride. This approach allows us to relate the transport mechanisms to the structural and electronic modifications introduced in the lattice, with atomistic resolution. In light of these new insights, we propose a rationale relating part of the experimental variability to its microscopic origin.
| 2025-12-27
| 2026-01-08
|
[
"cond-mat.mtrl-sci",
"cond-mat.mes-hall",
"physics.app-ph",
"physics.chem-ph",
"physics.comp-ph"
] |
Luigi Cigarini, Urszula Danuta Wdowik, Dominik Legut
|
2512.22541
|
Entanglement protection induced by mixed noise
|
Contrary to the conventional view that noise is detrimental, we show that mixed noise can protect entanglement in a two-atom-cavity system. Specifically, the leakage of the cavity and the stochastic atom-cavity couplings are modeled as two types of noises. From the analytical derivation of the dynamical equations, the mechanism of the entanglement protection is revealed as the high-frequency(HF) noise in the atom-cavity couplings could suppress the decoherence caused by the cavity leakage, thus protect the entanglement. We investigate the entanglement protection induced by mixed noise constructed from diverse noise types, including the Ornstein-Uhlenbeck noise, flicker noise, and telegraph noise. Numerical simulations demonstrate that entanglement protection depends critically on the proportion of HF components in the power spectral density of the mixed noise. Our work establishes that enhanced HF components are essential for effective noise-assisted entanglement protection, offering key insights for noise engineering in practical open quantum systems.
| 2025-12-27
| 2025-12-30
|
[
"quant-ph"
] |
Tengtao Guo, Yuxuan Zhou, Jiahui Feng, Xinyu Zhao, Yan Xia
|
2512.22679
|
When spacetime vibrates: An introduction to gravitational waves
|
This article presents a comprehensive analysis of the physics of gravitational waves, exploring both the theoretical foundations and the most recent experimental advances. After a general introduction to the theory of general relativity and its major implications, the article discusses the history of gravitational waves, from their prediction by Einstein to their actual detection. It then explains what gravitational waves are and how they interact with appropriate detectors. The main mechanisms of gravitational radiation emission are analyzed, with a focus on compact binary systems of compact objects, whose orbits typically evolve in three phases: inspiral, merger, and the final ringdown phase, each of these phases leaving distinct signatures in the emitted waves. The article highlights the fundamental role of the giant interferometers LIGO, Virgo, and KAGRA, true cathedrals of modern science, and revisits the historic event GW150914, the first direct detection of gravitational waves, which confirmed the predictions of general relativity and opened a new era for astronomy. This achievement was recognized with the 2017 Nobel Prize in Physics. Other observed events are also discussed, along with their astrophysical sources, and the possibility of detecting gravitational waves of cosmological origin, originating from the Big Bang itself. Finally, current and future projects are analyzed, including observatories based on increasingly sophisticated interferometers, as well as proposals for alternative detection methods, illustrating how gravitational-wave astronomy is shaping the present and future of our exploration of the universe. In concluding, the detection of gravitational waves is set in a broader context by examining the discoveries across the electromagnetic spectrum, thereby illustrating the complementary perspectives these different observational channels provide.
| 2025-12-27
| 2025-12-30
|
[
"gr-qc"
] |
José P. S. Lemos
|
2512.22549
|
Radiative symmetry breaking in a gauged Zee-Babu model and its gravitational wave imprints
|
We construct a classically scale invariant version of the Zee-Babu model governed by an $U(1)_{B-L}$ gauge symmetry wherein three right handed neutrinos with identical gauge charges are present. A $\mathbb{Z}_2$ symmetry is additionally imposed such that the lightest right handed neutrino becomes a dark matter candidate. A spontaneous breakdown of the $U(1)_{B-L}$ gauge group is triggered radiatively through renormalisation group effects and the dimensionful parameters thus emerging are proportional to the corresponding breaking scale $v_{BL}$. We demonstrate in this study how the same $v_{BL}$ controls the dynamics of neutrino mass generation, lepton flavour violation and dark matter phenomenology. It is revealed that the scenario can simultaneously accommodate the observed neutrino masses and mixings, an appropriately low lepton flavour violation and the observed dark matter relic density for 10 TeV $\lesssim v_{BL} \lesssim$ 55 TeV. In addition, the very radiative nature of the set-up signals a strong first order phase transition in the presence of a non-zero temperature. Stochastic gravitational waves stemming from this phase transition are within the reach of detectors such as LISA and BBO. The scenario therefore emerges as a concrete platform to test classical scale invariance that is tied to neutrino masses and dark matter, through gravitational waves.
| 2025-12-27
| 2025-12-30
|
[
"hep-ph"
] |
Indra Kumar Banerjee, Nabarun Chakrabarty, Ujjal Kumar Dey
|
2209.13010
|
Iterating sum of power divisor function and New equivalence to the Riemann hypothesis
|
This paper investigates the dynamics of the iterated sum-of-divisors function $Ï_k(m)$ and its behaviour modulo $m$, motivated by classical questions on perfect and multiperfect numbers and by the congruences $Ï_k(m) \equiv 0 \pmod m$. Perfect and multiperfect numbers remain extremely rare; odd perfect numbers are still unknown and must be astronomically large. Here, the emphasis is on the dynamical and statistical structure of the iterates rather than on isolated examples.
Three main results are obtained. First, it is proved that no integer $m>1$ can satisfy $Ï_k(m) \equiv 0 \pmod m$ for all $k \ge 0$, thereby ruling out the existence of "metaperfect" numbers and showing that the iteration of $Ï$ cannot remain permanently trapped in the residue class $0$ modulo $m$. Second, for certain explicit integers such as $m=6,12,24$, the sequence $Ï_k(m) \bmod m$ is strictly periodic with small period dividing $L=\mathrm{lcm}(e_i+1)$, where the $e_i$ are the prime exponents of $m$. Bifurcation plots and distributional analysis reveal a transition from rigid two-cycle structure to more complex residue dynamics as $m$ increases. Third, a new equivalence with the Riemann Hypothesis is established: RH holds if and only if, for every even non-squarefree $m \ge 5041$ containing a prime fifth power, \[ \frac{Ï_k(m)}{Ï_{k-1}(m)\log\logÏ_{k-1}(m)} \le e^γ, \] and the sequence $Ï_k(m) \bmod m$ is eventually periodic, uniformly in $k \ge 0$. Extensive computations support these periodicity phenomena, yield non-normal discrete distribution models for the residues, and suggest a connection with a newly proposed Schrodinger-type "Caceres" operator whose spectrum numerically reproduces key statistical features of the nontrivial zeros of the Riemann zeta function.
| 2025-12-27
| 2025-12-30
|
[
"math.GM"
] |
Pedro Caceres, Zeraoulia Rafik
|
2512.22491
|
ManchuTTS: Towards High-Quality Manchu Speech Synthesis via Flow Matching and Hierarchical Text Representation
|
As an endangered language, Manchu presents unique challenges for speech synthesis, including severe data scarcity and strong phonological agglutination. This paper proposes ManchuTTS(Manchu Text to Speech), a novel approach tailored to Manchu's linguistic characteristics. To handle agglutination, this method designs a three-tier text representation (phoneme, syllable, prosodic) and a cross-modal hierarchical attention mechanism for multi-granular alignment. The synthesis model integrates deep convolutional networks with a flow-matching Transformer, enabling efficient, non-autoregressive generation. This method further introduce a hierarchical contrastive loss to guide structured acoustic-linguistic correspondence. To address low-resource constraints, This method construct the first Manchu TTS dataset and employ a data augmentation strategy. Experiments demonstrate that ManchuTTS attains a MOS of 4.52 using a 5.2-hour training subset derived from our full 6.24-hour annotated corpus, outperforming all baseline models by a notable margin. Ablations confirm hierarchical guidance improves agglutinative word pronunciation accuracy (AWPA) by 31% and prosodic naturalness by 27%.
| 2025-12-27
| 2025-12-30
|
[
"cs.CL",
"cs.AI"
] |
Suhua Wang, Zifan Wang, Xiaoxin Sun, D. J. Wang, Zhanbo Liu, Xin Li
|
2512.22444
|
Three-Dimensional Almost Contact Metric Manifolds Revisited via the Newman-Penrose Formalism
|
This paper applies the Newman-Penrose formalism-a technique primarily used in General Relativity-to the analysis of three-dimensional almost contact metric (ACM) manifolds. We reformulate and discuss several known notions and properties within the Newman-Penrose framework, demonstrating the applicability of the method in this geometric context. Furthermore, as an application showcasing the utility of the formalism, we address the classification of three-dimensional compact normal ACM manifolds, or equivalently trans-Sasakian manifolds, that admit an $η$-Einstein metric.
| 2025-12-27
| 2025-12-30
|
[
"math.DG"
] |
Satsuki Matsuno
|
2507.05225
|
Extremal Behavior of ideals of minors
|
Let $(R,\mathfrak m,\mathsf k)$ be either a fiber product or an artinian stretched Gorenstein ring, with $\operatorname{ch}(\mathsf k)\neq 2$ in the latter case. We prove that the ideals of minors of the minimal free resolution of any finitely generated $R$-module are eventually 2-periodic. Moreover, if the embedding dimension of $R$ is at least 3, eventually the ideals of minors become the powers of the maximal ideal, yielding the 1-periodicity. These are analogs of results obtained over complete intersections and Golod rings by Brown, Dao, and Sridhar. We also study the transfer of periodicity between rings. Specifically, we prove that for any local ring $(R,\mathfrak m)$, if $x\in \mathfrak m$ is a super-regular element and $M$ is an $R/(x)$ module whose ideals of minors are asymptotically the powers of the maximal ideal over $R/(x)$, then the same holds for the ideals of minors of $M$ over $R$.
| 2025-12-27
| 2025-12-30
|
[
"math.AC"
] |
Trung Chau, Michale DeBellevue, Souvik Dey, K. Ganapathy, Omkar Javadekar
|
2512.22719
|
Global Martingale Entropy Solutions to the Stochastic Isentropic Euler Equations
|
We establish the existence and compactness of global martingale entropy solutions with finite relative-energy for the stochastically forced system of isentropic Euler equations governed by a general pressure law. To achieve these, a stochastic compensated compactness framework in $L^p$ is developed to overcome the difficulty that the uniform $L^{\infty}$ bound for the stochastic approximate solutions is unavailable, owing to the stochastic forcing term. The convergence of the vanishing viscosity method is established by employing the stochastic compactness framework, along with careful uniform estimates of the stochastic approximate solutions, to obtain the existence of global martingale entropy solutions with finite relative-energy. In particular, in the polytropic pressure case for all adiabatic exponents, we prove that the global solutions satisfy the local mechanical energy inequality when the initial data are only required to have finite relative-energy (while the higher moment estimates for entropy are not required here, as needed in the earlier work). Higher-order relative energy estimates for approximate solutions are also derived to establish the entropy inequality for more convex entropy pairs and to then prove the compactness of solutions to the stochastic isentropic Euler system. The stochastic compensated compactness framework and the uniform estimate techniques for approximate solutions developed in this paper should be useful in the study of other similar problems.
| 2025-12-27
| 2025-12-30
|
[
"math.AP",
"math-ph",
"math.FA",
"math.MP",
"math.PR"
] |
Gui-Qiang G. Chen, Feimin Huang, Danli Wang
|
2512.02970
|
Identification of Multivariate Measurement Error Models
|
This paper develops new identification results for multidimensional continuous measurement-error models where all observed measurements are contaminated by potentially correlated errors and none provides an injective mapping of the latent distribution. Using third order cross moments, the paper constructs a three way tensor whose unique decomposition, guaranteed by Kruskal theorem, identifies the factor loading matrices. Starting with a linear structure, the paper recovers the full distribution of latent factors by constructing suitable measurements and applying scalar or multivariate versions of Kotlarski identity. As a result, the joint distribution of the latent vector and measurement errors is fully identified without requiring injective measurements, showing that multivariate latent structure can be recovered in broader settings than previously believed. Under injectivity, the paper also provides user-friendly testable conditions for identification. Finally, this paper provides general identification results for nonlinear models using a newly-defined generalized Kruskal rank - signal rank - of intergral operators. These results have wide applicability in empirical work involving noisy or indirect measurements, including factor models, survey data with reporting errors, mismeasured regressors in econometrics, and multidimensional latent-trait models in psychology and marketing, potentially enabling more robust estimation and interpretation when clean measurements are unavailable.
| 2025-12-27
| 2025-12-30
|
[
"econ.EM",
"stat.ML"
] |
Yingyao Hu
|
2512.18516
|
Comment on "Spontaneous baryosynthesis with large initial phase"
|
Recently arXiv:2512.11011 set out to improve on previous work from 1994 by Dolgov and Freese, who used a small-angle approximation to derive the yield of spontaneous baryogenesis from a rolling phase, a pseudo-Nambu-Goldstone boson coupled to the baryon current. The goal of the recent paper was to investigate what happens when the small-angle approximation is not imposed. I point out a serious technical shortcoming in their derivation.
| 2025-12-27
| 2025-12-30
|
[
"hep-ph"
] |
James M. Cline
|
2503.12629
|
Quasilinearization with regularizing tensor paraproducts
|
We extend Bony's celebrated work on paraproducts to continous and multiscale \emph{tensor} paraproducts. For $A \in \mathcal{C}^2(\mathbb{R})$ and $f \in Î_α([0,1]^2, d_d(x,y)^α \times d'_d(x',y')^α)$, we construct an approximation, $\tilde{A}_{(N,N')}(f)$ to $A(f)$, replacing the operator $T: f \to A(f)$ with the continous tensor paraproduct, $Î ^{(t,t')}_{(A',A'')}$, and the multiscale tensor paraproduct $Î ^{(N,N')}_{(A',A'')}:f \to \tilde{A}_{(N,N')}(f) + Î_{ (N,N')}(A,f)$. In the multiscale case, we provide estimates on the residual, $Î_{(N,N')}(A,f)$, and show it has twice the regularity of $f$ such that $Î_{(N,N')}(A,f) \in Î_{2 α}([0,1]^2)$ and $\lVert Î_{(N,N')}(A,f) \rVert_{Î_{2α}([0,1]^2)} \leq C_A \lVert f \rVert_{Î_α([0,1]^2)} $. Our theoretical findings are supplemented with a computational example.
| 2025-12-27
| 2025-12-30
|
[
"math.AP",
"cs.DM"
] |
Oluwadamilola Fasina
|
2512.22715
|
Kinematic and dynamical origins of mean-$p_T$ fluctuations in heavy-ion collisions
|
Event-by-event fluctuations of the mean transverse momentum (mean-$p_T$) provide a sensitive probe of collective dynamics beyond single-particle spectra and anisotropic flow. In this study, we present a systematic comparison of mean-$p_T$ fluctuation measurements with calculations based on a Bayesian-calibrated multistage hydrodynamic framework. The experimental definitions employed by the STAR and ALICE Collaborations are implemented explicitly and found to yield consistent results within controlled limits. We study the centrality and beam-energy dependence of the observable, its sensitivity to key soft-sector ingredients, and the impact of the kinematic $p_T$ acceptance. By introducing scaled-$p_T$ cuts, we demonstrate that a part of the apparent energy dependence arises from kinematic projection effects, while the remaining trends reflect genuine collective dynamics. Our results establish mean-$p_T$ fluctuations as a nontrivial and independent validation of calibrated hydrodynamic descriptions of the quark--gluon plasma.
| 2025-12-27
| 2026-01-08
|
[
"nucl-th",
"nucl-ex"
] |
Lipei Du
|
2512.22552
|
Computing Pure-Strategy Nash Equilibria in a Two-Party Policy Competition: Existence and Algorithmic Approaches
|
We formulate two-party policy competition as a two-player non-cooperative game, generalizing Lin et al.'s work (2021). Each party selects a real-valued policy vector as its strategy from a compact subset of Euclidean space, and a voter's utility for a policy is given by the inner product with their preference vector. To capture the uncertainty in the competition, we assume that a policy's winning probability increases monotonically with its total utility across all voters, and we formalize this via an affine isotonic function. A player's payoff is defined as the expected utility received by its supporters. In this work, we first test and validate the isotonicity hypothesis through voting simulations. Next, we prove the existence of a pure-strategy Nash equilibrium (PSNE) in both one- and multi-dimensional settings. Although we construct a counterexample demonstrating the game's non-monotonicity, our experiments show that a decentralized gradient-based algorithm typically converges rapidly to an approximate PSNE. Finally, we present a grid-based search algorithm that finds an $ε$-approximate PSNE of the game in time polynomial in the input size and $1/ε$.
| 2025-12-27
| 2025-12-30
|
[
"cs.GT",
"cs.LG"
] |
Chuang-Chieh Lin, Chi-Jen Lu, Po-An Chen, Chih-Chieh Hung
|
2511.12862
|
Word Length Formulae and Normal Forms of Conjugacy Classes in Surface Groups
|
In this paper, we primarily investigate the following symmetric presentation of the surface group $Ï_1(Σ_g)=\left\langle c_1,\dots, c_{2g}\mid c_1\cdots c_{2g}c_1^{-1}\cdots c_{2g}^{-1}\right\rangle$. For every nontrivial element $x\in Ï_1(Σ_g)$, we obtain a uniform representation of the normal forms of $x^k$ under the length-lexicographical order. Based on this, we find a new relation among these normal forms, and then derive the following three formulae related to the word length: $|x^2|>|x|$; $|x^k|=(k-1)(|x^2|-|x|)+|x|$; $\lim_{k\to\infty}\frac{|x^k|}{k}=|x^2|-|x|$. Moreover, we extend these results to obtain analogous but less precise formulae for every minimal geometric presentation. Then, we define the normal forms of conjugacy classes in $Ï_1(Σ_g)$ and give a criterion for determining the conjugacy of elements. As a consequence, we give efficient algorithms for solving the root-finding and conjugacy problems. Finally, we present applications concerning the computation of some growth rates.
| 2025-12-27
| 2025-12-30
|
[
"math.GT",
"math.GR"
] |
Ke Wang, Qiang Zhang, Xuezhi Zhao
|
2512.22625
|
The Wisdom of Deliberating AI Crowds: Does Deliberation Improve LLM-Based Forecasting?
|
Structured deliberation has been found to improve the performance of human forecasters. This study investigates whether a similar intervention, i.e. allowing LLMs to review each other's forecasts before updating, can improve accuracy in large language models (GPT-5, Claude Sonnet 4.5, Gemini Pro 2.5). Using 202 resolved binary questions from the Metaculus Q2 2025 AI Forecasting Tournament, accuracy was assessed across four scenarios: (1) diverse models with distributed information, (2) diverse models with shared information, (3) homogeneous models with distributed information, and (4) homogeneous models with shared information. Results show that the intervention significantly improves accuracy in scenario (2), reducing Log Loss by 0.020 or about 4 percent in relative terms (p = 0.017). However, when homogeneous groups (three instances of the same model) engaged in the same process, no benefit was observed. Unexpectedly, providing LLMs with additional contextual information did not improve forecast accuracy, limiting our ability to study information pooling as a mechanism. Our findings suggest that deliberation may be a viable strategy for improving LLM forecasting.
| 2025-12-27
| 2025-12-30
|
[
"cs.AI",
"cs.MA"
] |
Paul Schneider, Amalie Schramm
|
2512.22717
|
A rational length scale for large-eddy simulation of turbulence on anisotropic grids
|
Due to the prohibitive cost of resolving all relevant scales, direct numerical simulations of turbulence remain unfeasible for most real-world applications. Consequently, dynamically simplified formulations are needed for coarse-grained simulations. In this regard, eddy-viscosity models for Large-Eddy Simulation (LES) are widely used both in academia and industry. These models require a subgrid characteristic length, typically linked to the local grid size. While this length scale corresponds to the mesh step for isotropic grids, its definition for unstructured or anisotropic Cartesian meshes, such as the pancake-like meshes commonly used to capture near-wall turbulence or shear layers, remains an open question. Despite its significant influence on LES model performance, no consensus has been reached on its proper formulation. In this work, we introduce a novel subgrid characteristic length. This length scale is derived from the analysis of the entanglement between the numerical discretization and the filtering in LES. Its mathematical properties and simplicity make it a robust choice for reducing the impact of mesh anisotropies on simulation accuracy. The effectiveness of the proposed subgrid length is demonstrated through simulations of decaying isotropic turbulence and a turbulent channel flow using different codes.
| 2025-12-27
| 2025-12-30
|
[
"physics.flu-dyn",
"physics.comp-ph"
] |
F. Xavier Trias, Jesús Ruano, Alexey Duben, Andrey Gorobets
|
2512.22546
|
Projection onto the parabola
|
In this note, we provide explicit expressions for the projections onto the graph of a quadratic polynomial. The projections are obtained by examining the critical points of the associated quartic polynomial, that is, the roots of the cubic polynomial defining its derivative. We also focus on the case where the point we project lies on the vertical line defined by the parabola. Lastly, an explicit formula for the projection onto a higher dimensional parabola is derived.
| 2025-12-27
| 2025-12-30
|
[
"math.GM"
] |
Francisco J. Aragón-Artacho, Heinz H. Bauschke, César López-Pastor
|
2508.04335
|
RiemanLine: Riemannian Manifold Representation of 3D Lines for Factor Graph Optimization
|
Minimal parametrization of 3D lines plays a critical role in camera localization and structural mapping. Existing representations in robotics and computer vision predominantly handle independent lines, overlooking structural regularities such as sets of parallel lines that are pervasive in man-made environments. This paper introduces \textbf{RiemanLine}, a unified minimal representation for 3D lines formulated on Riemannian manifolds that jointly accommodates both individual lines and parallel-line groups. Our key idea is to decouple each line landmark into global and local components: a shared vanishing direction optimized on the unit sphere $\mathcal{S}^2$, and scaled normal vectors constrained on orthogonal subspaces, enabling compact encoding of structural regularities. For $n$ parallel lines, the proposed representation reduces the parameter space from $4n$ (orthonormal form) to $2n+2$, naturally embedding parallelism without explicit constraints. We further integrate this parameterization into a factor graph framework, allowing global direction alignment and local reprojection optimization within a unified manifold-based bundle adjustment. Extensive experiments on ICL-NUIM, TartanAir, and synthetic benchmarks demonstrate that our method achieves significantly more accurate pose estimation and line reconstruction, while reducing parameter dimensionality and improving convergence stability.
| 2025-12-27
| 2025-12-30
|
[
"cs.CV",
"cs.RO"
] |
Yan Li, Ze Yang, Keisuke Tateno, Federico Tombari, Liang Zhao, Gim Hee Lee
|
2501.16044
|
MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation
|
Debugging software remains a labor-intensive and time-consuming process despite advances in testing and verification. Learning-based automated program repair (APR) has shown promise in reducing the effort of manually fixing bugs. However, existing techniques face several challenges, including language-dependent strategies, limited bug context utilization, and difficulties in handling bugs that span multiple locations in the code. This paper presents MultiMend, a multilingual learning-based APR approach designed to improve repair performance through language-independent context augmentation and multi-hunk patch generation. MultiMend fine-tunes a pre-trained code language model to generate bug-fixing patches. It embeds source code lines and applies retrieval-augmented generation to augment the usual function-based buggy context with relevant lines during patch generation. The approach also systematically constructs patches for multi-hunk bugs to extend the capabilities of single-hunk models and reduce the needed patch validations. We evaluate MultiMend on six benchmarks with 5,501 bugs covering four programming languages and compare it with state-of-the-art methods. Results show that MultiMend achieves competitive effectiveness and efficiency, fixing 2,227 bugs, of which 1,545 are identical to the developer's patch, and 121 are for multi-hunk bugs. Both context augmentation and multi-hunk patch generation contribute positively to these results. Overall, MultiMend's contributions are promising and offer practical and effective techniques to enhance APR performance for real-world software maintenance.
| 2025-12-27
| 2025-12-30
|
[
"cs.SE"
] |
Reza Gharibi, Mohammad Hadi Sadreddini, Seyed Mostafa Fakhrahmad
|
2504.11390
|
Unraveling Momentum and Heat Intercoupling in Reattaching Turbulent Boundary Layers Using Dynamic Mode Decomposition
|
Dynamic mode decomposition method is deployed to investigate the heat transfer mechanism in a compressible turbulent shear layer and shockwave. To this end, highly resolved Large Eddy Simulations are performed to explore the effect of wall thermal conditions on the behavior of a reattaching free shear layer interacting with an oblique shock in compressible turbulent flows. Various different wall temperature conditions, such as cold adiabatic and hot wall, are considered. Dynamic mode decomposition is used to isolate and study the structures generated by the shear layer exposed in the boundary layer. Results reveal that the shear layer flapping is the most energetic mode. The hot wall gains the highest amplitude for the flapping frequency, and the vortical motions are most intense in the vicinity of the reattachment point of the heated wall. The vortex shedding due to the large-scale motion of the shear layer is associated with the second energetic mode. The cold wall not only has a higher amplitude of the shedding mode, but it also has a lower frequency compared to the adiabatic and hot walls. This work sheds light on the underlying physics of the nonlinear intercoupling of momentum and heat, hence providing guidelines for designing control systems for high speed flight vehicles and mitigating aircraft fatigue loading caused by intense wall pressure fluctuations and heat flux.
| 2025-12-27
| 2025-12-30
|
[
"physics.flu-dyn"
] |
Rozie Zangeneh
|
2512.22521
|
Quantum Noise Spectroscopy of Nanoscale Charge Defects in Silicon Carbide at Room Temperature
|
The nanoscale charge environment critically influences semiconductor physics and device performance. While conventional bulk characterization techniques provide volume-averaged defect properties, they lack the spatial resolution to resolve nanoscale charge heterogeneity and identify microscopic noise sources. Here, we utilize single PL5 centers in 4H-SiC as room-temperature broadband quantum sensors to fill in the gap. We report the first real-time, nanoscale observation of singlecharge tunneling dynamics in a commercial semiconductor at room temperature, by monitoring the random telegraph noise using optically detected magnetic resonance (ODMR). This capability enables an electrical noise imaging technique, showing distinct noise variations across different wafer substrates. By employing dynamical decoupling, we extend noise spectroscopy from near-DC to MHz frequencies, uncovering significant noise spectral density correlations across frequency bands. Finally, we probe MHz-GHz noise and identify its origin via T1 relaxation spectroscopy, obtaining the first nanoscale electron paramagnetic resonance (EPR) spectroscopic fingerprint of charge defects in SiC. These techniques open avenues for characterizing noise environments in semiconductor devices, providing critical insights for optimizing SiC fabrication processes, defect control, and advancing quantum technologies.
| 2025-12-27
| 2025-12-30
|
[
"quant-ph",
"cond-mat.mtrl-sci"
] |
Jinpeng Liu, Yuanhong Teng, Yu Chen, Yixuan Wang, Chihang Luo, Jun Yin, Hao Li, Lixing You, Ya Wang, Qi Zhang, Fazhan Shi
|
2512.19156
|
Classical billiards can compute
|
We show that two-dimensional billiard systems are Turing complete by encoding their dynamics within the framework of Topological Kleene Field Theory. Billiards serve as idealized models of particle motion with elastic reflections and arise naturally as limits of smooth Hamiltonian systems under steep confining potentials. Our results establish the existence of undecidable trajectories in physically natural billiard-type models, including billiard-type models arising in hard-sphere gases and in collision-chain limits of celestial mechanics.
| 2025-12-27
| 2025-12-30
|
[
"math.DS",
"cs.CC",
"math-ph",
"math.MP"
] |
Eva Miranda, Isaac Ramos
|
2512.22596
|
Evaluating Soccer Player Movements Using the Attacker-Defender Model
|
The present study investigates the attacker-defender (AD) model proposed by Brink et al. (2023), a motion model that describes the interactions between a ball carrier (attacker) and the nearest defender during ball possession. The model is based on the equations of motion for both players, incorporating resistance, goal-oriented force, and opponent-oriented force. It generates trajectories based on physically interpretable parameters. Although the AD model reproduces real dribbling trajectories well, previous studies have explored only a limited range of parameter values and relied on relatively small datasets.
This study aims to (1) enhance parameter optimization by solving the AD model for one player with the opponent's actual trajectory fixed, (2) validate the model's applicability to a large dataset from 306 J1-League matches, and (3) demonstrate distinct playing styles of attackers and defenders based on the full range of optimized parameters.
| 2025-12-27
| 2025-12-30
|
[
"physics.soc-ph"
] |
Takuma Narizuka, Issei Yamazaki
|
2509.12974
|
The CCF AATC 2025 Speech Restoration Challenge: A Retrospective
|
Real-world speech communication is rarely affected by a single type of degradation. Instead, it suffers from a complex interplay of acoustic interference, codec compression, and, increasingly, secondary artifacts introduced by upstream enhancement algorithms. To bridge the gap between academic research and these realistic scenarios, we introduced the CCF AATC 2025 Challenge. This challenge targets universal blind speech restoration, requiring a single model to handle three distinct distortion categories: acoustic degradation, codec distortion, and secondary processing artifacts. In this paper, we provide a comprehensive retrospective of the challenge, detailing the dataset construction, task design, and a systematic analysis of the 25 participating systems. We report three key findings that define the current state of the field: (1) Efficiency vs. Scale: Contrary to the trend of massive generative models, top-performing systems demonstrated that lightweight discriminative architectures (<10M parameters) can achieve state-of-the-art performance, balancing restoration quality with deployment constraints. (2) Generative Trade-off: While generative and hybrid models excel in theoretical perceptual metrics, breakdown analysis reveals they suffer from "reconstruction bias" in high-SNR codec tasks and struggle with hallucination in complex secondary artifact scenarios. (3) Metric Gap: Most critically, our rank correlation analysis exposes a strong negative correlation (\r{ho}=-0.8) between widely-used reference-free metrics (e.g., DNSMOS) and human MOS when evaluating hybrid systems. This indicates that current metrics may over-reward artificial spectral smoothness at the expense of perceptual naturalness. This paper aims to serve as a reference for future research in robust speech restoration and calls for the development of next-generation evaluation metrics sensitive to generative artifacts.
| 2025-12-27
| 2025-12-30
|
[
"cs.SD",
"eess.AS"
] |
Junan Zhang, Mengyao Zhu, Xin Xu, Hui Bu, Zhenhua Ling, Zhizheng Wu
|
2512.22422
|
S-BLE: A Participatory BLE Sensory Data Set Recorded from Real-World Bus Travel Events
|
This contribution describes S-BLE, a data set created for supporting the design of robust and reliable Be-In-Be-Out systems in public transit. S-BLE was recorded by the smartphones of 28 participants during their daily transit routines in a university campus setting. 20 shuttle bus vehicles in the campus fleet were equipped with two Bluetooth low energy (BLE) beacons each. RSSI data from these beacons was recorded during regular rides, along with odometry information (from GPS) and data from the smartphone's inertial sensors. The article describes the system used for data collection and presents some statistics of interest for the recorded data.
| 2025-12-27
| 2025-12-30
|
[
"cs.OH"
] |
Jonathan Lam, Roberto Manduchi
|
2512.22421
|
Differentiable Inverse Modeling with Physics-Constrained Latent Diffusion for Heterogeneous Subsurface Parameter Fields
|
We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDE-constrained inverse problems involving high-dimensional spatially distributed coefficients. LD-DIM couples a pretrained latent diffusion prior with an end-to-end differentiable numerical solver to reconstruct unknown heterogeneous parameter fields in a low-dimensional nonlinear manifold, improving numerical conditioning and enabling stable gradient-based optimization under sparse observations. The proposed framework integrates a latent diffusion model (LDM), trained in a compact latent space, with a differentiable finite-volume discretization of the forward PDE. Sensitivities are propagated through the discretization using adjoint-based gradients combined with reverse-mode automatic differentiation. Inversion is performed directly in latent space, which implicitly suppresses ill-conditioned degrees of freedom while preserving dominant structural modes, including sharp material interfaces. The effectiveness of LD-DIM is demonstrated using a representative inverse problem for flow in porous media, where heterogeneous conductivity fields are reconstructed from spatially sparse hydraulic head measurements. Numerical experiments assess convergence behavior and reconstruction quality for both Gaussian random fields and bimaterial coefficient distributions. The results show that LD-DIM achieves consistently improved numerical stability and reconstruction accuracy of both parameter fields and corresponding PDE solutions compared with physics-informed neural networks (PINNs) and physics-embedded variational autoencoder (VAE) baselines, while maintaining sharp discontinuities and reducing sensitivity to initialization.
| 2025-12-27
| 2025-12-30
|
[
"math.NA",
"cs.LG",
"cs.NA",
"physics.geo-ph"
] |
Zihan Lin, QiZhi He
|
2512.22617
|
Evidence for a Stratified Accretion Disk Wind in AGN
|
We present observational evidence supporting the presence of a stratified accretion disk wind in active galactic nuclei (AGN), based on multi-wavelength spectroscopic analysis of broad and narrow emission lines. The diversity in emission line profiles, ionization potentials, and kinematic signatures suggests a structured outflow emerging from the accretion disk, with different zones contributing to specific spectral features. High-ionization lines (e.g., Civ λ1549) exhibit strong blueshifts and asymmetric profiles indicative of fast, inner winds, while low-ionization lines (e.g., H\b{eta}, Mgii λ 2800) show more symmetric profiles consistent with predominant emission from slower, denser regions farther out, although exhibiting systematic blueshifts in quasars radiating at high Eddington ratios. The intermediate ionization lines (e.g., Aliii λ1860) present a situation that is intermediate in terms of shift amplitudes, although in several super-Eddington candidates radial outflow velocity may reach values comparable to the ones of the high ionization lines. These results are consistent with radiatively driven wind models featuring radial stratification. We made preliminary photoionization modeling assuming unabsorbed radiation emitted from the corona and the hotter disk regions emission or absorbed by a layer of gas. Our findings provide new constraints on the geometry and physical conditions of AGN winds, providing clear evidence in favor of stratified wind emission.
| 2025-12-27
| 2025-12-30
|
[
"astro-ph.GA"
] |
P. Marziani, E. Bon, S. Panda, N. Bon, A. Del Olmo, A. Deconto-Machado, K. Garnica, D. Dultzin
|
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