1273993 results (page 138 of 50960)
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Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution
Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing CMM methods are hindered by a fundamental saturation-redundancy dilemma: backbone-centric approaches face parameter saturation and representation interference within fixed capacities, whereas Mixture-of-Experts (MoE) variants resort to indiscriminat…
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
The multi-messenger exploration of dark matter and physics beyond the Standard Model has emerged as a central direction in modern astro-particle physics, particularly following the discovery of gravitational waves. In this work, we present a comprehensive review and forward-looking perspective on machine-learning-enhanced multi-messenger approaches, combining information from gravitational waves, …
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Spatially resolved metallicity and ionization in the merging system Gz9p3 at z=9.3
Studying the interstellar medium (ISM) in merging high-redshift galaxies is crucial for understanding early galaxy assembly, star formation, and black hole growth, predicted by hierarchical $Λ$CDM models. Deep imaging and spatially resolved spectroscopy with JWST enable unprecedented insight into these processes, even for galaxies in the Epoch of Reionization. We present NIRSpec and MIRI integral …
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On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery
A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines the boundaries in which the system must operate. Compared to traditional process models, the process frame should, in principle, provide a somewhat more permissive representation of the managed processes, such that the (semi) autonomous behavior…
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Gamifying Architectural Governance to Reduce Organizational Coupling in Microservice Systems
Microservice is a popular software architecture that relies on decentralized teams and clear service ownership to support modularity and scalability. However, in practice, developers frequently contribute across multiple services, creating organizational coupling (OC) that gradually erodes architectural boundaries and increases coordination overhead. This study proposes a vision for behavior-drive…
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Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents
Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language model agents scale to populations of millions, a key question arises: Does collective intelligence emerge spontaneously from scale? We present the first empirical evaluation of this question in a large-scale autonomous agent society. Studying MoltBo…
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Exploring Cosmic Evolution in Rényi Entropic Cosmology with Constraints from DESI DR2 BAO and GW Data
We explore a cosmological model based on Rényi entropic corrections to the Friedmann equations and constrain it using DESI, P-BAO, CC, and gravitational-wave observations. Unlike earlier works, we obtain a direct and stringent constraint on the Rényi parameter $λ$ from late-time cosmic acceleration, with the resulting value even satisfying recent Big Bang Nucleosynthesis and baryogenesis bounds. T…
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From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupled coordination logic, and session-bound learning. We argue that this reflects a deeper absence: a principled organisational layer that governs how a workforce of agents is assembled, governed, and improved over time, de…
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HubRouter: A Pluggable Sub-Quadratic Routing Primitive for Hybrid Sequence Models
We introduce HubRouter, a pluggable module that replaces O(n^2) attention layers with O(nM) hub-mediated routing, where M << n is a small number of learned hub tokens. We demonstrate it in two from-scratch architectures: a Jamba-style hybrid and a 12-layer Transformer; retrofit into pretrained models is a tested negative case. HubRouter implements an encode-decode-score-council pipeline: M learned…
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How lonely are the Binary Compact Objects Detected by the LIGO-Virgo-KAGRA Collaboration?
Gravitational-wave (GW) observations of compact binary coalescences (CBCs) are traditionally interpreted under the assumption that the binary evolves in isolation. However, in realistic astrophysical environments, brief three-body encounters may perturb the binary's orbital evolution and imprint deviations on the emitted GWs. We develop a physically motivated model for such interactions, retaining…
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NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting
We propose a neural regularization method that refines the noisy 3D semantic field produced by lifting multi-view inconsistent 2D features, in order to obtain an accurate and robust 3D semantic Gaussian Splatting. The 2D features extracted from vision foundation models suffer from multi-view inconsistency due to a lack of cross-view constraints. Lifting these inconsistent features directly into 3D…
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SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking
Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatility, efficiency, and effectiveness in natural language generation. However, KGW's effectiveness degrades significantly under low-entropy settings such as code generation and mathema…
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Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients
Computed tomography (CT)-based attenuation and scatter correction improves quantitative PET but adds radiation exposure that is particularly undesirable in pediatric imaging. Existing CT-free methods are commonly trained in homogeneous settings and often degrade under scanner or radiotracer shifts, which limits their clinical utility. We propose the Generalizable PET Correction Network (GPCN), a d…
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Spontaneous spherical symmetry breaking of black holes with resonant hair
Black holes with resonant hair are static, spherical, electrically charged solutions of the Einstein-Maxwell-(gauged-)scalar system. Scalar self-interactions are mandatory for their existence. Initial dynamical studies restricted to spherical symmetry suggested stability; more recently, fully non-spherical dynamical studies revealed instabilities, at least for a particular class of self-interactio…
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AgentSearchBench: A Benchmark for AI Agent Search in the Wild
The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution-dependent, making them difficult to assess from textual descriptions alone. However, existing research and benchmarks typically assume well-s…
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The influence of implantation conditions on dopant activation in Al-implanted 4H-SiC: A MD study applying an Al potential fitted to DFT barriers
We present molecular dynamics simulations of shallow Al implantation in 4H-SiC to clarify how implantation temperature and dose control defect evolution and dopant activation during early annealing. Using the Gao-Weber potential together with a reparameterized Morse Al-SiC interaction fitted to DFT migration and kick-in/out barriers, we find that higher implantation temperature reduces Frenkel-pai…
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Beyond Land Surface Temperature: Explainable Spatial Machine Learning Reveals Urban Morphology Effects on Human-Centric Heat Stress
Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Understanding the spatial heterogeneity of heat exposure and its drivers is vital for climate-adaptive urban planning. However, most planning-oriented studies rely on land surface temperature (LST), and whether LST adequately represents human heat exposure and how it d…
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R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
Accurate requirement-to-code traceability is crucial for software maintenance. However, existing IR- and embedding-based methods are heavily dependent on lexical similarity, often yielding incomplete or inconsistent links across projects and languages and incurring high cost from long-context retrieval and prompting. This paper presents R2Code, an LLM-based semantic traceability framework designed…
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Robust Bayesian Sequential Borrowing for Multi-Population Clinical Programmes
We introduce Robust Bayesian Sequential Borrowing (RBSB), a framework for extrapolating evidence across adjacent subgroups in multi-population clinical programmes where studies are conducted in sequence and populations are ordered by clinical proximity. Conventional approaches weight all historical sources uniformly or exclude distant populations entirely, failing to reflect the natural gradient o…
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A comprehensive evaluation of spatial co-execution on GPUs using MPS and MIG technologies
To mitigate the increasingly common underutilization of computational resources in modern GPUs, spatial sharing methods enable multiple applications to use them simultaneously. This work presents a comprehensive evaluation of NVIDIA's primary technologies to achieve that goal: Multi-Process Service (MPS) and Multi-Instance GPU (MIG). Our findings reveal a crucial trade-off between MPS's flexibilit…
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Horizontal SCA Attacks on Binary kP Algorithms using Chevallier-Mames Atomic Blocks
Scalar multiplication kP is the operation most frequently targeted in Elliptic Curve (EC) cryptosystems. To protect against single-trace Side-Channel Analysis (SCA) attacks, the atomicity principle and various atomic block patterns have been proposed in the past. In this work we use our software and hardware implementations to demonstrate that binary right-to left and left-to-right kP algorithms, …
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CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories. The model integrates multi-modal longit…
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Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation
The offensive security landscape is highly fragmented: enterprise platforms avoid memory-corruption vulnerabilities due to Denial of Service (DoS) risks, Automatic Exploit Generation (AEG) systems suffer from semantic blindness, and Large Language Model (LLM) agents face safety alignment filters and "Live Fire" execution hazards. We introduce Automation-Exploit, a fully autonomous Multi-Agent Syst…
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A discrete Saint-Venant principle for finite element discretizations of elliptic problems
The present paper studies finite element discretizations of second-order elliptic boundary value problems with homogeneous right-hand side and inhomogeneous boundary conditions. We establish discrete spatial decay estimates on element patches for the energy norm of the discrete solution, showing that the influence of boundary data decays exponentially away from the boundary. The resulting estimate…
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Kahler decoupling for Kerr perturbations
The Euclidean Kerr metric is conformal, in two distinct ways, to a Kahler metric, with conformal factors determined by the repeated eigenvalue of the two chiral halves of the Weyl curvature. A Lorentzian analogue holds, where the conformally related metric is complex but retains key features of Kahler geometry. We show that this hidden Kahler structure provides a geometric explanation for the exis…