arXiv preprints from January 1, 2026 through September 22, 2026 — 05:39:35 EST
Compositionality is a cognitive mechanism that allows humans to systematically combine known concepts in novel ways. This study demonstrates how artificial neural agents acquire and utilize compositional generalization to describe previously unseen images. Our method, termed "Composition through Decomposition", involves two sequential...
Open-vocabulary 3D Scene Graph (3DSG) can enhance various downstream tasks in robotics by leveraging structured semantic representations, yet current 3DSG construction methods suffer from semantic inconsistencies caused by noisy cross-image aggregation under occlusions and constrained viewpoints. To mitigate the impact of such...
Debt collection is a critical function within the banking, financial services, and insurance (BFSI) sector, relying heavily on large-scale human-to-human conversational interactions conducted primarily in Vietnamese contact centers. These conversations involve informal spoken language, emotional variability, and complex...
Quantum algorithms are gaining attention in Computational Fluid Dynamics (CFD) for their favorable scaling, as encoding physical fields into quantum probability amplitudes enables representation of two to the power of n spatial points with only n qubits. A key challenge in Quantum CFD is the efficient readout of simulation results, a...
Recent progress in reasoning capabilities of Multimodal Large Language Models(MLLMs) has highlighted their potential for performing complex video understanding tasks. However, in the domain of Video Anomaly Detection and Understanding (VAD&U), existing MLLM-based methods are largely limited to anomaly localization or post-hoc...
The rapid growth of Cloud Computing and Internet of Things (IoT) has significantly increased the interconnection of computational resources, creating an environment where malicious software (malware) can spread rapidly. To address this challenge, researchers are increasingly utilizing Machine Learning approaches to identify malware...
The booksize $ \mathrm{bk}(G) $ of a graph $ G $, introduced by Erdős, refers to the maximum integer $ r $ for which $G$ contains the book $ B_r $ as a subgraph. This paper investigates two open problems in spectral graph theory related to the booksize of graphs. First, we prove that for any positive integer $r$ and any $ B_{r+1}...
We characterize boundedness and compactness of Toeplitz operators on quaternionic Fock spaces with positive measure symbols and slice-function symbols in \(\mathrm{BMO}^1\). For positive measure symbols, we derive criteria using normalized reproducing kernels and symmetric box averages, while for slice \(\mathrm{BMO}^1\) symbols, the...
Named Entity Recognition (NER) is a foundational task in Natural Language Processing (NLP) and Information Retrieval (IR), which facilitates semantic search and structured data extraction. We introduce \textbf{AWED-FiNER}, an open-source collection of agentic tool, web application, and 53 state-of-the-art expert models that provide...
Pretraining corpora contain extensive discourse about AI systems, yet the causal influence of this discourse on downstream alignment remains poorly understood. If prevailing descriptions of AI behaviour are predominantly negative, LLMs may internalise corresponding behavioural priors, giving rise to self-fulfilling misalignment. This...
Most interpretability work focuses on layer- or neuron-level mechanisms in Transformers, leaving expert-level behavior in MoE LLMs underexplored. Motivated by functional specialization in the human brain, we analyze expert activation by distinguishing domain and driver experts. In this work, we study expert activation in MoE models...
We perform a systematic study on the effects of electric fields in the Luttinger liquid phase of the one-dimensional spin-$1/2$ $K_1J_1Γ_1Γ_1^\prime K_2J_2$ model in the region of ferromagnetic nearest-neighboring Kitaev coupling. We find that while electric fields along $(1,1,1)$-direction maintain the Luttinger liquid behavior,...
Graph Neural Networks (GNNs) have been widely adopted for Protein Representation Learning (PRL), as residue interaction networks can be naturally represented as graphs. Current GNN-based PRL methods typically rely on single-perspective graph construction strategies, which capture partial properties of residue interactions, resulting...
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks. Monitoring step-level tool invocation behaviors in real time and proactively intervening before unsafe execution is critical for agent deployment, yet remains under-explored. In this work, we...
Compressing the KV cache is a required step to deploy large language models on edge devices. Current quantization methods compress storage but fail to reduce bandwidth as attention calculation requires dequantizing keys from INT4/INT8 to FP16 before use. We observe that attention scoring is mathematically equivalent to the inner...
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, inconsistent documentation, and reproducibility issues. Here, we introduce MHub$.$ai,...
This paper presents a data center exchange (Data Center Xchange, DCX) architecture for all-photonics networks-as-a-service in distributed data center infrastructures, enabling the creation of a virtual large-scale data center by directly interconnecting distributed data centers in metropolitan areas. Key requirements for such an...
Context. Building on the transformative success of optical redshift surveys, the emerging technique of neutral hydrogen (HI) intensity mapping (IM) offers a novel probe of large-scale structure (LSS) growth and the late-time accelerated expansion of the universe. Aims. We present cosmological forecasts for the Baryon Acoustic...
We investigate the polar Kerr and Faraday effects in two-dimensional multiband chiral superconductors. We show that the clapping modes--the relative phase and amplitude oscillations between two chiral components of the superconducting order parameter--lie well within the quasiparticle excitation gap in multiband systems and dominate...
The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application of these methods to node classification tasks still faces a number of challenges. First, existing data enhancement...
This paper proposes a new second-order symmetric algorithm for solving decoupled forward-backward stochastic differential equations. Inspired by the alternating direction implicit splitting method for partial differential equations, we split the generator into the sum of two functions. In the computation of the value process Y,...
Long-sequence decision-making, which is usually addressed through reinforcement learning (RL), is a critical component for optimizing strategic operations in dynamic environments, such as real-time bidding in computational advertising. The Decision Transformer (DT) introduced a powerful paradigm by framing RL as an autoregressive...
Recent studies have extensively explored chaotic dynamics in quantum optical systems through the mean-field approximation, which corresponds to an ideal, fluctuation-free scenario. However, the inherent sensitivity of chaos to initial conditions implies that even minute fluctuations can be amplified, thereby questioning the...
Identifying an appropriate exchange-correlation functional and computational conditions is essential for explaining the fundamental physics of materials and predicting their properties. Here, we investigate the performance of the meta-GGA functionals SCAN and r2SCAN, with and without a Hubbard U, for describing the charge density wave...
We present a comprehensive study of warm hybrid inflation within the framework of $α$-attractor models, where an axionic inflaton is coupled to a waterfall field in the presence of thermal dissipation. The model is analyzed for both linear ($Υ\propto T$) and cubic ($Υ\propto T^{3}$) dissipation regimes. Confronting the theoretical...