Multi-agent systems (MAS) enable complex reasoning by coordinating multiple agents, but often incur high inference latency due to multi-step execution and repea...
Studying the optoelectronic structure of materials can require the computation of up to several thousands of the smallest eigenpairs of a pseudo-hermitian Hamil...
Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real-world applications. Despit...
Large language models are increasingly used for code generation and debugging, but their outputs can still contain bugs, that originate from training data. Dist...
The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabili...
Despite being under development for over 15 years, transaction throughput remains one of the key challenges confronting blockchains, which typically has a cap o...
In the field of artificial intelligence, real parameter single objective optimization is an important direction. Both the Differential Evolution (DE) and the Co...
AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood...
Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate develope...
Understanding complex parameter dependencies is critical for effective configuration and maintenance of software systems across diverse domains - from Computer-...
A new transformation is underway in software engineering, driven by the rapid adoption of generative AI in development workflows. Similar to how version control...
Distributed linearly separable computation is a fundamental problem in large-scale distributed systems, requiring the computation of linearly separable function...
The rapid growth of Cloud Computing and Internet of Things (IoT) has significantly increased the interconnection of computational resources, creating an environ...
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research a...
Scientific and engineering verticals often suffer from data scarcity and strict executability requirements: models must generate not only fluent text, but also ...
This study proposes MCEMOL (Multi-Constrained Evolutionary Molecular Design Framework), a molecular optimization approach integrating rule-based evolution with ...
The rapid integration of IoT with edge computing has revolutionized various domains, particularly healthcare, by enabling real-time data sharing, remote monitor...
Neuromorphic computers hold the potential to vastly improve the speed and efficiency of a wide range of computational kernels with their asynchronous, compute-m...
Coded polynomial aggregation (CPA) enables the master to directly recover a weighted aggregation of polynomial evaluations without individually decoding each te...
Federated learning (FL) enables collaborative model training without sharing raw user data, but conventional simulations often rely on unrealistic data partitio...
The proliferation of connected devices and privacy-sensitive applications has accelerated the adoption of Federated Learning (FL), a decentralized paradigm that...
Organizations and enterprises across domains such as healthcare, finance, and scientific research are increasingly required to extract collective intelligence f...
Since the release of GPT 5.2, AI tools have become inescapable in high-level mathematics....
Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on ...
