Generative Reward Models (GRMs) have attracted considerable research interest in reward modeling due to their interpretability, inference-time scalability, and ...
Sequence modeling layers in modern language models typically face a trade-off between storage capacity and computational efficiency. While Softmax attention off...
Spiking Neural Networks (SNNs) are dynamical systems that operate on spatiotemporal data, yet their learnable parameters are often limited to synaptic weights, ...
Large Protein Language Models have shown strong potential for generative protein design, yet they frequently produce structural hallucinations, generating seque...
Deploying large language models (LLMs) in mobile and edge computing environments is constrained by limited on-device resources, scarce wireless bandwidth, and f...
Large language models (LLMs) frequently produce contextual hallucinations, where generated content contradicts or ignores information explicitly stated in the p...
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models ho...
Real-time log analysis is the cornerstone of observability for modern infrastructure. However, existing online parsers are architecturally unsuited for the dyna...
Intelligent Connected Vehicles (ICVs) are a core component of modern transportation systems, and their security is crucial as it directly relates to user safety...
Traditional customer support systems, such as Interactive Voice Response (IVR), rely on rigid scripts and lack the flexibility required for handling complex, po...
Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally...
Althoughthereislittleempiricalresearchonplatform-specific performance for retail workloads, the digital transformation of the retail industry has accelerated th...
In this article, we explore federated customization of large models and highlight the key challenges it poses within the federated learning framework. We review...
Large Language Model (LLM)-based applications are increasingly deployed across various domains, including customer service, education, and mobility. However, th...
The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning and action, not to supplant human ...
Autonomous coding agents are increasingly deployed as AI teammates in modern software engineering, independently authoring pull requests (PRs) that modify produ...
Model-driven engineering (MDE) provides abstraction and analytical rigour, but industrial adoption in many domains has been limited by the cost of developing an...
Advances in artificial intelligence (AI) and deep learning have raised concerns about its increasing energy consumption, while demand for deploying AI in mobile...
This paper explores the complexities of automatic detection of software similarities, in relation to the unique challenges of digital artifacts, and introduces ...
The quadratic complexity of self-attention mechanism presents a significant impediment to applying Transformer models to long sequences. This work explores comp...
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client ...
Deploying LLMs efficiently requires testing hundreds of serving configurations, but evaluating each one on a GPU cluster takes hours and costs thousands of doll...
With the increasing demand for high-performance and high-efficiency computing, cloud computing, especially serverless computing, has gradually become a research...
Human biological systems sustain life through extraordinary resilience, continually detecting damage, orchestrating targeted responses, and restoring function t...
