Edge-based representations are fundamental cues for visual understanding, a principle rooted in early vision research and still central today. We extend this pr...
Large Language Models (LLMs) play a critical role in how humans access information. While their core use relies on comprehending written requests, our understan...
Modern code intelligence agents operate in contexts exceeding 1 million tokens--far beyond the scale where humans manually locate relevant files. Yet agents con...
Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due t...
LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task execution. Th...
As LLM-based Multi-Agent Systems (MAS) are increasingly deployed for complex tasks, ensuring their reliability has become a pressing challenge. Since MAS coordi...
As Operational Technology increasingly integrates with Information Technology, the need for Intrusion Detection Systems becomes more important. This paper explo...
The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint o...
The adoption of large language models in safety-critical system engineering is constrained by trustworthiness, traceability, and alignment with established veri...
Autonomous coding agents increasingly contribute to software development by submitting pull requests on GitHub; yet, little is known about how these contributio...
Representational similarity metrics typically force all units to be matched, making them susceptible to noise and outliers common in neural representations. We ...
Managing personal health data is a challenge in today's fragmented and institution-centric healthcare ecosystem. Individuals often lack meaningful control over ...
