In today's data-driven era, deep learning is vital for processing massive datasets, yet single-device training is constrained by computational and memory limits...
With the rapid development of large language models in code generation, AI-powered editors such as GitHub Copilot and Cursor are revolutionizing software develo...
Recent progress in Large Language Models (LLMs) has substantially advanced the automation of software engineering (SE) tasks, enabling complex activities such a...
In 2008, melamine in infant formula forced laboratories across three continents to verify a compound they had never monitored. Non-targeted analysis using LC/GC...
The memory of contemporary Large Language Models is bound by a physical paradox: as they learn, they fill up. The linear accumulation (O(N)) of Key-Value states...
The serverless computing paradigm offers compelling advantages for deploying Large Language Model (LLM) inference services, including elastic scaling and pay-pe...
The advances of large language models (LLMs) have paved the way for automated software vulnerability repair approaches, which iteratively refine the patch until...
Multi-agent systems have extended the capability of agentic AI. Instead of single inference passes, multiple agents perform collective reasoning to derive high ...
Distributed Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in numerous high-performance computing and deep learning applications. The maj...
For nearly two decades, population protocols have been extensively studied, yielding efficient solutions for central problems in distributed computing, includin...
Evolutionary Neural Architecture Search (ENAS) has gained attention for automatically designing neural network architectures. Recent studies use a neural predic...
Matrix Product State (MPS) is a versatile tensor network representation widely applied in quantum physics, quantum chemistry, and machine learning, etc. MPS sam...
