6 papers
Fair Comparison of Scheduling Algorithms on Heterogeneous Edge Clusters: A Continuous Adaptive Benchmark
Zihang Wang, Boris Sedlak, Juan Luis Herrera +1
Modern Artificial Intelligence (AI) workloads deployed across the heterogeneous tiers of an edge--cloud continuum must satisfy multi-dimensional Service Level Objectives (SLOs) ove…
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
Jiaqi Yuan, Jialu Wang, Zihan Wang +3
Generative search engines represent a transition from traditional ranking-based retrieval to Large Language Model (LLM)-based synthesis, transforming optimization goals from rankin…
Reducing Tool Hallucination via Reliability Alignment
Hongshen Xu, Zichen Zhu, Lei Pan +6
Large Language Models (LLMs) have expanded their capabilities beyond language generation to interact with external tools, enabling automation and real-world applications. However,…
Delusions of Large Language Models
Hongshen Xu, Zixv yang, Zichen Zhu +7
Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high beli…
Alignment for Efficient Tool Calling of Large Language Models
Hongshen Xu, Zihan Wang, Zichen Zhu +4
Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries…