Personal use of this material is permitted. Abstract-- V ehicles are becoming increasingly automated and interconnected, enabling the formation of cooperative intelligent transport systems (C-ITS) and the use of offboard services. As a result, cloud-native techniques, such as microservices and container orchestration, play an increasingly important role in their operation. However, orchestrating applications in a large-scale C-ITS poses unique challenges due to the dynamic nature of the environment and the need for efficient resource utilization. In this paper, we present a demand-driven application management approach that leverages cloud-native techniques - specifically Kubernetes - to address these challenges. T aking into account the demands originating from different entities within the C-ITS, the approach enables the automation of processes, such as deployment, reconfiguration, update, upgrade, and scaling of microservices. Executing these processes on demand can, for example, reduce computing resource consumption and network traffic. A demand may include a request for provisioning an external supporting service, such as a collective environment model. The approach handles changing and new demands by dynamically reconciling them through our proposed application management framework built on Kubernetes and the Robot Operating System (ROS 2). We demonstrate the operation of our framework in the C-ITS use case of collective environment perception and make the source code of the prototypical framework publicly available at https://github.com/

Prompt engineering is crucial for achieving reliable and effective outputs from large language models (LLMs), but its design requires specialized knowledge of prompting techniques and a deep understanding of target tasks. To address this challenge, we propose a novel method that adaptively selects task-appropriate prompting techniques based on users' abstract task descriptions and automatically generates high-quality prompts without relying on pre-existing templates or frameworks. The proposed method constructs a knowledge base that associates task clusters, characterized by semantic similarity across diverse tasks, with their corresponding prompting techniques. When users input task descriptions, the system assigns them to the most relevant task cluster and dynamically generates prompts by integrating techniques drawn from the knowledge base. An experimental evaluation of the proposed method on 23 tasks from BIG-Bench Extra Hard (BBEH) demonstrates superior performance compared with standard prompts and existing automatic prompt-generation tools, as measured by both arithmetic and harmonic mean scores. This research establishes a foundation for streamlining and standardizing prompt creation, enabling non-experts to effectively leverage LLMs.

We propose a process to compress a pre-trained Vision Language Model into a ternary version of itself instead of training a ternary model from scratch. A new initialization scheme from pre-trained weights based on the k-means algorithm is proposed to reduce the ternarization time. We implement different custom operators for executing the ternary model on the TensorFlow Lite Engine. We compare the original model with its ternary and binary versions in terms of memory consumption, inference speed and perplexity. We find that the ternary model using our custom ternary matrix multiplication operator provides a good compromise in term of memory usage and perplexity, while having the fastest token generation speed.

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and generalizability. Recent research has sought to automate the generation and optimization of these workflows, but existing methods still rely on initial manual setup and fall short of achieving fully automated and effective workflow generation. To address this challenge, we reformulate workflow optimization as a search problem over code-represented workflows, where LLM-invoking nodes are connected by edges. The code will be available at https://github.com/geekan/MetaGPT. However, the rapid advancement of LLMs heavily relies on manually designed agentic workflows - structured sequences of LLM invocations accompanied by detailed instructions. Designing and refining these workflows requires significant human effort, which limits the scalability and adaptability of LLMs to new, complex domains and hinders their ability to transfer skills across diverse tasks (Tang et al., 2024). Recent efforts have focused on automating the discovery of effective agentic workflows to reduce the reliance on human intervention (Khattab et al., 2024; Yüksekgönül et al., 2024; Liu et al., 2023; Hu et al., 2024).

This is a key task in my opinion while writing code because you are not really doing regular typing when coding -- at least you shouldn't. What we are doing is going up & down in the file, copying some stuff, pasting it on top of something and changing some of the arguments etc. It involves much more repetitive tasks compared to writing a post or an essay. Hence, we can use shortcuts for those repetitive tasks. Start using Kite or Jedi or other code completion tools (Kite doesn't let new downloads at the moment for some reason though).

Facebook today unveiled a highly efficient, AI text-to-speech (TTS) system that can be hosted in real time using regular processors. In tandem with a new data collection approach, which leverages a language model for curation, Facebook says the system -- which produces a second of audio in 500 milliseconds -- enabled it to create a British-accented voice in six months as opposed to over a year for previous voices. Most modern AI TTS systems require graphics cards, field-programmable gate arrays (FPGAs), or custom-designed AI chips like Google's tensor processing units (TPUs) to run, train, or both. For instance, a recently detailed Google AI system was trained across 32 TPUs in parallel. Synthesizing a single second of humanlike audio can require outputting as many as 24,000 samples -- sometimes even more.