Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System

• URL: http://arxiv.org/abs/2404.17244v1
• Date: Fri, 26 Apr 2024 08:35:02 GMT
• Title: Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System
• Authors: Abdullatif AlShriaf, Hans-Martin Heyn, Eric Knauss,
• Abstract summary: This work introduces a tool for generating runtime configurations automatically from textual requirements stored as artifacts in git repositories (a.k.a. T-Reqs) alongside the software code. The tool leverages T-Reqs-modelled architectural description to identify relevant configuration properties for the deployment of artificial intelligence (AI)-enabled software systems.
• Score: 5.095988654970361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a tool for generating runtime configurations automatically from textual requirements stored as artifacts in git repositories (a.k.a. T-Reqs) alongside the software code. The tool leverages T-Reqs-modelled architectural description to identify relevant configuration properties for the deployment of artificial intelligence (AI)-enabled software systems. This enables traceable configuration generation, taking into account both functional and non-functional requirements. The resulting configuration specification also includes the dynamic properties that need to be adjusted and the rationale behind their adjustment. We show that this intermediary format can be directly used by the system or adapted for specific targets, for example in order to achieve runtime optimisations in term of ML model size before deployment.

Related papers

• REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry [0.0]
  Large Language Models (LLMs) enable new forms of agentic automation.<n>We present REGAL, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry.
  arXiv  Detail & Related papers  (2026-03-03T14:13:39Z)
• The Auton Agentic AI Framework [5.410458076724158]
  The field of Artificial Intelligence is undergoing a transition from Generative AI to Agentic AI.<n>This transition exposes a fundamental architectural mismatch: Large Language Models (LLMs) produce unstructured outputs, whereas the backend infrastructure they must control requires deterministic, schema-conformant inputs.<n>The present paper describes the Auton Agentic AI Framework, a principled architecture for the creation, creation, and governance of autonomous agent.
  arXiv  Detail & Related papers  (2026-02-27T06:42:08Z)
• OpenSage: Self-programming Agent Generation Engine [56.399761469404496]
  We propose OpenSage, the first agent development kit (ADK) to automatically create agents with self-generated topology and toolsets.<n>OpenSage offers effective functionality for agents to create and manage their own sub-agents and toolkits.<n>We believe OpenSage can pave the way for the next generation of agent development, shifting the focus from human-centered to AI-centered paradigms.
  arXiv  Detail & Related papers  (2026-02-18T21:16:29Z)
• Hardware Co-Design Scaling Laws via Roofline Modelling for On-Device LLMs [49.99513618431772]
  We propose a hardware co-design law that captures model accuracy and inference performance.<n>We empirically evaluate 1,942 candidate architectures on NVIDIA Jetson Orin.<n>Our architecture achieves 19.42% lower perplexity on WikiText-2.
  arXiv  Detail & Related papers  (2026-02-10T23:51:00Z)
• A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge [1.932555230783329]
  Lightweight, open-source Python framework designed to democratize the construction of LLM-driven autonomous agents.<n>AgentForge introduces three key innovations: (1) a composable skill abstraction that enables fine-grained task decomposition with formally defined input-output contracts, (2) a unified backend interface supporting seamless switching between cloud-based APIs and local inference engines, and (3) a declarative YAML-based configuration system that separates agent logic from implementation details.
  arXiv  Detail & Related papers  (2026-01-19T20:33:26Z)
• Monadic Context Engineering [59.95390010097654]
  This paper introduces Monadic Context Engineering (MCE) to provide a formal foundation for agent design.<n>We demonstrate how Monads enable robust composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities.<n>This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components.
  arXiv  Detail & Related papers  (2025-12-27T01:52:06Z)
• Zero-shot 3D Map Generation with LLM Agents: A Dual-Agent Architecture for Procedural Content Generation [8.398818816613806]
  We propose a training-free architecture that utilizes LLM agents for zero-shot PCG parameter configuration.<n>Our system pairs an Actor agent with a Critic agent, enabling an iterative workflow where the system autonomously reasons over tool parameters.
  arXiv  Detail & Related papers  (2025-12-11T10:22:02Z)
• Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
  We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
  arXiv  Detail & Related papers  (2025-08-12T09:45:19Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [72.44384066166147]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation [1.7819099868722776]
  We introduce a temporal feature encoding architecture called Time Series Representation Model (TSRM) for time series forecasting and imputation. The architecture is structured around CNN-based representation layers, each dedicated to an independent representation learning task. The architecture is fundamentally based on a configuration that is inspired by a Transformer encoder, with self-attention mechanisms at its core.
  arXiv  Detail & Related papers  (2025-04-26T09:53:20Z)
• ReXCL: A Tool for Requirement Document Extraction and Classification [3.888266374580385]
  The ReXCL tool automates the extraction and classification processes in requirement engineering. Performance evaluations indicate that ReXCL significantly improves efficiency and accuracy in managing requirements.
  arXiv  Detail & Related papers  (2025-04-10T08:46:54Z)
• Model-Driven Rapid Prototyping for Control Algorithms with the GIPS Framework (System Description) [0.0]
  We have created the GIPS (Graph-Based ILP Problem Specification) framework to support rapid prototyping of software systems. Developers can use our high-level specification language GIPSL (Graph-Based ILP Problem Specification Language) to specify their desired model optimization as sets of constraints and objectives. GIPS is able to derive executable (Java) software artifacts automatically that optimize a given input graph instance at runtime.
  arXiv  Detail & Related papers  (2025-03-26T11:52:52Z)
• Autonomous Deep Agent [0.7489814067742621]
  Deep Agent is an advanced autonomous AI system designed to manage complex multi-phase tasks. The system's foundation is built on our Hierarchical Task DAG framework. Deep Agent establishes a novel paradigm in self-governing AI systems.
  arXiv  Detail & Related papers  (2025-02-10T21:46:54Z)
• Adaptable Embeddings Network (AEN) [49.1574468325115]
  We introduce Adaptable Embeddings Networks (AEN), a novel dual-encoder architecture using Kernel Density Estimation (KDE) AEN allows for runtime adaptation of classification criteria without retraining and is non-autoregressive. The architecture's ability to preprocess and cache condition embeddings makes it ideal for edge computing applications and real-time monitoring systems.
  arXiv  Detail & Related papers  (2024-11-21T02:15:52Z)
• Control Industrial Automation System with Large Language Models [2.2369578015657954]
  This paper introduces a framework for integrating large language models with industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets.
  arXiv  Detail & Related papers  (2024-09-26T16:19:37Z)
• Towards Living Software Architecture Diagrams [0.0]
  We propose a tool that generates architectural diagrams for a software system by analyzing its software artifacts and unifying them into a comprehensive system representation. This representation can be manually modified while ensuring that changes are reintegrated into the diagram when it is regenerated.
  arXiv  Detail & Related papers  (2024-07-25T12:31:52Z)
• Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning [115.79349923044663]
  Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples.<n>Existing methods face a critical dilemma: static architectures rely on a fixed parameter space to learn from data that arrive sequentially, prone to overfitting to the current session.<n>In this study, we explore the potential of Selective State Space Models (SSMs) for FSCIL.
  arXiv  Detail & Related papers  (2024-07-08T17:09:39Z)
• Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow [3.2821049498759094]
  We propose a novel model- and feature-based approach to development of vehicle software systems. One of the key points of the presented approach is the inclusion of modern generative AI, specifically Large Language Models (LLMs) The resulting pipeline is automated to a large extent, with feedback being generated at each step.
  arXiv  Detail & Related papers  (2024-03-21T15:07:57Z)
• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• Ensemble-based modeling abstractions for modern self-optimizing systems [0.0]
  We extend our ensemble-based component model DEECo with the capability to use machine-learning and Industry optimizations in establishing and reconfiguration of component ensembles. We argue that machine-learning and optimizations is a key feature for modern smart systems which are to learn over the time and optimize their behavior at runtime to deal with uncertainty in their environment.
  arXiv  Detail & Related papers  (2023-09-11T21:01:11Z)
• PDSketch: Integrated Planning Domain Programming and Learning [86.07442931141637]
  We present a new domain definition language, named PDSketch. It allows users to flexibly define high-level structures in the transition models. Details of the transition model will be filled in by trainable neural networks.
  arXiv  Detail & Related papers  (2023-03-09T18:54:12Z)
• Policy Architectures for Compositional Generalization in Control [71.61675703776628]
  We introduce a framework for modeling entity-based compositional structure in tasks. Our policies are flexible and can be trained end-to-end without requiring any action primitives.
  arXiv  Detail & Related papers  (2022-03-10T06:44:24Z)
• Federated Learning with Heterogeneous Architectures using Graph HyperNetworks [154.60662664160333]
  We propose a new FL framework that accommodates heterogeneous client architecture by adopting a graph hypernetwork for parameter sharing. Unlike existing solutions, our framework does not limit the clients to share the same architecture type, makes no use of external data and does not require clients to disclose their model architecture.
  arXiv  Detail & Related papers  (2022-01-20T21:36:25Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization [23.52446054521187]
  Amazon SageMaker Automatic Model Tuning (AMT) is a fully managed system for black-box optimization at scale. AMT finds the best version of a machine learning model by repeatedly training it with different hyperparameter configurations. It can be used with built-in algorithms, custom algorithms, and Amazon SageMaker pre-built containers for machine learning frameworks.
  arXiv  Detail & Related papers  (2020-12-15T18:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.