← Back to all news
arXiv Computation and Language By Koushikur Islam, Rodrigo N. Calheiros

Intent Engine: Natural-Language Intent Translation for Intent-Driven Orchestration in the Compute Continuum

Read the original on arXiv Computation and Language →

Intent Engine is a natural‑language intent translation architecture that converts user intents into validated Service‑level Objectives (SLOs) for compute‑continuum microservice placement. It combines schema‑constrained extraction, retrieval‑grounded value construction from monitored infrastructure, and validation against supported constraints to produce reliable SLO artifacts. In evaluations on a 716‑record dataset, Intent Engine outperformed prompting baselines and a rule‑based parser, achieving a 0.941 total F1 score with GPT‑4.1 mini and reducing downstream placement failures from 30.8% to 2.1%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Jul 14

A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery

arXiv:2607. 11138v1 Announce Type: new Abstract: The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thousands of options simultaneously.

By Prashant Devadiga, Abhishek, Adithya Mishra, Alok Singh, Amisha Sinha, Asit Desai, Gaurang Dahad, Harshit Bhushan, Mandati Pramod Reddy, Prakhar Gupta, Rupesh Patil, Siddhi Behere
llmsagentsbenchmarks
More like this →
arXiv AI
Aug 12

Conversational Orchestration for Organic 6G

arXiv:2608. 10714v1 Announce Type: cross Abstract: The Organic 6G vision of a network of networks spanning an edge-cloud continuum complemented by non-terrestrial resources requires, to realize its promise, service provisioning that is simple to operate, scalable across independently administered domains, and agile under domain churn (i.

By Masoud Shokrnezhad, Tarik Taleb
llmsagents
More like this →
arXiv AI
Sep 7

Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters

Atlas is a framework that optimizes the deployment of compound AI workflows on heterogeneous clusters by selecting execution plans that satisfy service level objectives (SLOs). It introduces MAP, a Markovian Accuracy Predictor, which estimates configuration accuracy using local conditional accuracy transitions between adjacent workflow stages, avoiding exhaustive end‑to‑end profiling. Atlas formulates plan selection as a mixed‑integer linear program, achieving near‑oracle accuracy while reducing deployment cost by up to 42% and profiling cost by up to 2.6×.

By Milos Gravara, Andrija Stanisic, Stefan Nastic
More like this →