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%.
By Koushikur Islam, Rodrigo N. Calheiros
arXiv:2610.33007v1 Announce Type: new
Abstract: Natural-language service intents in integrated terrestrial and non-terrestrial (TN/NTN) 6G networks can be incomplete, ambiguous, or inconsistent, maki...
By Wajdi Hammami, Irfan Azam, Soumaya Cherkaoui
Intent2Tc is a closed‑loop, language‑model‑driven framework that translates high‑level business traffic‑shaping intents into executable Linux traffic‑control (tc) configurations. It uses an AQM‑based digital twin semantic model, automated metadata extraction, critique‑driven refinement, and Retrieval‑Augmented Generation to improve semantic consistency and configuration reliability. Evaluation on 100 RFC 9315‑compliant intents shows high semantic fidelity and deployment readiness, with Claude Sonnet‑4.6 achieving 0.98 semantic similarity and 0.045 normalized edit distance, while RAG reduces token consumption and latency for compact models.
By Andrea Masini, Sudipta Acharya, Paolo Bellavista, Luca Foschini, Burak Kantarci
The paper introduces a method to improve large language model agents that invoke enterprise APIs by embedding process and schema constraints into a typed property graph. By integrating OData metadata and BPMN reference processes, a hybrid retriever can identify correct endpoints and follow process edges, boosting Recall@3 for low lexical overlap queries and enabling more reliable multi-step service invocation. Experiments on 42 enterprise services and 81 multi-step queries demonstrate significant gains over baseline approaches, with a marked reduction in invalid API calls.
By Saiful Khan, Afrah Farea
arXiv:2608.29543v1 Announce Type: cross
Abstract: Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, oft...
By Yujia Liu, Jiayan Lin, Zijin Hong, Zheng Yuan, Shengyuan Chen, Hao Chen, Qinggang Zhang, Xiao Huang, Feiran Huang
The paper introduces INTENT-AS-A-TOOL, a method that equips large language models with intent-targeted tools to provide a fine-grained, judge‑free signal of their commitment to specific behaviors during reasoning. By monitoring the probability of calling these intent tools, the authors can track how intent evolves throughout generation, complementing chain‑of‑thought monitoring and expanding post‑hoc labels into dense trajectories. The approach identifies critical steps for online intervention, demonstrating that action preferences are useful for detecting agentic misalignment in autonomous agents.
By Yutong Zhang, Jianshuo Dong, Peng Xu, Long Wang, Jie Zhang, Tianwei Zhang, Xiaoping Zhang, Han Qiu