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
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
The paper introduces a negotiation‑aware hybrid LLM framework that interprets natural‑language intents and discovers cloud services. It refines incomplete or conflicting intents, converts them into a provider‑independent TOSCA template, and retrieves candidate services via deterministic matching. Conflicts detected during interpretation or discovery are resolved through iterative natural‑language negotiation with the user, improving recall, F1, and eliminating hallucinated offers.
By Wided Meflah, Hayet Brabra, Walid Gaaloul
IntentCoding is a decoding strategy that amplifies user intent in large language model code generation by masking the intent and applying a multi‑strength ensemble mechanism. It is model‑agnostic, requires no extra training, and integrates with existing decoding procedures. Experiments on the new CodeConstraints benchmark and other datasets show significant improvements in constraint satisfaction and functional correctness, with up to 71.0% relative gains on CodeConstraints and 29.3% on HumanEval and LiveCodeBench compared to greedy decoding.
By Zheng Fang, Yihong Dong, Lili Mou, Dongming Jin, Zhi Jin, Ge Li
arXiv:2606. 03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge.
By Louis Nisiotis, Aimilios Hadjiliasi
The paper introduces LocalIntent, a structured generation framework that routes smart building automation requests to either ordinary or persistent-rule branches using locally deployed large language models. LocalIntent improves outcome-specific prompting, yielding higher accuracy in intent understanding, device identification, and action/parameter correctness compared to generic two-stage prompting, especially for rule-based requests. Experiments on Gemma and Qwen models (2B‑9B) across 101 non‑persistent and 54 persistent‑rule records demonstrate significant performance gains in rule-mode correctness and overall intent handling.
By Tianyang Liu, Jiayue Wang, Zhanhao Cheng, Changyang Wang, Shaoshu Zhu, Zhenghao Wu, Hadi Tabatabaee Malazi