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
The paper introduces GPlan, a generative framework for Generative Spatiotemporal Intent Sequence Recommendation (GSISR) that integrates large language model reasoning into lightweight models. It employs Progressive Implicit CoT Distillation to compress reasoning into latent tokens for low-latency inference, and Spatiotemporal Counterfactual DPO to align plans with real-world constraints, improving sequence coherence and context responsiveness. Experiments and online A/B tests confirm the effectiveness of these techniques, and the authors provide an implementation and dataset on GitHub.
By Sicong Wang, Ruiting Dong, Yue Liu, Bowen Zheng, Jun Meng, Jie Li, Shuaijun Guo, Yu Gu, Fanyi Di, Xin Li
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:2601.06640v2 Announce Type: replace
Abstract: The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level o...
By Genze Jiang, Kezhi Wang, Xiaomin Chen, Yizhou Huang
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
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
arXiv:2608. 03311v1 Announce Type: cross Abstract: Enterprise compliance management requires rapid adaptation to evolving regulatory frameworks (e.
By Xavier Wrenn, Radoslav Raykov, Aleksandar Angelov, Hirokuni Kitahara, Yuji Watanabe, Anca Sailer