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%.
arXiv:2606. 03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge.
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.
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.
arXiv:2608. 03311v1 Announce Type: cross Abstract: Enterprise compliance management requires rapid adaptation to evolving regulatory frameworks (e.
arXiv:2606. 24063v1 Announce Type: cross Abstract: Modern spoken language understanding (SLU) systems are increasingly deployed in real-world settings, where specific functionalities may need to be removed due to policy or safety constraints.
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×.
arXiv:2606. 15199v1 Announce Type: new Abstract: Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints.
Microservice architectures have become dominant for modernizing monolithic systems, yet identifying appropriate services remains challenging and largely manual. Existing decomposition approaches are predominantly code-centric, limiting applicability in early design stages where only textual requirements are available.
UniACE is a unified framework that standardizes the evaluation of large language model (LLM) agents by representing each benchmark as an instruction–tool–environment triplet and running models through a shared, task‑agnostic harness in isolated runtimes. It preserves native success criteria, offers an offline mode for dynamic‑resource tasks, and standardizes efficiency metrics, execution records, and failure attribution. Applying UniACE to 7 benchmarks across 24 domains and 15 models revealed significant score shifts, ranking reversals, and sensitivity to evidence representation, highlighting the impact of evaluation configuration on reported agent performance.
The paper introduces a Symbolic-RAG-Generative architecture called GRACE for goal‑oriented conversational systems. GRACE transforms business intent into a fixed objective set and uses a constrained policy to update the conversation state based on visitor‑authored evidence while ensuring visitor utility. Evaluation on real‑estate and professional‑cleaning dialogues shows high accuracy in state transitions, evidence precision/recall, and monotonicity.
arXiv:2603. 04444v3 Announce Type: replace-cross Abstract: As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- selecting the right model for each query at inference time -- has become a critical systems challenge.
The paper introduces CRAFT, a data‑centric fine‑tuning approach that aligns small language models (SLMs) for pre‑hoc reasoning in AI‑native 6G radio access networks (RAN). By automatically generating verified (input, trace, label) triplets and fine‑tuning with low‑rank adaptation, CRAFT achieves high accuracy and F1 scores on TRACTOR and IC xApp datasets while avoiding parse failures that plague RL methods like GRPO. It also reduces energy consumption by 59% compared to GRPO baselines, offering a more sustainable path to auditable AI in 6G RAN.