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
The paper introduces SCX Router, a lightweight GLiClass-based model selector that assigns suitability scores to inference-time language models without autoregressive generation. It uses a 0.6B-parameter Qwen3 decoder with a shallow bidirectional scorer, preserving a text-only key–value cache across sessions and predicting task attributes such as type, difficulty, and expected output length. The authors build a comprehensive task ontology with 23 families, 115 types, and 1,173 synthetic examples, generating 150,000 verifier-scored tasks to train the router, which outperforms baseline models on LiveBench subsets with a top‑1 score of 0.707 versus 0.696 for the strongest fixed model.
By Ihor Stepanov, Aleksandr Smechov, Mykhailo Shtopko, Dmytro Vodianytskyi, Oleksandr Lukashov
The paper "Can You Check That? The Checkability Boundary for Local LLM Network Automation" proposes a method called Touchstone that uses local small language models (SLMs) to generate network‑automation candidates and applies task‑specific intrinsic checks to reject incorrect outputs before escalating to a larger frontier LLM. By defining a task as checkable when a cheap, deterministic test can reject outputs violating a necessary correctness condition, the authors demonstrate that Touchstone achieves high end‑to‑end accuracy (98.6% on conflict detection and 93.8% on intent translation) while escalating only a small fraction of inputs. The study shows that local inference is preferable when precise, low‑cost checks are available, whereas tasks lacking such checks should rely on the frontier model.
By Maleeha Masood, Momina Nofal
arXiv:2608. 06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests.
By Muhammad Adnan, Rohan Mahapatra, Prashant J. Nair, Daniel Berger, Pantea Zardoshti, Rodrigo Fonseca, Esha Choukse
arXiv:2609.01572v1 Announce Type: new
Abstract: Data-residency constraints force enterprises to self-host LLMs, but continuous adoption of newer models without decommissioning their predecessors expa...
By Olga Tsymboi, Dmitrii Stoianov, Ramil Latypov, Danil Taranets, Daniil Dryabin, Mikhail Gashkov, Viktor Zelenkovskiy, Aleksandr Fida, Gleb Alektorov, Nikita Gulyakov, Arthur Babkin, Aleksandr Medvedev, Pavel Gein, Anatolii Potapov
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 presents a unified evaluation of seven open reasoning language models across four benchmarks (ARC-Challenge, GSM8K, MATH levels 1–3, and TruthfulQA MC1) using a consistent 238-example subset and three prompting strategies (zero-shot, chain-of-thought, few-shot CoT). It reports not only accuracy but also Wilson confidence intervals, latency, VRAM usage, weighted aggregate performance, Pareto-efficient points, prompt-sensitivity, and compatibility diagnostics, revealing that Gemma-4-26B-A4B tops the weighted score while Gemma-4-E4B offers a strong practical trade-off. The study emphasizes that model rankings shift with prompting strategy and that deployment trade-offs remain crucial, advocating for a deployment-aware, multi-objective evaluation framework rather than a single-score leaderboard.
By Md Motaleb Hossen Manik, Ge Wang
System1-Arena is a benchmark that evaluates which decisions can be handled by System‑1 models—those that output probabilities in a single forward pass—versus System‑2 models that generate reasoning steps. It contains 92,000 questions across 105 tasks from 268 public sources, grouped into text classification, applied decisions, evaluation and control, and agent/action, and also includes 138 closed‑loop environments such as web shopping, coding control, robotics, games, and driving. The study tests 30 models, showing that large System‑1 models can match or exceed fast closed LLMs in non‑agentic accuracy with better‑calibrated probabilities at lower latency, while reasoning LLMs are most accurate but far more expensive, and large open LLMs perform best in closed‑loop settings.
By Tianyu Chen, Xinyang Liu, Ruyi An, Zhendong Wang, Mingyuan Zhou
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:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.
By Anders Vestrum, Arya Raeesi, Hanna Roed
The paper investigates how small language models (SLMs) perform in knowledge graph question answering (KGQA) when evaluated on the reasoning paths they take, rather than just the final answer. Using the THESEUS navigation and traceability framework, the authors test frozen, off‑the‑shelf SLMs as local action policies that choose graph actions and decide when to stop, without any task‑specific training or free‑form answer generation. By measuring both Hits@1 and Path Edit Distance (PED) across the Kinship and MQuAKE‑ST datasets, the study finds that models vary significantly in both answer accuracy and path fidelity, and that prompting can either help or hurt navigation depending on the model.
"whyItMatters":"The results show that evaluating SLMs solely on endpoint accuracy can be misleading, highlighting the need to assess reasoning path fidelity in KGQA tasks."
By Eduin E. Hernandez, Sergio A. Diaz, Luis F. Garcia, Nurassyl Askar, Stefano Rini
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.
By Pranshav Gajjar, Vijay K Shah