Automating AUTOSAR BSW Configuration Generation with Fine-Tuned LLMs and a Compact Intermediate Representation

RAG-Based Auto-Configuration for Industrial Fieldbus Devices

SysName, a production-oriented pipeline that automates device configuration end-to-end for Modbus RTU, OPC-UA, Profibus DP, and CANopen, builds a hybrid dense-sparse retrieval index augmented by an ontology graph derived from ECLASS, AAS, and SOSA/SSN, using a BGE-M3 encoder with a cross-encoder reranker to surface relevant manual passages.

An LLM-Based Framework for the Automatic Generation of SysML Models

Model-based systems engineering (MBSE) takes Systems Modeling Language (SysML) as the industrial standard modeling language, yet cloud Large Language Model (LLM)-based SysML generation faces limited domain data, model hallucinations, high hardware cost and confidential data leakage risks. This paper builds a 914-sample SysML PlantUML corpus and proposes a fully offline lightweight framework based on Qwen2.5-Coder-7B-Instruct, integrating 4-bit NF4 Quantized Low-Rank Adaptation (QLoRA) fine-tuning, vector-free Jaccard same-diagram reference retrieval and a three-round syntax correction loop. PlantUML executes syntax parsing while Graphviz only renders layouts. Tested on 131 samples covering five structural and behavioral SysML v1 diagram types, the plain-prompt baseline achieves word-set semantic F1 of 52.94%, and the retrieval-enhanced variant lifts the zero-retry syntax pass rate from 92.37% to 99.28%, with F1 slightly dropping to 50.64%. Running fully local without cloud data transmission, this pipeline offers a privacy-safe lightweight solution for SysML PlantUML modeling and does not support SysML-exclusive requirement or parametric diagrams.

BashCoder-R1: Towards Robust and Explainable Bash Script Generation with Robustness-Aware Group Relative Policy Optimization

Structural Validation of LLM-Generated Microservice Decompositions Using Source-Code Dependencies

The findings demonstrate that structural evaluations of LLM-generated decompositions should explicitly control for mapping coverage, as apparent differences between prompting strategies may otherwise reflect methodological bias rather than genuine architectural quality.

YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family

Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constraints absent from regular Transformer stacks. We propose YOLO-PEFT, a structure-aware framework that formulates adapter placement as an auditable constraint-planning problem. Given a detector graph, a PEFT request, and a resource budget, YOLO-PEFT assigns operator and semantic roles, evaluates explicit operator-validity, detector-semantic, graph-interface, and deployment predicates, records a reason code for each excluded module, and either emits a budgeted target-module plan or returns Refuse before training. Under the official VOC07+12 trainval-to-VOC07 test protocol, planner-selected RS-LoRA reaches 0.7138 and 0.7307 mAP50-95 on YOLO11s and YOLO12s, respectively, compared with 0.6428 and 0.6662 for Full-SFT. On RT-DETR-L, all seven evaluated LoRA-family configurations cross the predefined catastrophic threshold, supporting a calibrated Refuse-to-Full-SFT decision within the evaluated coverage. A controlled YOLO11 audit further shows that LoRA reduces peak training memory by 43.9 percent, although training takes 1.72 times longer. Within the evaluated detector families, placement policies, and calibration coverage, YOLO-PEFT replaces manual target-module trial and error with explicit, inspectable planning while preserving verified train-save-merge-export paths; refusal on unseen detector architectures remains an open validation problem. Project Page: github.com/Tencent/YOLO-Master

TSGen: Automated Troubleshooting Guide Generation

Maintaining up-to-date troubleshooting guides (TSGs) is critical for the reliability of cloud systems, yet manual maintenance often leads to inefficiencies and outdated documentation. This paper proposes TSGen, an automated pipeline for generating high-quality, structured TSGs from historical incident reports using large language models (LLMs). Our approach consists of three stages: (1) filtering and classifying incident data into diagnostically relevant categories, (2) distilling core incidents to ensure diversity and generalizability, and (3) organizing the distilled knowledge into a directed acyclic graph (DAG) that captures root causes and resolutions in a structured manner. By leveraging real-world incident discussions, TSGen produces dynamic and reusable guides tailored for live troubleshooting. Experiments on real-world incidents from Microsoft demonstrate that TSGen achieves 54.8% incident coverage and approximately 3× higher retrieval accuracy compared to baselines. Furthermore, the system supports iterative updates, allowing guides to evolve alongside dynamic cloud environments. Human evaluation shows that on-call engineers rate these generated TSGs significantly higher than human-crafted ones.