Smart Prompt Analyzer and Recommendation System: An Ollama-Based Local LLM Platform for Prompt Quality Analysis and Optimization

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

RLMOpt is introduced, a prompt optimizer that makes the search policy itself language-model-driven through a recursive language model (RLM), which operates over a tool-based environment, inspecting task information, analyzing failures, generating candidates, allocating evaluation budget, and deciding when to stop.

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

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.

PromptSentinel-X: A Leakage-Aware and Context-Aware Framework for Prompt-Injection Detection in Large Language Model-Powered Web Agents

Large language model (LLM)-powered web agents combine privileged instructions with user requests, webpages, retrieved documents, tool outputs, memory, and conversation history, creating prompt-injection risks that static text classification may not capture. This study presents PromptSentinel-X, a leakage-aware and context-aware screening framework. The primary benchmark contained 1581 English records from the Prompt Injection Malignant dataset; 30,015 external prompt-injection, jailbreak, and benign hard-negative records were reserved for transfer and stress testing. The framework uses prompt-family-aware partitioning, trusted–untrusted context segmentation, calibrated risk prediction, robustness analysis, and deployment-oriented routing. On a 465-record group-aware test set, PromptSentinel-X achieved 0.9849 accuracy, 0.8887 macro-F1, 0.9851 attack recall, a 0.0050 benign false-positive rate, 0.9971 area under the receiver operating characteristic curve (AUROC), 0.9836 area under the precision-recall curve (AUPRC), and 0.0153 expected calibration error. Random splitting produced a higher baseline macro-F1 but 144 leakage warnings. Context-aware macro-F1 declined from 0.8301 for static prompts to 0.6306 for multi-turn, 0.3844 for retrieval-augmented generation, and 0.3200 for memory scenarios. PromptSentinel-X is positioned as a calibrated screening and escalation component, not a stand-alone authorization mechanism. Broader tool, memory, multimodal browser, multilingual, and end-to-end agent studies remain necessary.

Prompt Template-Driven Large Model SQL Generation

What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering

Prompts stopped being isolated strings some time ago. In real systems, one model call feeds another, retrieval interleaves with generation, routers branch, and aggregators merge parallel results. Practice converged on a single structure to hold this together: the graph. Frameworks such as LangGraph, DSPy, and Prompt Flow expose it openly, and research systems already optimize it automatically. The vocabulary, however, lags behind. Graph names, variously, a reasoning topology inside one sampling strategy, a multi-agent conversation, or an orchestration artifact, while prompt engineering still evokes writing one good string. What is missing is a reference definition treating prompts as nodes of an explicit, executable, improvable graph. We build that definition through conceptual analysis over sources with persistent identifiers, complemented by primary grey literature. We reconstruct the genealogy of the idea, from dataflow graphs and build systems, through prompt chaining and the thought topologies (chain, tree, graph), to graphs compiled and optimized as artifacts. We then propose a constitutive definition of prompt graph engineering, state its four conditions (explicit structure, separation between structure and prompt content, executable semantics, and the graph as a first-class engineering artifact), and operationalize them as an inclusion and exclusion test. We draw the boundary against six neighboring concepts and apply the test to six real systems (LangGraph, DSPy, Prompt Flow, AutoGen, CrewAI, and Claude Code subagents); it includes and excludes consistently. We close with a research agenda organized along four design tension axes. The contribution is an operational definition and a shared vocabulary for a practice that industry already exercises daily without naming precisely.

A Study of Cursorrules Files in GitHub Open Source Projects

An empirical study of configuration prompt files in Cursor, a widely used AI-assisted code editor, shows that .cursorrules files emerged rapidly from mid-2024 and shows that there is a continuity of themes and topics between the now-legacy .cursorrules files and the current standard .mdc files.