Ragexe - Llm-Rag Automation: Intelligent Function Execution and Secure System Orchestration

Dependency-Aware Reliable Orchestration for Smart-IoT Control with Reasoning-Aligned LLMs

Large Language Models (LLMs) offer a natural interface for smart-IoT control, yet reliable deployment requires more than producing valid API calls. Multi-device commands often contain preconditions, ordering constraints, and conflicts that must be satisfied before actions are safely executed. This paper presents DARIO, a dependency-aware orchestration framework for translating natural-language commands into verified IoT action plans. DARIO combines supervised instruction tuning, KL-regularized PPO, an explicit dependency graph $\mathcal{G}=(V, E)$, and a lightweight verifier that checks schema, execution, dependency, and safety constraints before commit. Verifier outcomes are folded into a decomposed reward, enabling the policy to learn from dependency violations rather than treating plans as flat token sequences. On a 2k-prompt held-out HA-Instruct split across five seeds, DARIO achieves $0.85 \pm 0.01$ JSON exact match, perfect schema validity, $0.91 \pm 0.01$ overall task success, and $0.81 \pm 0.02$ success on the dependency-heavy L3 split, outperforming SIT and SIT+PPO baselines by large margins. It also raises dependency satisfaction to 0.94, reduces unsafe execution to 0.02, and runs as a 4-bit 8B LoRA deployment at about 609 ms including verification.

CARE: Pre-Execution Command Verification for Shell-Executing LLM Agents

Large Language Model (LLM) agents are increasingly used for coding and terminal automation, making shell-command dispatch a high-stakes runtime control point. We study command-level pre-execution mediation for individual shell commands produced by LLM agents under bounded path context. Existing safeguards remain limited: generic guardrails do not model shell structure in sufficient detail, always-on LLM judges are relatively costly and variable, and shell parsers do not directly prevent harmful execution. We present CARE (Canonicalization, Attribution, and Resolution Engine), a shell-specific, static-first verifier for individual shell commands before execution. CARE canonicalizes generated commands into stable verification targets, derives deterministic evidence over syntax, command semantics, path context, and provenance-backed risk patterns, and escalates only underdetermined cases to an LLM judge. This design keeps the common case fast, reproducible, and auditable while reserving neural adjudication for borderline commands. On the balanced main split, CARE reaches 85.64% F1 with a 0.91% false-positive rate at 2.32 ms mean latency. When deployed in its static enforcement profile, CARE retains 84.99% F1 at 0.34 ms and reduces realised harm on RedCode-gen to 37.33%. Across external-generalization tests and controlled Docker-sandbox execution, these profiles expose a practical trade-off between benign recovery, false-positive burden, latency, and harm reduction. Overall, command-level shell mediation can reduce dispatch-boundary risk for LLM agents while preserving most benign workflows.

MASTEST: A LLM-Based Multi-Agent System For Testing RESTful APIs

Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a multi-agent system called MASTEST that combines LLM-based intelligent agents and programmed agents to automate REST API testing. They form a complete tool chain covering the whole workflow of REST API test with API specification in the OpenAPI Swagger format as the input. It also incorporates human testers in the process to review and correct LLM generated test artefacts to control the quality of testing activities. MASTEST is evaluated on two LLMs, GPT-4o and DeepSeek V3.1 Reasoner with five public APIs. Its performances on various testing activities are measured by a wide range of metrics, including adequacy and coverage metrics, the syntax and data type correctness of generated test scripts, the usability of LLM generated test cases and scripts, as well as the bug detection ability. Experiment results demonstrated that both DeepSeek and GPT-4o achieved a high overall performance but had strengths and weaknesses on different testing activities. MASTEST generated test cases achieved 94% and 98% unit test coverage and 79% and 78% system test coverage for GPT-4o and DeepSeek respectively in comparison with human designed test cases. The generated test scripts maintained 100% syntax correctness and only required minimal manual edits for semantic correctness. The generated test scripts contain assertions on the expected status code as well as contents in the response messages. They are highly capable of detecting bugs in the REST APIs. Experiment data shows that the bug detection rates are between 2.13 to 4.50 per operation. These findings indicate that MASTEST is highly efficient and effective.

MAGen: Multi-agent smart contract generation with automated testing and verification

TSAutoBox: A Localized Multi-Agent System for Industrial Time-Series Task Automation

LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications

A solver-grounded design principle is presented: a numerical result is reported only when it originates from a trusted tool and passes explicit verification, and a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency is proposed.