UPPAAI extends the UPPAAL tool by integrating large language models (LLMs) to assist users in constructing, explaining, debugging, and specifying models through dialogue. The system follows a workflow where the user requests, the LLM proposes and explains, and UPPAAL performs verification, exposing its editor, queries, and verifier via a Tools API. Seven case studies demonstrate UPPAAI’s effectiveness across timed automata, stochastic hybrid automata, and strategy learning, showing that LLMs not only generate solutions but also proactively suggest relevant UPPAAL features.
arXiv:2608. 07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc.
The paper introduces LLMON, an LLM-native markup language designed to encode the structure and semantic metadata of textual inputs for large language models. By distinguishing components such as instructions and data within prompts, LLMON aims to reduce model confusion and mitigate security risks like prompt injection. The authors outline design requirements, present the language, and provide preliminary evidence that incorporating LLMON can improve model accuracy, safety, and security during training and inference.
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
arXiv:2606. 13220v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as interactive assistants for technical problem solving.
arXiv:2606. 19588v1 Announce Type: new Abstract: Formal tools such as SAT and SMT solvers are increasingly embedded in language model reasoning pipelines when a safety or security critical question can be formulated in logic.
arXiv:2603. 02070v3 Announce Type: replace Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise.
arXiv:2606. 15943v1 Announce Type: cross Abstract: Engineering LLM-native software remains a challenging and immature field.
Large language models (LLMs) are increasingly used as interactive assistants for technical problem solving. However, when users provide incomplete descriptions or plausible but unverified explanations, LLMs may prematurely align with these assumptions and propose solutions before collecting sufficient evidence.
The paper reviews and categorizes large language models that combine step‑by‑step reasoning with interactive search, treating retrieval as an action within an iterative agentic loop. It analyzes these systems along four architectural axes—retrieval coupling, controller design, knowledge source, and grounding strictness—while also considering the learning signal as a cross‑cutting factor. The authors conduct controlled ablations and benchmark analyses to clarify how design choices affect search, stopping, and grounding behavior, aiming to provide a unified perspective for future research.
The paper introduces a hierarchical feedback framework that guides large language models (LLMs) to produce executable plans for cyber‑physical systems (CPSs) from natural language commands. It prompts LLMs to generate both a structured task plan and signal temporal logic (STL) constraints, then uses a sound algorithm to detect contradictions and provide feedback for plan revision. At execution time, diagnostic information from failed control synthesis—such as violated constraints and conflicting component assignments—is fed back to the LLM to enable task replanning, achieving an 87.9% success rate on seven heterogeneous CPS scenarios.
The paper introduces a new benchmark that evaluates large language models (LLMs) on their agentic mathematical reasoning rather than just final answers. It aligns problem‑solving behaviors with a taxonomy of reusable mathematical atomic capabilities and includes planning, action, and feedback tasks in both textual and multimodal settings. Experiments show that models with similar end‑to‑end accuracy can have very different agentic profiles, highlighting the importance of process‑level evaluation.
AutoLR is an autonomous harness designed to streamline the iterative research‑and‑engineering cycle for industrial recommender systems, exemplified by NetEase’s gaming‑community app DASHEN. It integrates a multi‑expert council for adversarial review, a deterministic evidence‑weighted selector to allocate trial budgets, and a layered knowledge system that fuses external research with domain‑specific insights and empirical evidence. Large language model agents handle semantic reasoning and code generation, while deterministic controllers maintain control over execution, metrics, guardrails, and state management.