# Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM arXiv: https://arxiv.org/abs/2503.10071 ยท HTML: https://mlsanity.com/paper/2503.10071/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. - Builds on 26 of its 37 references that are in the map. - First posted on arXiv 13 Mar 2025. ## Placed in - LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/) ## Builds on (most cited first, 26 of 26) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021) - [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024) - [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022) - [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://mlsanity.com/paper/2209.14610/) (2022) - [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023) - [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023) - [Large Language Models as Tool Makers](https://mlsanity.com/paper/2305.17126/) (2023) - [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024) - [AutoAgents: A Framework for Automatic Agent Generation](https://mlsanity.com/paper/2309.17288/) (2023) - [CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets](https://mlsanity.com/paper/2309.17428/) (2023) - [Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies](https://mlsanity.com/paper/2502.02533/) (2025) - [Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools](https://mlsanity.com/paper/2502.04644/) (2025) - [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://mlsanity.com/paper/2305.06849/) (2023) - [CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models](https://mlsanity.com/paper/2305.14318/) (2023) - [Tuning Language Models by Proxy](https://mlsanity.com/paper/2401.08565/) (2024) - [Learning to Use Tools via Cooperative and Interactive Agents](https://mlsanity.com/paper/2403.03031/) (2024) - [Studying LLM Performance on Closed- and Open-source Data](https://mlsanity.com/paper/2402.15100/) (2024) - [Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons](https://mlsanity.com/paper/2502.02988/) (2025) - [ToolFactory: Automating Tool Generation by Leveraging LLM to Understand REST API Documentations](https://mlsanity.com/paper/2501.16945/) (2025) - [Multi-Agent Combinatorial Contracts](https://mlsanity.com/paper/2405.08260/) (2024) ## Built on by (most cited first, 8 of 8) - [Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents](https://mlsanity.com/paper/2509.26354/) (2025) - [CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution](https://mlsanity.com/paper/2512.23880/) (2025) - [ReCreate: Reasoning and Creating Domain Agents Driven by Experience](https://mlsanity.com/paper/2601.11100/) (2026) - [Self-Improvements in Modern Agentic Systems: A Survey](https://mlsanity.com/paper/2607.13104/) (2026) - [Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails](https://mlsanity.com/paper/2510.04860/) (2025) - [TinyLLM: Evaluation and Optimization of Small Language Models for Agentic Tasks on Edge Devices](https://mlsanity.com/paper/2511.22138/) (2025) - [El Agente Forjador: Task-Driven Agent Generation for Quantum Simulation](https://mlsanity.com/paper/2604.14609/) (2026) - [Is Bash All You Need? An Empirical Study of Tool Interfaces for Enterprise Digital Worker Agents](https://mlsanity.com/paper/2609.11999/) (2026) ## Abstract (arXiv) The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality; the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability; and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving.