# LLM Agents & Tool Use Domain: Artificial Intelligence · 265 papers indexed · newest 2026-10-29 Agents that plan and act: tool and function calling, computer-use and coding agents, multi-agent systems, agent benchmarks and agent protocols. Key concepts: LLM agent, language agent, tool use, function calling, computer use agent, software engineering agent, multi-agent, agent benchmark, SWE-bench ## Most influential - **[Augmenting large language models with chemistry tools](https://space-kitty.com/papers/doi:10.1038/s42256-024-00832-8)**. Andres M Bran, Sam Cox, Oliver Tobias Schilter et al. · 2024 · Nature Machine Intelligence (doi:10.1038/s42256-024-00832-8 · 792 citations). Source: > Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insec… > _Abstract excerpt by Andres M Bran et al., CC-BY, https://doi.org/10.1038/s42256-024-00832-8; truncated._ - **[Empowering biomedical discovery with AI agents](https://space-kitty.com/papers/arxiv:2404.02831)**. Shanghua Gao, Ada Fang, Yepeng Huang et al. · 2024 · Cell (arXiv:2404.02831 · doi:10.1016/j.cell.2024.09.022 · 315 citations). Source: > We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowle… - **[2004, “From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools](https://space-kitty.com/papers/openalex:W3037786853)**. Andrei V. Borshchev · 2011 (501 citations). Source: - **[CRISPR-GPT for agentic automation of gene-editing experiments](https://space-kitty.com/papers/arxiv:2404.18021)**. Yuanhao Qu, Kaixuan Huang, Ming Yin et al. · 2025 · Nature Biomedical Engineering (arXiv:2404.18021 · doi:10.1038/s41551-025-01463-z · 146 citations). Source: > The introduction of genome engineering technology has transformed biomedical research, making it possible to make precise changes to genetic information. However, creating an efficient gene-editing system requires a deep understanding of CRISPR technology, and the complex experimental systems under investigation. While Large Language Models (LLMs) have shown promise in various tasks, they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate an… - **[Large language models for reticular chemistry](https://space-kitty.com/papers/doi:10.1038/s41578-025-00772-8)**. Zhiling Zheng, Nakul Rampal, Théo Jaffrelot Inizan et al. · 2025 · Nature Reviews Materials (doi:10.1038/s41578-025-00772-8 · 134 citations). Source: - **[Biomni: A General-Purpose Biomedical AI Agent](https://space-kitty.com/papers/doi:10.1101/2025.05.30.656746)**. Kexin Huang, Serena Zhang, Hanchen Wang et al. · 2025 · bioRxiv (Cold Spring Harbor Laboratory) (doi:10.1101/2025.05.30.656746 · 124 citations). Source: > Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse b… > _Abstract excerpt by Kexin Huang et al., CC-BY, https://doi.org/10.1101/2025.05.30.656746; truncated._ - **[Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence Models for Diabetes Education](https://space-kitty.com/papers/doi:10.2337/dc23-0197)**. Gerald Gui Ren Sng, Joshua Yi Min Tung, Daniel Yan Zheng Lim et al. · 2023 · Diabetes Care (doi:10.2337/dc23-0197 · 160 citations). Source: - **[Demystifying LLM-Based Software Engineering Agents](https://space-kitty.com/papers/doi:10.1145/3715754)**. Chunqiu Steven Xia, Yinlin Deng, Soren Dunn et al. · 2025 · Proceedings of the ACM on software engineering. (doi:10.1145/3715754 · 115 citations). Source: > Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the fo… > _Abstract excerpt by Chunqiu Steven Xia et al., CC-BY, https://doi.org/10.1145/3715754; truncated._ - **[Parallel agent-based simulation with Repast for High Performance Computing](https://space-kitty.com/papers/doi:10.1177/0037549712462620)**. Nicholson T. Collier, MICHAEL J. NORTH · 2012 · SIMULATION (doi:10.1177/0037549712462620 · 215 citations). Source: - **[Large Language Model Enhanced Multi-Agent Systems for 6G Communications](https://space-kitty.com/papers/arxiv:2312.07850)**. Feibo Jiang, Yubo Peng, Li Dong et al. · 2024 · IEEE Wireless Communications (arXiv:2312.07850 · doi:10.1109/mwc.016.2300600 · 123 citations). Source: > The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task requirements to LLMs by nature language. However, directly applying native LLMs in 6G encounters various challenges, such as a lack of private communication data and knowledge, limited logical reasoning, evaluation, and refinement abilities. Integrating LLMs with the capabilities of retrieval, planning, memory, evaluation and reflection in agents can greatly enhance the potential of LLMs for 6G communications. To th… - **[Emergent Tool Use From Multi-Agent Autocurricula](https://space-kitty.com/papers/arxiv:1909.07528)**. Bowen Baker, Ingmar Kanitscheider, Todor Markov et al. · 2019 (arXiv:1909.07528 · 424 citations). Source: > Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build multi-object shelters using moveable boxes which in turn leads to agents discovering that they can over… - **[Optimizing generative AI by backpropagating language model feedback](https://space-kitty.com/papers/doi:10.1038/s41586-025-08661-4)**. Mert Yüksekgönül, Federico Bianchi, Joseph Boen et al. · 2025 · Nature (doi:10.1038/s41586-025-08661-4 · 85 citations). Source: - **[Survey of Agent Based Modelling and Simulation Tools](https://space-kitty.com/papers/doi:10.5286/dltr.2010007)**. RJ Allan · 2010 · Science and Technology Facilities Council (doi:10.5286/dltr.2010007 · 159 citations). Source: > Agent Based Modelling and Simulation is a computationally demanding technique having itsorigins in discrete event simulation, genetic algorithms and cellular automata. It is a powerful technique for simulating dynamic complex systems and observing "emergent" behaviour. There is currently a lot of interest in developing ABMs as a general computational technique applicable to the study of large scale systems. ABMs has been adapted to run on novel architectures such as GPGPU, e.g. nVidia hardware using the CUDA programming language. Argonne National Laboratory have a Web site on Exascale ABMs and… > _Abstract excerpt by RJ Allan, CC-BY, https://doi.org/10.5286/dltr.2010007; truncated._ - **[Large language models for chemistry robotics](https://space-kitty.com/papers/doi:10.1007/s10514-023-10136-2)**. Naruki Yoshikawa, Marta Skreta, Kourosh Darvish et al. · 2023 · Autonomous Robots (doi:10.1007/s10514-023-10136-2 · 112 citations). Source: > Abstract This paper proposes an approach to automate chemistry experiments using robots by translating natural language instructions into robot-executable plans, using large language models together with task and motion planning. Adding natural language interfaces to autonomous chemistry experiment systems lowers the barrier to using complicated robotics systems and increases utility for non-expert users, but translating natural language experiment descriptions from users into low-level robotics languages is nontrivial. Furthermore, while recent advances have used large language models to gene… > _Abstract excerpt by Naruki Yoshikawa et al., CC-BY, https://doi.org/10.1007/s10514-023-10136-2; truncated._ - **[Evaluating large language models and agents in healthcare: key challenges in clinical applications](https://space-kitty.com/papers/doi:10.1016/j.imed.2025.03.002)**. Xiaolan Chen, Jiayang Xiang, Shanfu Lu et al. · 2025 · Intelligent Medicine (doi:10.1016/j.imed.2025.03.002 · 75 citations). Source: ## Newest - **[Well-Specified and Well-Tested? Assessing SWE-bench-Style Datasets with a Self-Evolving Agent](https://space-kitty.com/papers/arxiv:2610.39417)**. Zhonghao Jiang, Xuyang Zheng, Yanfu Yan et al. · 2026 (arXiv:2610.39417). Source: - **[ATLAS-Bench: An Agentic Tool-Use Benchmark for Local Area Search](https://space-kitty.com/papers/arxiv:2610.39438)**. Keyun Zhang, Dapeng Yin, Fanfu Wang et al. · 2026 (arXiv:2610.39438). Source: - **[Soei: Structured Observability of Evidence and Intent for Multi-Agent AI Software Engineering](https://space-kitty.com/papers/arxiv:2610.39468)**. Mohammed Nayeem Khan, K S Sarun Kumar · 2026 (arXiv:2610.39468). Source: - **[Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?](https://space-kitty.com/papers/arxiv:2610.39497)**. Qi Wang, Yun Qu, Lizhou Cai et al. · 2026 (arXiv:2610.39497). Source: - **[Judging Before Propagation: Task-Graph Contextual Integrity for Multi-Agent Privacy](https://space-kitty.com/papers/arxiv:2610.39532)**. Hengyu Wu, Blaise Delattre, Yang Cao · 2026 (arXiv:2610.39532). Source: - **[LLM Agent-Based Design of RISC-V CPUs with FPGA-in-the-Loop Evaluation](https://space-kitty.com/papers/arxiv:2610.39570)**. Leo Itoh, Junichiro Kadomoto · 2026 (arXiv:2610.39570). Source: - **[Bayesian Offline Multi-Agent Reinforcement Learning with Latent World Models](https://space-kitty.com/papers/arxiv:2610.39576)**. Xi Xiong, Lu Liu, Yiming Li et al. · 2026 (arXiv:2610.39576). Source: - **[From Intent to Improvement: Agentic Dataset Search for Better Model Training](https://space-kitty.com/papers/arxiv:2610.39608)**. Qiaosheng Chen, Kaijia Huang, Fanqi Zhou et al. · 2026 (arXiv:2610.39608). Source: - **[Sticky Collusion: Persistence of LLM Agent Collusion after Incentive Removal and Partner Replacement](https://space-kitty.com/papers/arxiv:2610.39660)**. Joshua Adrian Cahyono · 2026 (arXiv:2610.39660). Source: - **[Can AI Agents Interface Clinicians and Complex Models?](https://space-kitty.com/papers/arxiv:2610.39681)**. Giulio Colombini, Matteo Falcioni, Mario Massimo et al. · 2026 (arXiv:2610.39681). Source: --- Space Kitty Archive · https://space-kitty.com · API: https://space-kitty.com/api/v1 · MCP: https://space-kitty.com/mcp · Premium: $0.20/request via x402 (Base Sepolia (testnet)). Metadata: arXiv (CC0), OpenAlex (CC0), Europe PMC (CC BY/CC0 works), ClinicalTrials.gov.