SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents
Authors: name: Yujiong Shen, name: Yajie Yang, name: Zhiheng Xi, name: Binze Hu, name: Huayu Sha, name: Jiazheng Zhang, name: Qiyuan Peng, name: Junlin Shang, name: Jixuan Huang, name: Yutao Fan, name: Jingqi Tong, name: Shihan Dou, name: Ming Zhang, name: Lei Bai, name: Zhenfei Yin, name: Tao Gui, name: Xingjun Ma, name: Qi Zhang, name: Xuanjing Huang, name: Yu-Gang Jiang

Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely overlook agents' ability to orchestrate tools for such rigorous workflows. To bridge this gap, we introduce SciAgentGym, a scalable interactive environment featuring 1,780 domain-specific tools across four natural science disciplines, supported by a robust execution infrastructure. Complementing this, we present SciAgentBench, a tiered evaluation suite designed to stress-test agentic capabilities from elementary actions to long-horizon workflows. Our evaluation identifies a critical bottleneck: state-of-the-art models struggle with complex scientific tool-use. Even for a leading model like GPT-5, success rates drop sharply from 60.6% to 30.9% as interaction horizons extend, primarily due to failures in multi-step workflow execution. To address this, we propose SciForge, a data synthesis method that models the tool action space as a dependency graph to generate logic-aware training trajectories. By fine-tuning on these trajectories, our SciAgent-8B outperforms the significantly larger Qwen3-VL-235B-Instruct while exhibiting positive cross-domain transfer of scientific tool-use capabilities. These results underscore the promising potential of next-generation autonomous scientific agents.