Poster

SciAgentGym: Benchmarking Multi-Step Scientific Tool-Use in LLM Agents

Yujiong Shen ⋅ Yajie Yang ⋅ Zhiheng Xi ⋅ Binze Hu ⋅ Huayu Sha ⋅ Qiyuan Peng ⋅ Jiazheng Zhang ⋅ Junlin Shang ⋅ Jixuan Huang ⋅ Yutao Fan ⋅ Jingqi Tong ⋅ Shihan Dou ⋅ Ming Zhang ⋅ LEI BAI ⋅ Zhenfei Yin ⋅ Tao Gui ⋅ Xingjun Ma ⋅ Qi Zhang ⋅ Xuanjing Huang ⋅ Yu-Gang Jiang

Abstract

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.