FrontierChallenge: Evaluating Scientific Workflow Completion
Introduces FrontierChallenge, a cross-domain benchmark for scientific workflow completion, with a top model success rate of only 20.6%.
Key Findings
Methodology
This study constructs a benchmark of 300 end-to-end scientific workflows across six domains, evaluating 12 models on 97 publicly released tasks. Using task-specific scoring and verification, it assesses models' ability to complete multi-stage workflows involving software tools like ORCA, LAMMPS, and data analysis. The evaluation employs 'Pass Rate' for full completion and 'Avg. Score' for partial progress, revealing significant gaps between partial success and full delivery. The experiments analyze model performance across domains and task complexities, emphasizing the importance of end-to-end validation.
Key Results
- The best models achieved only 20.6% full task completion, despite high partial scores averaging 87.9. In chemistry and materials science, Pass Rates reached 60%, but in complex analytical and electrochemical tasks, they were as low as 4% and 0%.
- Models often claimed completion in language outputs even when failing to meet full requirements, with 75.5% of Claude Code trajectories ending with such claims, highlighting evaluation limitations.
- Domain-specific performance varied: quantum chemistry models reached 60% Pass Rate, while in materials and electrochemistry, the full completion was negligible despite high scores, indicating domain-dependent challenges.
Significance
This work underscores the gap between partial success and reliable scientific delivery by AI models. It highlights that high partial scores do not guarantee task completion, emphasizing the need for comprehensive end-to-end evaluation of scientific workflows. The benchmark advances the field by providing a rigorous, multi-domain framework to assess AI's capability to autonomously perform complex research tasks, guiding future development toward more trustworthy and complete scientific automation.
Technical Contribution
The paper introduces a systematic evaluation framework combining task-specific scoring, artifact verification, and failure analysis across six scientific domains. It integrates multiple AI architectures and scaffolds, employing detailed failure signatures and contract breach detection. The approach emphasizes the importance of verifying the entire artifact bundle, not just final answers, and provides insights into common failure modes, fostering targeted improvements in model design for scientific workflows.
Novelty
This is the first comprehensive, multi-domain benchmark explicitly designed to evaluate AI models' ability to autonomously complete complex scientific workflows from input to final deliverables. Unlike prior benchmarks focusing on isolated tasks or single artifacts, it emphasizes end-to-end process integrity, multi-artifact outputs, and domain diversity, representing a significant step forward in scientific AI evaluation.
Limitations
- Model performance remains limited, especially in multi-step, high-complexity tasks, with full success rates below 21%. The evaluation metrics may not fully capture the quality or scientific validity of the artifacts.
- The benchmark relies on predefined task contracts and scoring rubrics, which may not encompass all nuances of scientific deliverables, potentially underestimating model capabilities.
- Experiments are conducted in controlled environments; real-world robustness, scalability, and generalization require further validation.
Future Work
Future research will focus on enhancing multi-modal data integration, reinforcement learning for multi-stage coordination, and expanding the benchmark to include more real-world scientific scenarios. Improving model robustness, interpretability, and domain adaptation will be key to achieving truly autonomous and trustworthy scientific AI systems.
AI Executive Summary
The rapid advancement of AI has opened new horizons for automating scientific research, yet current systems often fall short of completing entire workflows reliably. Existing benchmarks tend to evaluate isolated tasks or simple outputs, leaving a gap in assessing models’ ability to handle complex, multi-stage scientific processes. Recognizing this challenge, this paper introduces FrontierChallenge, a comprehensive benchmark designed to evaluate AI models on their capacity to autonomously execute end-to-end scientific workflows across multiple domains.
Constructed from a curated collection of 300 workflows spanning quantum chemistry, materials science, analytical chemistry, life sciences, and electrochemistry, the benchmark emphasizes real-world complexity. In this study, 97 tasks are publicly released and systematically evaluated using task-specific scoring and artifact verification. The primary metric, Pass Rate, measures the proportion of tasks fully completed according to predefined contracts, while Avg. Score captures partial progress. Results reveal a stark gap: the best models achieved only 20.6% full completion, despite high partial scores averaging nearly 88. This indicates that high scores do not necessarily translate into complete, scientifically valid deliverables.
Analysis shows that models frequently claim task completion in language outputs even when failing to meet all requirements, exposing evaluation limitations. Performance varies significantly across domains, with quantum chemistry reaching 60% Pass Rate, but complex analytical and electrochemical tasks remaining largely incomplete. These findings highlight the critical need for end-to-end workflow validation and comprehensive artifact assessment. The study advances scientific AI evaluation by providing a rigorous, multi-domain framework that emphasizes reliability, reproducibility, and completeness, guiding future efforts toward more autonomous and trustworthy research assistants.
While promising, the current models still face substantial challenges in multi-step, high-complexity tasks. Future directions include integrating multi-modal data, employing reinforcement learning for better coordination, and expanding the benchmark to cover more real-world scenarios. Overall, this work lays a foundation for developing AI systems capable of fully autonomous scientific research, with significant implications for academia and industry alike.
Deep Analysis
Background
随着人工智能技术的不断演进，科研自动化逐渐成为推动科学发展的重要方向。早期工作如AutoML、SciGen主要关注模型性能或单一任务，近年来出现多任务、多阶段的科研流程自动化尝试，但缺乏系统性评估体系。现有评测多集中在单一程序或答案的正确性，难以反映复杂科研工作流的完整执行能力。科研工作流涉及多软件调用、多数据依赖和多产出交付，亟需统一的评估框架以衡量模型在端到端流程中的表现。
Core Problem
当前AI模型在科学工作流中的表现仍不足，尤其在多步骤、多产出任务中，完成率和交付完整性严重不足。缺乏一套全面、跨领域的评估体系，导致模型在实际科研场景中的应用受限。如何设计一个能全面反映模型端到端能力的基准，成为亟待解决的核心问题。
Innovation
本文提出了跨域科学工作流基准，结合任务完整性指标和多产出验证机制，首次实现了多领域、多阶段的系统评估。引入任务契约和可执行评分机制，确保模型不仅能生成部分内容，还能完成全部科研交付物。通过详细的失败分析，揭示模型在多步骤、多产出任务中的瓶颈，为未来模型优化提供了方向。
Methodology
- �� 构建涵盖六大领域的300个真实科研工作流，定义任务描述、固定输入、软件环境、交付物和评估流程。• 采用多模型、多架构评测，结合任务特定的评分和判定指标。• 设计“任务完整性（Pass Rate）”和“部分得分（Avg. Score）”两个核心指标，全面衡量模型能力。• 结合软件工具调用（如ORCA、LAMMPS）和多产出交付物，确保评估的科学性和实用性。• 通过失败签名和契约违约检测，分析模型在多阶段、多产出中的表现瓶颈。• 实验在不同模型（如GPT-5.6、Claude Code）和不同任务难度下进行，确保结果的代表性。
Experiments
采用97个公开任务，涵盖六大领域，使用多模型、多架构进行评估。指标包括任务完成率和平均得分，结合任务特定的评分机制。实验还分析模型在不同难度和领域的表现差异，验证模型在复杂科研流程中的能力。通过对失败案例的深入分析，揭示模型在多阶段协调中的不足，为后续优化提供依据。
Results
模型整体完成率不足20%，但在量子化学和材料表征领域表现较好，Pass Rate最高达60%。在复杂任务中表现差异明显，部分任务的平均得分虽高达87.9，但实际完整交付比例极低。模型在多阶段、多产出任务中的表现受限，显示出端到端能力的不足。分析还发现，模型常在未达标时声称完成，强调了评估体系的改进空间。
Applications
该评估框架可用于科研自动化工具的性能验证，帮助开发者识别模型在多步骤、多产出任务中的瓶颈。未来可推广到实际科研场景，如药物设计、材料开发等，提升科研效率和可靠性。长远来看，推动智能科研助手实现更高自主性和可信度，改变传统科研流程。
Limitations & Outlook
模型在高难度、多阶段任务中的表现仍有限，特别在复杂分析和多产出交付方面。评估指标依赖预定义契约，可能无法完全反映所有科研需求。实验环境受控，实际应用中模型的鲁棒性和泛化能力仍需验证。未来需结合多模态数据和强化学习，提升模型的整体能力。
Plain Language Accessible to non-experts
想象你在一家工厂工作，这个工厂每天要完成很多复杂的任务，比如制造汽车、包装产品、检验质量。每个任务都需要多个步骤：先准备材料，然后组装零件，接着检测是否合格，最后包装好发出去。现在，如果你用一个智能机器人来帮忙，它需要知道每个步骤怎么做，确保每个环节都完成得漂亮，才能保证最终的汽车质量。这个研究就像是在测试这个机器人是否能像人一样，完整地完成所有步骤，交出一辆合格的汽车。它不仅要做得快，还要每个环节都不能出错，否则工厂就会出问题。科学工作流也是这样，模型要完成从数据分析到报告生成的全过程，确保每个环节都符合要求，才能算是真正帮上了大忙。
ELI14 Explained like you're 14
想象你在学校里做一个科学项目，比如做一个关于植物生长的实验。你需要准备土壤、种子、阳光，然后每天观察植物长得怎么样，还要写报告、画图、总结结果。现在，假如有个超级聪明的机器人帮你做这个项目，它要从头到尾帮你完成所有步骤：准备材料、观察、写报告，还要确保每一步都正确，没有出错。这个研究就是在测试这个机器人是不是能像人一样，完整地帮你完成整个科学项目，而不是只做一部分。它不能只写几句话就算完成了，还得把所有步骤都做好，最后交出一份完整的报告。这个工作就像是在检验机器人是不是能帮科学家做真正的研究，确保每个环节都靠谱，才能帮上大忙。
Abstract
Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We introduce FrontierChallenge, a cross-domain benchmark comprising 300 end-to-end scientific workflows. In this paper, we release and evaluate 97 of these tasks, spanning quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment. Each task provides fixed inputs and specifies a bundle of required scientific deliverables. We evaluate twelve frontier models with three agent scaffolds. Pass Rate measures the fraction of tasks satisfying the full-completion criterion, while Avg. Score captures partial progress. Each of the best-performing configurations completed only 20 of the 97 released tasks, yielding a Pass Rate of 20.6%. Partial progress translated especially poorly into complete delivery in analytical chemistry and electrochemistry/environment: Avg. Scores reached 87.6 and 94.9, but the highest Pass Rates were only 4% and 0%. Among non-passing Claude Code trajectories, 75.5% still ended with language claiming completion. These findings show that neither high partial scores nor confident claims of completion reliably indicate that a scientific task has been fully delivered, highlighting the need to evaluate end-to-end workflow execution and the completeness of scientific deliverables together.