AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.

翻译：AI编码智能体越来越多地被用于科学研究工作，但其端到端的自主研究能力仍难以验证。我们提出ResearchClawBench，这是一个涵盖10个科学领域、包含40项任务的自主科学研究评估基准。每项任务均基于一篇已发表的真实论文，提供相关文献与原始数据，并在评估过程中隐藏目标论文。专家策划的多模态评分标准将目标科学成果分解为加权指标，能够在评估目标论文级重发现的同时为新的发现留有余地。我们通过统一协议评估了七种自主研究智能体，并通过轻量级ResearchHarness框架评估了17种原生大语言模型。当前系统距离可靠的重发现仍有较大差距：最强自主智能体Claude Code平均得分为21.5，最强ResearchHarness大语言模型Claude-Opus-4.7平均得分为20.7，LLM前沿模型均值仅为26.5。错误分析表明，失败主要集中在实验方案不匹配、证据不匹配以及科学核心缺失三个方面。ResearchClawBench为衡量自主科学研究的进展提供了可复现的评估基准。