想用AI帮你从头到尾写论文？PAPERCLAW能自动搜文献、定假设、跑实验、写全文，你还能中途插手改方向。
论文提出PAPERCLAW，一个多智能体系统，可从研究领域自主生成完整论文。该系统通过实时文献、数据集和代码孵化想法，并利用假设地图的迭代“提出-测试-反思”循环推进，在证据支持结论时自动撰写符合会议格式的论文。PAPERCLAW支持全生命周期记忆，允许暂停、检查与恢复，并内置人机协作接口，使研究者可在任意阶段介入优化。评估使用LLM评判表明，PAPERCLAW在完全自主和人在回路两种模式下均能产出高质量论文。
PaperClaw: Harnessing Agents for Autonomous Research and Human-in-the-Loop Refinement
Large language models have become capable reasoners and tool users that write and run code and search the literature, which makes automating the research process itself a realistic goal. We present PAPERCLAW, a harnessed multi-agent system that carries a project autonomously, from a field of study to a finished paper. PAPERCLAW curates a domain from a field's live literature, datasets, and code; brainstorms it into an idea with a pre-registered main-result contract; and drives a stoppable hypothesis map through an iterative propose, test, reflect loop that grows only from measured verdicts and halts once the evidence supports the idea, at which point it writes a venue-compliant paper. A full-lifecycle memory keeps each stage in a single living record, so a long run can be paused, inspected, and resumed without losing context. At the centre is an in-cycle research assistant with research tools and skills: it can drive the whole pipeline on its own, while the same interface lets a person step in at any stage, turning a first autonomous draft into a stronger paper through human-in-the-loop refinement. Throughout, PAPERCLAW keeps its output grounded and checkable, citing only references validated against open scholarly indexes and reporting results that genuinely ran. An evaluation with an LLM judge finds that PAPERCLAW produces strong papers both fully autonomously and with human-in-the-loop refinement.