AI科学家终于能直接看原始数据了
现在的AI科学家大多只读文字、代码或别人算好的摘要，真正决定科学结论的空间、时间、跨通道关系它们根本看不见。这篇让AI直接吃原始证据——图像、信号、音频、视频、3D结构、轨迹、表格、公式、图——再靠三个自主智能体(想点子、做实验、写论文)在一条固定流水线上跑完整个研究。在36个真实数据集上，它从原始数据一路写到成稿全部成功，平均论文分6.3;跟一个只喂预计算特征的盲版比，直接感知在全部7个维度都赢，85%的对比判它更好。它不是你明天能用上的东西，但方向很明确：AI做科研的瓶颈不在流程自动化，而在它能不能亲眼看到证据。
📄 原文摘要(英文)
Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation. Yet workflow coverage alone does not provide access to the full evidence on which scientific discovery depends. Existing systems typically reason over text, code, labels, or precomputed summaries, leaving scientifically decisive spatial, temporal, cross-channel, and procedural relations unavailable to the agent. We introduce OmniScientist, an end-to-end, omni-modal AI scientist that conducts multidisciplinary research directly from heterogeneous raw evidence. A perception layer and 3 autonomous agents for ideation, experiment, and writeup operate within a deterministic pipeline, allowing observations to shape research questions, experimental decisions, and final claims throughout the research lifecycle. By running idea, rigour, and claim checks in code, the system enforces novelty screening, statistical validity, execution provenance, and numerical traceability. We evaluate OmniScientist on 36 real-data cases spanning 5 discipline families, 4 families of scientific evidence, and modalities including images, signals, audio, video, 3-D structures, trajectories, tables, formulae, and graphs. The system completes the full path from raw data to a compiled manuscript in all 36 cases and achieves a mean overall paper score of 6.3 with the reference reasoning backbone. In paired comparisons against a blind variant that receives only precomputed scalar features, direct perception improves all 7 evaluation dimensions and wins 85% of head-to-head judgments. These results show that lifecycle-wide perception is essential for evidence-grounded scientific discovery and provides a practical path toward broadly capable AI scientists.