AI科学家终于能直接看原始数据了
现在的AI科学家其实是个“二手科学家”——它只能读论文、代码和现成的数据摘要，真正的原始实验数据(图像、信号、视频、3D结构)它看不见。这篇让AI科学家第一次直接“看”原始证据：一个感知层把各种模态的原始数据喂给三个自主智能体(想点子、做实验、写论文)，整个流程在代码里跑，从数据到成稿全自动。在36个真实数据集上，它全部走完了从原始数据到论文的完整流程，平均得分6.3;和只能看摘要的对照组比，直接感知让7项评估全部提升，赢了85%的对比。这不是你明天能用的工具，但它指向一个关键转变：AI做科研的瓶颈可能不在推理能力，而在它根本看不见证据。
📄 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.