OmniScientist: An Omni-Modal Omni-Discipline AI Scientist· OmniScientist：多模态多学科的AI科学家
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 t
多模态AI系统进行跨学科研究，直接处理多种原始证据。
- 核心方法
- 引入感知层及三个自主代理（创意、实验、撰写）构成确定性流水线，处理多模态原始证据。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有AI科研自动化工具无法处理科学研究所需的多类型原始数据，导致关键关系缺失。
- 关键实验
- 未提供
- 主要贡献
- 实现了直接从异构原始数据中开展跨学科研究的全自动化流程。
- 意义与局限
- 增强了AI科研的全面性与准确性，但仍需验证其在实际科研中的效果。