OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

Key points

It solves the problem that data representation constrains research by making raw observations, including spatial, temporal, and channel relationships, accessible throughout the entire research process instead of relying on text and summaries.
It controls three autonomous agents for ideas, experiments, and writing through a thin deterministic pipeline, and it enforces freshness, leakage, HARKing, and traceability checks in code.
Across all 36 real-data cases, spanning five subject areas and four evidence types, it completed the full path from raw data to paper and received an average paper score of 6.3.
Direct perception outperformed the blinded variant that only received precomputed features across all seven evaluation dimensions and won 85 percent of pairwise comparisons.
As one example, it found that 21.7 percent of 750 earthquake entries labeled as noise actually contained earthquake signals, and the result was statistically validated.

External research summaries. These are not HDATF publications or measured product results.