The authors consulted 15 experienced scientists from 10 universities across 8 domains, and collaborated with 10 experts to design 26 tasks in neuroscience, geophysics, astrophysics, computer science, plasma physics, and chemistry.
AI agents can predict accurately but rarely produce scientific insights, benchmark shows.
In a new cross-domain benchmark with 26 expert-designed tasks, top AI agents match human-level predictive accuracy but fall far short on deriving interpretable scientific insights.
Big Tech
Jiayi Geng · Zhengxuan Wu · Kevin S. Chen · Seungone Kim · Joseph Janssen · Zora Zhiruo Wang · +9 more
Carnegie Mellon University · Stanford University · Yale University · Massachusetts Institute of Technology · Columbia University
Research Digest··2 min read
The authors introduce EurekaBench, a benchmark of 26 long-horizon tasks across six scientific fields, and evaluate seven AI agents on three axes: constraint satisfaction, predictive accuracy, and scientific insights.
Why this paper
From Google DeepMind and 8 others
In one line
EurekaBench reveals that AI agents surpass humans in predictive accuracy but fall short in deriving scientific insights.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ✓Limitations stated by the authors (2 noted)
- ✓Reports numbers on named benchmarks
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§