DataSense-Bench: The First Step Toward an AI Scientist

Key points

The agent selected disjoint, equal-sized subsets of candidate training data and ranked them by expected value for post-training performance.
The benchmark evaluated Qwen3-4B performance after training on the predicted top-ranked group under a standardized protocol.
On BFCL, all six agents' first groups exceeded random selection, but gains ranged from only 0.35 to 1.78 % points.
On TBLite, two of ten agents underperformed random selection, and the leading agent improved on it by only 0.99 points.
Ranking ability did not hold consistently across tasks. GPT-6 Astra identified the best group in all three BFCL runs but in only one of three TBLite runs.
Selection recipes varied substantially across three independent runs of the same agent, changing the selection rule itself.

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