EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
- Published
- Source
- arXiv
- Paper number
- 409
- Field
- AI / General
- arXiv ID
- 2606.13662
Key points
- The key claim is that as models get stronger, the bottleneck shifts from the workflow to environment engineering.
- It defines four forms of environment engineering: permissions for evaluation separation, artifacts for Git and file-sharing memory, budget for cost-aware exploration, and human-in-the-loop control.
- It achieves a new SOTA of 2.635999 on 26-circle packing at a cost of $11 in API usage.
- It improves TriMul kernel optimization by 4.3% over the former leaderboard leader and by 10.8% over TTT-Discover.
- On seven MLE-Bench Lite tasks, it earns medals in 85.71% of cases and outperforms systems built on commercial models.
- ResearchClawBench provides evidence that general-purpose CLI agents outperform domain-specific systems.
Paper links
External research summaries. These are not HDATF publications or measured product results.