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Tsinghua team proposes EurekAgent, arguing that the bottleneck in autonomous scientific research is environment engineering rather than smarter Agents. By engineering four dimensions—permissions, artifacts, budgets, and human-AI collaboration—they achieve SOTA on several mathematical and kernel engineering tasks, discovering a new optimal arrangement for 26 circles for under $11.
THU Team Eureka open-sources EurekAgent, an autonomous research system built with Claude Code that achieves state-of-the-art results on math, kernel engineering, and ML tasks through environment engineering.
The paper introduces EurekAgent, an environment-engineered agent system for metric-driven autonomous scientific discovery that achieves state-of-the-art results on math, kernel engineering, and ML tasks with low computational costs.
A comprehensive survey on agentic environment engineering for LLMs, covering environment modeling, synthesis, evaluation, and application, with a focus on agent-environment co-evolution.