Escher-Loop: Mutual Evolution by Closed-Loop Self-Referential Optimization
While recent autonomous agents demonstrate impressive capabilities, they predominantly rely on manually scripted workflows and handcrafted heuristics, inherently limiting their potential for open-ended improvement. To address this, we propose Escher-Loop, a fully closed-loop framework that operationalizes the mutual evolution of two distinct populations: Task Agents that solve concrete problems, and Optimizer Agents that recursively refine both the task agents and themselves. To sustain this self-referential evolution, we propose a dynamic benchmarking mechanism that seamlessly reuses the empirical scores of newly generated task agents as relative win-loss signals to update optimizers' sc...
| Authors | Ziyang Liu, Xinyan Guo, Xuchen Wei et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 April 2026 |
| Last updated at source | 27 May 2026 |
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