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Global Debugging Memory Helps AutoResearch Agents Avoid Repeating Mistakes, Raising AIDE Gold-Medal Runs from 22 to 38
With GPT‑5‑mini fixed as the model and a two-hour budget, researchers enabled a debugging advisor to share execution-environment constraints across search branches, allowing all 90 AIDE runs to produce valid submissions. The results suggest that agent scaffolds still contain substantial recoverable compute, though the experiments covered only nine tabular prediction competitions.