AINeutralarXiv – CS AI · 6h ago6/10
🧠
What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents
Researchers demonstrate that successful machine learning strategies remain highly compressible and generalizable even when trained on held-out benchmarks, suggesting overfitting in benchmark-driven ML is rare because effective strategies occupy a low-complexity region of strategy space. Using LLM-driven research agents, they show that short prompts and minimal feedback suffice to reproduce high-performance models across diverse domains.