EdgeBench: Unveiling scaling laws of (AI) learning from real-world environments
EdgeBench is a benchmark of 134 real-world tasks for evaluating how autonomous AI agents learn from real-world environments over extended interactions. It tracks performance for 12+ hours per task, revealing a log-sigmoid scaling law (R²=0.998). Tasks span scientific & ML, systems & SE, optimization, knowledge, formal reasoning, and games. Leading models like Claude Opus 4.8 and GPT-5.5 show continuous improvement with more interaction time.
EdgeBench: Unveiling scaling laws of (AI) learning from real-world environments
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