Most AI benchmarks test what a model already knows. ByteDance Seed just released one that tests whether a model can get smarter while it works.
EdgeBench, released on July 2 by ByteDance’s AI research division, is a new evaluation framework built around 134 long-duration tasks that force AI agents to operate continuously for 12 to over 72 hours. The point isn’t to measure how much an AI knows on day one. It’s to measure how much better it gets by hour 50.
What EdgeBench actually measures
The benchmark spans six domains: scientific and machine learning problems, systems engineering, combinatorial optimization, professional knowledge work, formal mathematics, and interactive games. Expert humans averaged 57.2 hours per task to complete them, with the most demanding tasks requiring up to 320 hours of effort.
The research team analyzed nearly 38,000 hours of agent-environment interactions across multiple frontier models, including Claude Opus 4.8 and GPT-5.5. The team found that agent performance during these extended sessions follows a log-sigmoid scaling relationship with a coefficient of determination (R²) of 0.998. It means AI learning curves during long tasks are remarkably predictable, not chaotic.
