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About this paper
Govind Pimpale, Axel Højmark, Jérémy Scheurer, Marius Hobbhahn
This research develops and validates methods for forecasting the capabilities of frontier Language Model agents, utilizing publicly available data like model release dates and Elo ratings. It predicts that state-of-the-art LM agents could achieve a 90% success rate on software development tasks by early 2026 and similar high performance on cybersecurity and research engineering tasks in a comparable timeframe.
Key insights
- Chatbot Arena Elo ratings serve as a robust, publicly available proxy for general LM capabilities, correlating well with PC-1 scores (R2 of 0.74).
- Forecasting models using release date as an input variable generally outperform those using log-FLOPs for predicting frontier model performance, likely due to implicitly accounting for algorithmic advancements.
- The Release Date
- original_problem
Summary by alphaXiv.
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