Elusive10
Super Human intelligence requires Super Human data, we make it
What We Do
Progress toward artificial super intelligence (ASI) is constrained by the limits of human-generated training data. We focus on creating super-human training data—synthetic datasets that enable models to acquire capabilities beyond what can be reliably extrapolated from human examples.
Research Focus
We investigate how training data composition and structure influence the emergence of elevated intelligence in foundation models. Our work centers on the hypothesis that progress toward ASI is constrained not only by model architecture, but by the informational limits of predominantly human-generated data.A core area of focus is the design and evaluation of synthetic and semi-synthetic training data that extends beyond conventional human cognitive distributions. This includes data derived from structured reasoning processes, simulated environments, and non-traditional signal sources.In particular, we study prediction markets, geopolitical events, and real-time information flows from global news and analysis platforms as rich sources of high-signal data. These domains provide naturally occurring forecasts, incentives, and feedback loops that can be leveraged to generate training signals for advanced reasoning, uncertainty estimation, and long-horizon prediction.Our research emphasizes controlled experimentation, rigorous benchmarking, and statistical validation to understand how such data sources affect model generalization, robustness, and emergent capabilities.
Contact Us
For research collaboration, infrastructure partnerships, or technical inquiries, please get in touch.
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