Excited to share our first research paper Learning, Fast and Slow: Towards LLMs That Adapt Continually.
Fast-Slow Training (FST) combines optimized context with model weight updates.
Read more here: arxiv.org/pdf/2605.12484
Fast-Slow Training (FST) combines optimized context with model weight updates.
Read more here: arxiv.org/pdf/2605.12484
1/ At Eragon, we’re building an AI operating system that connects a company’s entire tech stack into a single interface for work, powered by a model post-trained on the customer’s own data so it understands the company’s unique context.
We believe that AI system post-training shouldn’t have to choose between adapting quickly and learning durably: the future of adaptive AI is fast learning + slow learning:
- fast enough to absorb task-specific lessons
- slow enough to improve without forgetting
Our recent research paper: Learning, Fast and Slow, makes that case.
arxiv.org/abs/2605.12484View quoted post on X
