Our technical team members share their work in agent science and other fields on the forefront of AI.
A new paper explores how to encode specific innate knowledge into AI systems that are agnostic to the sensory modality of their inputs.
A new paper, "Transformer Layers as Painters," co-written by Emergence researchers and Sakana AI, investigates the internal workings of transformers, focusing on how the removal or reorganization of information impacts pretrained models.
We evaluated our Agent-E with the WebVoyager benchmark.
As we’ve seen the rapidly rising impact of LLMs, we’ve also seen the growing importance of “synthetic data,” generated instructional raw text used to train LLMs.
Skill harvesting allows agentic systems to self-reflect, autonomously developing more specialized skills.
A new paper, "Transformer Layers as Painters," co-written by Emergence researchers and Sakana AI, investigates the internal workings of transformers, focusing on how the removal or reorganization of information impacts pretrained models.
We evaluated our Agent-E with the WebVoyager benchmark.
As we’ve seen the rapidly rising impact of LLMs, we’ve also seen the growing importance of “synthetic data,” generated instructional raw text used to train LLMs.
Skill harvesting allows agentic systems to self-reflect, autonomously developing more specialized skills.
