Our technical team members share their work in agent science and other fields on the forefront of AI.
Skill harvesting allows agentic systems to self-reflect, autonomously developing more specialized skills.
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.
Emergence is a compelling phenomenon observable both in natural systems and in engineered designs, where complex behaviors and patterns arise from simple interactions.
In this post, we consider how to make language models better, not just faster, inspired by several papers.
At Emergence, we’ve always believed that the next significant advancement in workflow automation will come from the planning, selection, and use of multiple external tools by artificial intelligence.
The high accuracy and precision of our model represent a new achievement in reliably identifying unsuitable prompts and biased datasets.
Our everyday interactions with computers are filled with slow and repetitive tasks.
A number of enterprise workflows involving language- and tool-control tasks can be augmented with LLM- and LVM-powered agents.
Self-improving agents have varying objectives, and the issue of aligning them with human values is critical.
The concept of a software agent can be traced back to the model Hewitt, et al.
The pivotal advancement in the ability of computers to understand language and develop functional world models has profoundly reset the landscape in computing.