EngramLab Publishes First Research Blog on Law Firm Agents

This is a good time to share that I’m taking a sabbatical to work at Engram full time! Our blog post highlights some of the directions we’re taking, building agents that study and learn with complementary memory systems. There's lots of research still to be done, and I’m… Show more

So excited to share our first result! We get agents to study and deeply understand the context of a synthetic law firm we built with @Harvey. By combining text memory and parametric memory in training, we solve complex search and knowledge tasks over the law firm with a 3.3x… Show more

We had Qwen 3.8 study all of the docs in Harvey’s synthetic law firm - the more it studies, the fewer tokens it needs at inference time to respond to the queries https://twitter.com/EngramLab/status/2089439832686911626

i'm quite excited about this! • it's cool in general that models can generate their own training data and learn from it. wasn't fully clear to me even a year ago that this would work reliably. • the thing that we're trying to build seems really important and no one has built it… Show more

Great working with the @EngramLab team to explore model memory as a way to improve personalization and effective knowledge recall We're particularly excited about how models improve trajectories through memory. Rather than just learning to use search tools better, memory… Show more

We partnered with @EngramLab to train a model that learns from a firm's accumulated knowledge. We first built a 100M token synthetic law firm containing ~10k documents across 250+ matters and 46 clients. The model then internalized the firm's knowledge in two ways: 1)… Show more