Cairo Labs · Large Behavior Models

Cairo Rosetta

Language is what people say.
Behavior is what they choose.
We’re building AI for the second.

A new lens for artificial intelligence

Language. Worlds.
Now, human behavior.

AI can write the message. It can model the environment. Our question is different: what will the person do? Cairo Rosetta is a specialized behavior model built to predict individual choices—not just produce convincing answers.

LLMs

The language layer.

Large language models generate and interpret language. Their general-purpose capabilities make them useful across many tasks.

World models

The world layer.

World models represent environments and how they change. Their focus is the dynamics of a world and its possible future states.

LBMs · Cairo Rosetta

The behavior layer.

Our focus is the person inside that world. Given a profile, a situation and possible actions, which choice is that individual likely to make?

Our mission: build the world’s first Large Behavior Model. This is an ambition, not a verified claim of historical priority. LLMs, world models and behavior models are complementary lenses, not a ranking of intelligence.

Built around the individual

Not an average person.
A person in a situation.

A population average can hide the very differences you need to understand. Rosetta conditions its prediction on who the person is, what is at stake and what they can actually choose.

01 / The person

A distinct profile

Psychometric and cognitive traits describe the individual—not a generic role-play prompt.

02 / The situation

Context + possible actions

The scenario, its constraints and a defined set of alternatives frame the decision.

03 / The prediction

A structured choice

Rosetta selects the action it estimates best matches that person in that situation.

Specialization changes the economics

More behavior.
Less waiting. Less overhead.

Behavioral simulation becomes practical when the decision engine is fast and economical enough to repeat. Rosetta is designed for low-latency, low-marginal-cost predictions across many profiles and scenarios.

Faster
by design.

Less time per decision

Our preliminary research reports lower mean latency than every frontier language model evaluated. Dedicated behavioral inference keeps the focus on the choice.

A promising
accuracy edge.

Competitive individual prediction

Rosetta led the evaluated models in point accuracy. The direction is encouraging; a statistically conclusive advantage has not yet been established.

Built for
scale.

Low marginal cost as the goal

Dedicated inference avoids a paid external LLM API call for each Rosetta prediction. Infrastructure still costs money; this is not a claim of free or zero-cost simulation.

These comparisons describe preliminary Cairo research, not a guarantee for every workload. End-to-end speed, accuracy and cost depend on the task and the surrounding simulation pipeline.

Explore the evidence and limitations

The horizon

Understand the choice
before you make your move.

Our long-term ambition is a behavior layer for decisions about products, communication and social systems. These are directions for development and validation—not claims that every domain is already solved.

Products & experiences

Explore how different people might respond to an offer, navigate an experience or weigh alternatives.

Messages & positioning

Investigate the gap between what a message says and the decision it may inspire.

Social & economic choices

Study how profiles and context interact when people face trade-offs, uncertainty and negotiation.

Rosetta, explained

Clear answers.
No black-box promises.

What is Cairo Rosetta?

Cairo Rosetta is a proprietary, specialized behavior model developed by Cairo Labs. It uses an individual’s psychometric profile, the context of a situation and a catalog of available actions to estimate which action that person is most likely to take. Cairo calls this direction a Large Behavior Model, or LBM.

What is a Large Behavior Model?

An LBM is our framing for AI focused on modeling human decisions, rather than primarily generating language or modeling environmental dynamics. These categories overlap; they are not mutually exclusive or a universally standardized taxonomy. Cairo’s mission is to help establish the behavior layer as a category in its own right.

Is Rosetta more accurate than frontier LLMs?

Rosetta achieved a higher point accuracy than the frontier language models evaluated in our preliminary research. The evidence is encouraging, but the accuracy advantage is not statistically conclusive. It does not establish universal superiority across tasks, audiences or model versions.

Does low-cost inference mean free simulations?

No. Rosetta avoided external language-model API charges in the reported research, but infrastructure and other simulation stages still have costs. The paper does not establish an all-in inference price or a commercial Rosetta API price. Cairo workspace simulations use the platform’s published credit plans.

Can Rosetta replace research with real people?

No. Behavioral predictions are estimates, not observed decisions or guaranteed outcomes. Use simulated signals to explore hypotheses and prioritize what to test with real people. Broader validation across domains remains part of our research agenda.