Sapient Intelligence

Introducing PRAXIST Beta, your autonomous research team🚀

Define the objective, constraints, and what success looks like. PRAXIST discovers the path.

From there, PRAXIST takes on the experimental research loop. Multiple Research Peers explore competing approaches in parallel, share useful findings, and build on accumulated evidence across experiments and generations.

In partner-provided environments, PRAXIST reached a 100% safe-landing rate in a rocket simulation and reduced accumulated error in an industrial SLAM system from 9.37 centimeters to 5.01 centimeters.

From robotics control to quantitative finance, PRAXIST has delivered measurable advances across fundamentally different problems by combining a shared core research architecture with domain-specific tools, knowledge, constraints, and evaluators.

More approaches explored. Faster iteration. Greater R&D capacity without proportionally increasing specialist headcount.

We’re happy to share that two papers from Sapient Intelligence have been accepted to NeurIPS 2026!

The accepted work contributes to a broader research agenda around optimization, adaptation, and efficient learning in complex systems, all of which are key to building increasingly capable and autonomous learning systems.

See you at NeurIPS 🎉

Excited to see the conversation around OpenAI’s Astra bringing recurrent architectures into the frontier spotlight.

At Sapient, recurrence has been central to our vision from day one. We introduced HRM in June 2025 and open-sourced HRM-Text this May. It’s encouraging to see a direction we committed to early gaining wider attention.

Want to explore recurrent reasoning yourself? HRM-Text is an open-source ~1B-parameter model trained from scratch on just 40B unique tokens, for about $1,000 in GPU compute-with a full pretraining framework to get you started.

Explore the architecture. Train a model. Build on it.

There’s still so much to explore in recurrent architectures. We’re continuing that work and building PRAXIST to help more researchers pursue ideas they believe in, just as we have with HRM.

We’re still learning how to maintain and evolve PRAXIST responsibly, and we’ve established the following contribution workflow:

1. Submit an issue or PR
2. Maintainers review it, and proposed fixes move into validation branches
3. Related PRs may be grouped into a focused validation branch
4. Each branch goes through CI and real-world task testing, typically requiring 1–3 days of validation by the PRAXIST maintenance team
5. Once validated, changes are merged into main, validation branches are removed, and contributors are credited

We aim to run this cycle on a weekly basis. We’re grateful for every piece of feedback, idea, and contribution that helps make PRAXIST better.

More updates are coming soon. Stay tuned.

🔗 Learn more about the contribution workflow:
github.com/sapientinc/PRA…

Complex engineering solutions are rarely the result of a single breakthrough. They are assembled from discoveries: a mechanism uncovered in one experiment, a design principle validated in another, or an interaction that later becomes useful in a completely different direction. PRAXIST preserves these discoveries as evidence-backed building blocks that carry across generations of research, where they can be validated, refined, reused, and combined into more capable designs.

With every generation, the set of tested building blocks grows, giving the next round of research a stronger foundation to build on. Useful discoveries can be carried forward, refined, and recombined across future experiments, so each generation begins with more knowledge than the last. Every experiment expands what can be built next.

Read the full paper: arxiv.org/abs/2608.25955