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 🎉

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

From the stage to the booth, PRAXIST sparked a lot of conversations at AI4. We spent the week meeting leaders and technical teams from across industries, discussing the problems they’re working on and how PRAXIST could be applied inside their organizations. Thanks to everyone who stopped by, shared their challenges with us, and explored what putting autonomous R&D to work could look like.

PRAXIST on stage at Ai4. Earlier this month, our co-founder @wcwilliamchen shared the thinking behind PRAXIST and our approach to the next generation of autonomous R&D. A few moments from the presentation.

What does it look like when AI learns to land a rocket? PRAXIST coordinates a team of Research Peers, each taking on a different part of the problem. Together, they learn across experiments and turn accumulated evidence into a validated, production-ready solution. See it in action🚀

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. Across more than 100 research and optimization tasks, PRAXIST has been applied to problems spanning over 50 application areas.

Across the full 75-task MLE-bench, PRAXIST (with an open-source model) achieved: → 65.3% Gold Rate — 49 gold medals → 80% Any Medal Rate — 60 total medals → 44% more gold medals than Claude Code + Claude Opus 4.8 → $3,054 in token cost, compared with $38,370 for Claude Code + Claude Opus 4.8 — roughly one-twelfth the cost Against the stronger Claude Code baseline, PRAXIST delivered better benchmark results at 92% lower token cost. *The token cost in USD for the full 75-task benchmark suite, measured July 1–30, 2026.

General-purpose coding agents are optimized to complete software tasks. Many research agents use a winner-takes-all loop: keep the best result and discard the rest. PRAXIST is purpose-built for cumulative experimental research. Every useful finding can shape what happens next.

PRAXIST assembles the team each problem needs: analyzers, explorers, innovators, exploiters, falsifiers, and combinators. Research Peers pursue competing directions in parallel. Shared memory connects their evidence. A PI Panel decides what to test next.

Starting today, PRAXIST is open source. Explore the code. Adapt it to your domain. Discover what comes next. Learn More: praxist.sapient.inc GitHub: github.com/sapientinc/praxis… Tech Report: arxiv.org/abs/2608.25955 Announcement: praxist.sapient.inc/en/about

LLMs generate. Agents execute. But what happens when the path itself is unknown? It must be discovered. Explore. Experiment. Validate. Optimize. Stay tuned.

Ai4 is happening NOW! Find us at 📍 Booth 1564 at @Ai4Conferences 2026. Get an exclusive preview of our newest product ahead of its official launch and experience it hands-on. Only at Ai4. Plus, grab a fun surprise. #Ai42026

Sapient Intelligence is heading to Vegas🔥 From August 4 to 6, @Ai4Conferences attendees will get an exclusive preview of our newest product ahead of its official launch. See it first. Experience it hands-on. Only at Ai4. #Ai42026 #ArtificialIntelligence

Excited to see our community building with HRM-Text! Can’t wait to see where you take it next.🚀 Whether you’re reproducing the benchmarks, testing it out in your own field, or building something entirely new, we’d love to hear about it. Drop your ideas, questions, and experiments below, and tag @Sapient_Int so we can follow along and cheer you on!🎉