Focus Areas
Our research is organized around four pillars — each representing a domain where we believe focused, open inquiry can meaningfully advance the state of the art.
Agentic Systems
We research the architecture and orchestration of multi-agent systems capable of autonomous reasoning, tool use, and collaborative problem-solving. Our focus is on understanding how agents can recursively generate and compose tools, how they align semantically when working together, and how they navigate open-ended environments.
AI Safety & Alignment
We develop formal verification frameworks and empirical methods to ensure that autonomous systems preserve their intended values across self-modification and deployment. Our research combines formal methods, adversarial robustness analysis, and interpretability techniques to build systems we can trust.
Foundational Models
We research the architectural and algorithmic challenges of building large-scale foundation models — sparse attention mechanisms, efficient scaling, multimodal reasoning, and long-context capabilities. Our goal is to understand the fundamental principles that allow models to scale efficiently while maintaining reasoning fidelity.
Developer Tooling
We bridge the gap between human developer intent and verified AI implementations. This includes research into how agents can parse and execute high-level developer goals, how to ensure implementations remain faithful to specifications, and how to build tools that make AI-assisted development both powerful and trustworthy.
How We Do Research
Reproducibility
Every finding includes detailed methods and, where possible, code. We commit to enabling independent verification of our results.
Transparent Limitations
We document what our work cannot do as clearly as what it can. Honest scope boundaries help the community understand real applicability.
Open Data
Datasets, benchmarks, and analysis code are released openly to facilitate independent investigation and community contribution.
Formal Verification
Where applicable, we employ formal methods to prove properties of systems rather than relying solely on empirical validation.