Toward more interpretable attention mechanisms
A new method for tracing how transformer attention heads contribute to final predictions.
Independent AI Research Lab
Research that pushes machine intelligence forward — responsibly.
Datavane Labs is an AI research lab dedicated to advancing foundation models, multi-agent systems, and interpretability — turning open questions into rigorous, reproducible science.
04
Active research tracks
2026
Founded
100%
Open research ethos
FOCUS AREAS
Who we are
A small team obsessed with understanding how intelligent systems actually work.
Datavane Labs was founded on a simple premise: the systems we build should be as well-understood as they are capable. We run long-horizon research programs rather than chase quarterly demos, and we publish our findings so the field can build on them.
We work at the intersection of model architecture, evaluation, and safety — collaborating with academic partners, engineering teams, and independent researchers who share a commitment to rigorous, transparent AI research.
01
Rigor over hype
Every claim we publish is backed by reproducible experiments and open methodology.
02
Safety by design
Interpretability and alignment research run alongside capability work, not after it.
03
Open collaboration
We partner with universities, labs, and independent researchers to accelerate shared progress.
What we work on
Research areas
Four core programs guide our work, each spanning fundamental research and applied prototypes.
01 / Foundation Models
Model architecture & training
Exploring more efficient training regimes, architectures, and scaling laws for next-generation language and multimodal models.
Active program
02 / Multi-Agent Systems
Coordinated AI agents
Studying how multiple AI agents plan, negotiate, and collaborate on complex, long-horizon tasks reliably.
Active program
03 / Interpretability
Understanding model internals
Building tools and techniques to trace, explain, and predict the internal reasoning of large models.
Active program
04 / Applied Research
Translating research to practice
Turning lab findings into practical tools and prototypes, in partnership with industry collaborators.
Active program
How we work
Our approach
A consistent research process, from open question to peer-reviewed result.
01
Question
Identify open problems worth years of sustained inquiry, not just a headline.
02
Experiment
Design controlled, reproducible experiments with clear evaluation criteria.
03
Review
Stress-test findings internally and with external collaborators before release.
04
Publish
Share methodology, data, and results openly to move the whole field forward.
Latest thinking
Insights & publications
Notes, papers, and updates from the lab.
Paper
Report
Coordination failures in multi-agent planning
An analysis of where and why agent teams break down on long-horizon tasks.
Update
Datavane Labs: our first year
A behind-the-scenes look at how our research programs came together.
Let's talk
Interested in collaborating with Datavane Labs?
Whether you're a researcher, institution, or company exploring a partnership, we'd love to hear from you.