Understanding before deployment.
Our research spans the full stack of AI understanding — from the internal mechanics of individual neurons to the emergent behaviour of autonomous agent systems.
Why we research.
The AI industry moves fast. Models ship before anyone fully understands why they produce the outputs they do. We believe this gap between capability and understanding is the central risk of our field.
At Kaer Labs, every system we build is informed by rigorous research into how models actually work. Not how we hope they work. Not how benchmarks suggest they work. How they actually process information, form representations, and make decisions.
Empiricism over intuition
We measure, probe, and verify. Every claim about model behaviour is backed by systematic experimentation, not hand-waving.
Interpretability as infrastructure
Understanding isn't a nice-to-have — it's the foundation everything else is built on. Our agents are interpretable by design.
Open by default
Research locked behind closed doors doesn't advance the field. We publish our findings, open-source our tools, and share our datasets.
Active research domains.
Agentic AI Systems
Designing autonomous agents that plan, reason, and execute multi-step workflows. We study the full lifecycle of agent behaviour — from task decomposition and tool selection to self-correction loops and output verification.
Our work focuses on the reliability gap: the difference between what an agent can do in a controlled benchmark and what it actually does in production. We develop formal methods for bounding agent behaviour and ensuring convergence on correct outputs.
LLM Behavior Research
Probing the internal representations and decision boundaries of large language models. We study when and why models hallucinate, refuse, generalize, or fail — and what the internal mechanics look like when they do.
Using mechanistic interpretability techniques, we trace information flow through transformer circuits, identify feature representations in superposition, and map the causal pathways that lead to specific model outputs. This gives us a mechanistic understanding, not just a statistical one.
Efficient Model Design
Building models that do more with less. We research architecture innovations, training efficiency, and inference optimization — because the most impactful AI systems are the ones that can actually be deployed.
Our work spans knowledge distillation, structured pruning, quantization-aware training, and novel attention mechanisms. We're particularly interested in the relationship between model compression and capability preservation — understanding exactly what is lost when models are made smaller.
Alignment & Safety
Ensuring AI systems do what we intend them to do — not just in the average case, but under adversarial pressure, distribution shift, and at the boundaries of their training distribution.
We develop formal verification methods for behavioral constraints, automated red-teaming pipelines that discover failure modes at scale, and guardrail architectures that maintain integrity without degrading capability. Our approach treats safety as a systems engineering problem, not a policy one.
Interested in collaborating?
We partner with research teams pushing AI understanding forward.
Get in Touch