We conduct focused research in Applied Meta-Learning.

Adapting model behavior at inference time — without retraining, large context windows, or touching the primary model's weights.

NEMA: meta-learning architectures that generate specialized, task-specific agents automatically.

Memory augmentation and model editing for handling outdated and domain-specific knowledge.

Benchmarking Nace's Safety Model against AWS Bedrock, Azure, NeMo Guardrails, and prompt-based approaches.

A theoretical framework connecting the geometry of data representations to training dynamics.

Efficient fine-tuning of LLMs through task-specific parameter generation.

Improving task adaptability in large language models.

A modular RAG pipeline tailored to enterprise needs — over 90% F1 on verification.

How structured, verifiable knowledge complements LLMs where they fall short. No ML background required.

Tailored, meta-learned Small Language Models for rapid, efficient adaptation.

Most post-training performance gains are reranking — aligning the proposal distribution with the task.