Pushing
Boundaries.
Our research goes beyond simple RAG into intelligent pipelines that encode, store, forget, and recall important information for AI agents.
We turn the context exhausted from these systems into higher-fidelity representations, and build the translation layers to communicate across latent spaces.
Core Focus Areas
Intelligent Memory
Building pipelines that go beyond static retrieval to active, stateful memory management (encode, store, forget, recall).
Better User Representations
Developing multi-level embeddings that capture domain-specific nuances and conceptual structures beyond general semantic similarity.
Shared Embedding Spaces
Constructing common geometric grounds, translation adapters, and communication protocols where diverse models can communicate without loss of fidelity.
Techniques
The Fragmented World
Modern AI resembles the Tower of Babel. The industry is witnessing a proliferation of giant foundation models, but as it matures, dominant systems will shift toward specialized, domain-specific models optimized for precise outcomes.
These isolated intelligence silos speak different mathematical languages. To prevent fragmentation, a robust translation layer must be built to allow these models to communicate.
Our research draws insights from the Platonic Representation Hypothesis, which posits that different embedding spaces trained on similar data tend to converge on a shared geometric structure. By aligning this shared geometry, we can engineer adapters that map concepts between disjoint latent spaces, restoring unity to the ecosystem.
Commissioned Research
We partner with select organizations to solve hard technical problems in memory systems, latent space navigation, and model alignment.
Publications
2026 ARCHIVEThe State of AI Memory 2026
A comprehensive review of the current landscape, from RAG to long-context windows and beyond. Analyzing the technical tradeoffs between context injection, fine-tuning, and memory-augmented generation.
Latent Space Alignment via Manifold Projection
Politzki, J. et al. — Exploring zero-shot transfer capabilities across disjoint latent spaces.