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.
Publications & Research
2026AI Memory: A Landscape Review
Politzki, J. — Academic survey of the AI memory space—encoding, storage, retrieval, and the technical tradeoffs between context injection, fine-tuning, and memory-augmented generation.
The State of AI Memory 2026
Politzki, J. — Industry report on the current landscape, from RAG to long-context windows and beyond.
On the Implicit Encoding of Human Psychology in Large Language Model Representations
Politzki, J. — LLMs trained on text have implicitly learned structured representations of human psychological traits.
Local Drift-Adapters: Mixture-of-Expert Embedding Translation for Heterogeneous Vector Databases
Politzki, J. — Per-cluster MoE adapters with drift-aware clustering outperform global baselines on MS MARCO.
Towards Universal Human Embeddings
Exploring unified representations of human-relevant concepts across domains.
Mapping Representations Across Representation Spaces
A survey drawing on the Platonic Representation Hypothesis—how different embedding spaces converge on shared geometric structure.
Latent Space Alignment via Manifold Projection
Politzki, J. et al. — Exploring zero-shot transfer capabilities across disjoint 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
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.