Multi-Layer Vectors
Could several representations of the same material help an agent find both direct matches and useful connections? The aim is to preserve source identity while exploring different retrieval perspectives.
Explore representation →Research vision · not a released offering
Memory that helps you find a new angle.
A useful memory can surface a familiar fact or a connection you had not considered. We are exploring how different representations and vector resolutions could support both kinds of discovery.
The concepts below describe a direction for research and evaluation. They are not delivered product guarantees, published performance results, plan entitlements or a release schedule.
Two questions to explore
Different views. Different resolutions.
Matryoshka Vectors
For models designed for nested vector representations, could choosing a vector resolution offer a useful quality and resource tradeoff for a workload?
Explore resolution →Representation
Keep the source. Explore the perspective.
A proposed source layer would represent the original material. Additional representations might emphasize a theme, narrative structure or another useful perspective. Retrieval could then compare what each view contributes.
Source material
Keep the original text and its identity available for inspection. An embedding is a representation, not an exact reconstruction of the text.
Interpretive views
Explore representations that serve a particular question. Generated interpretations would need review and a clear connection to the source.
Useful connections
Test whether additional views improve the task. A novel association alone is not evidence that a result is correct or relevant.
Resolution
Explore the tradeoff before choosing it.
Matryoshka representations are a candidate for evaluating retrieval at different dimensions. The benefit depends on the model, indexing strategy and workload; shorter vectors do not automatically mean useful results or faster end-to-end search.
Model suitability
Establish that the model supports the representation and chosen dimensions.
Retrieval quality
Compare ranked results on representative queries and relevance judgments.
Resource cost
Measure storage, latency and processing cost in the actual deployment.
Evaluation
Measure retrieval. Review the result.
Relevance metrics can test whether expected material is found. Human review can ask whether a connection helps the task. Neither alone proves a general improvement in creativity or reasoning.
We will need reproducible results before making performance or quality claims for these ideas. Current retrieval workflows and evaluation tools are described in the documentation.