Inference-Native Memory: Beyond Approximate Vector Retrieval

Retrieval-Augmented Generation (RAG) established vector databases as the de facto memory layer for LLMs. By chunking text documents, embedding them into dense vectors, and querying via cosine similarity, applications can pull semantically related paragraphs into active prompts. For loose natural language question-answering, this paradigm works admirably.

For autonomous agents and enterprise data systems, however, vector-only retrieval breaks down almost immediately. Cosine similarity cannot evaluate strict numeric ranges (such as 'transactions between $400 and $600'), cannot perform exact relational joins across disparate entities, and offers no guarantees against hallucinated omissions. Crucially, raw vector retrieval treats memory as unstructured text blobs, forcing models to repeatedly re-parse schemas, disambiguate entity references, and burn tokens deciphering raw strings.

ContexDB is our exploration of an inference-native context store. Rather than storing raw strings and approximate vector embeddings, ContexDB stores immutable, pre-compiled context artifacts. Each stored block is content-addressed by its cryptographic hash and indexed by its structural type, schema shape, and factual entity relations.

When an autonomous agent needs factual context to complete a task, retrieval is executed against typed schema slots rather than fuzzy semantic distance. Because the stored memory blocks are already compiled into canonical intermediate representation, the host runtime can page them directly into the active prompt buffer in microsecond time, bypassing text serialization and tokenizer prefill bottlenecks entirely.

As agent architectures mature from single-turn chatbots into stateful, autonomous reasoning engines, context memory must transition from fuzzy search engines to typed, low-overhead system substrates.