[regnodon]

Really interesting approach to the RAG noise problem. The atomic swap via shadow tables is a clever way to handle the migration.

One edge case I’m curious about is how the system handles modal logic or intent vs. fact. If a user says 'I live in Texas' and then 'I wish I lived in Florida,' a regex-heavy approach might struggle to differentiate between current state and aspiration.

In a 'neuroplastic' database, how do you handle schema deprecation or 'forgetting' when the foundational patterns drift (e.g., a user moves cities or changes a diet)? Do you have a mechanism for the schema to 'de-evolve' or merge back into a generic table if a specific entity's mention-frequency drops below a certain threshold?

[Mutatis]

Mutatis: Autonomous Schema Evolution & Managed Deprecation

I’ve seen a lot of discussion about "Memory Bloat" in RAG systems. In Mutatis, we solve this by treating the database schema as a fluid organism that evolves (and de-evolves) based on a combination of Semantic Pattern Detection and Confidence Decay.

As the data scales, the system shadow-builds specialized tables for high-confidence entities, shifting query complexity from O(N) to O(log N).

How we handle the lifecycle of a memory from "Generic" to "Optimized" and back again:

1. SEMANTIC LOGIC VS. REGEX We don't trigger schema changes on keyword frequency alone. We use an LLM-driven classifier to distinguish Modal Logic (intent) from Foundational Facts. - Intent: "I wish I lived in Florida" -> Stored as preference in a generic table. - Fact: "I live in Florida" -> Triggers the evolution pipeline. This prevents schema "pollution" from noise or aspirational intent.

2. MENTIONS, DECAY, AND "DE-EVOLUTION" Schema evolution is a reward for frequently referenced data; deprecation is the penalty for irrelevance. - Confidence Decay: When contradictory statements are detected (e.g., "I moved to Texas"), the confidence score for the "Florida" schema decays. - Frequency Thresholds: If an optimized table isn't hit within a specific window, it is flagged for De-Evolution.

3. MECHANISM: SHADOW TABLES & ATOMIC SWAPS To ensure zero-downtime, we use a shadow-table migration pattern: - Selection: A schema is flagged for merging via periodic hygiene checks. - Shadow Merge: A background transaction copies data from the specialized table back into a generic_memories table. - Atomic Swap: We drop the specialized table and update the query router in a single atomic transaction.

MANAGED MEMORY LIFECYCLE SUMMARY: Mechanism | Purpose | Implementation Mention Decay | Identifies stale data | Rolling counters on hits Confidence Scoring | Handles contradictions | Drift via sqrt(2) weighting Hygiene Checks | Prevents schema bloat | Periodic TTL-driven merges Atomic Swaps | Safe transitions | Transactions + Shadow Tables Modal Tagging | Filters intent vs fact | Zero-shot categorization

THE BOTTOM LINE: By allowing the schema to "de-evolve" back into generic tables, we maintain O(log N) performance for relevant data without the overhead of maintaining thousands of stale indices.