Build a brain that never forgets

tl;dr Our system prevents LLM catastrophic forgetting by using an append-only TimescaleDB database as external memory. Instead of retraining the LLM, we manipulate the context using database structures like continuous aggregates and coined terms, allowing the model to learn without overwriting past knowledge.

Let's talk about how Transformers work. At their core, they rely on something called the attention mechanism. The key player here is scaled dot-product attention, which you can express mathematically as:

\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right) V

Here's what's happening under the hood:

• Q (Query), K (Key), and V (Value) are projections of your input embeddings
• The QK^T operation calculates how relevant each position is to your current query
• The sqrt(d_k) scaling keeps your gradients stable
• The softmax function converts raw scores into probabilities, telling the model what to focus on

The problem with traditional learning

When you fine-tune an LLM, you're adjusting its internal parameters through backpropagation. This changes how the model computes Q, K, V, and everything in between. The softmax output shifts, and the model learns to pay attention to different patterns. Sounds good, right?

Not exactly. This is where catastrophic forgetting (CF) comes in. When you optimize for new data, you risk overwriting parameters that were crucial for old knowledge. It's like trying to learn a new language and forgetting your native tongue in the process.

Our solution: Learning without forgetting

We took a different approach with our MCP knowledge base design. Instead of modifying the LLM's parameters, we keep it frozen. The learning happens by manipulating the context we feed into the model, using our database as an evolving external knowledge store.

Here's how it works with softmax:

1. External memory and context filtering

   • We store observations in the observation_log
   • Instead of adjusting internal weights, we focus on selecting the right external context
   • This feeds into the LLM's fixed attention mechanism

2. Statistical significance through aggregates

   • Our continuous aggregates (like content_trends, entity_trends) act as first-pass filters
   • They identify recurring patterns based on mention_count or relation_count
   • Think of it as pre-computing what's likely important, similar to how softmax assigns weights

3. Knowledge synthesis and anchoring

   • When the LLM analyzes aggregates and coins terms like "Vibe Coding", it's turning raw trends into compact concepts
   • These coined_terms get stored back in the observation_log
   • They become powerful anchors for future context retrieval

4. Context-based learning The learning cycle works like this:

   • New data comes in
   • Aggregates track trends
   • LLM synthesizes trends into coined terms
   • Coined terms enrich the database
   • Future queries use the GIN index on payload to find relevant observations

Why this approach works

Think of softmax as a "focus amplifier". It takes raw relevance scores and turns them into a probability distribution. The most relevant items get high weights, while less relevant ones get near-zero weights. The total attention budget always sums to 1.

Our database design achieves something similar through its structure:

The echo chamber analogy

• Raw observations (observation_log): Every voice in a massive echo chamber
• Frequency counting (continuous aggregates): Microphones counting how often specific phrases are uttered
• Identifying trends (LLM querying aggregates): Checking which phrases have the highest "loudness scores"
• Knowledge synthesis (coined_terms): The LLM recognizing and naming significant themes

The popularity contest analogy

• Raw observations (observation_log): Every interaction and event
• Trend tracking (continuous aggregates): Systems monitoring likes, shares, and mentions
• Pattern recognition (LLM querying aggregates): Spotting rapidly increasing or consistently high scores
• Concept labeling (coined_terms): Giving names to viral trends

The technical payoff

The observation_log holds raw potential. The continuous aggregates provide scoring based on historical frequency. The LLM interprets high scores and synthesizes them. Together, they model the outcome of softmax: highlighting and prioritizing statistically significant information from a vast dataset.

We're not changing how the Transformer calculates attention. Instead, we're building an external system that learns by structuring, summarizing, and synthesizing information over time. This allows us to feed more relevant and distilled context into the LLM's existing attention mechanism.

The learning lives in the evolving state of our TimescaleDB database and the explicit knowledge artifacts generated by the LLM, not in the LLM's weights. It's learning by curating memory, not by retraining the brain.

Next: Brainery architecture design