Self-Corpus Vocabulary

Problem

Generic embeddings score against the world's distribution of meaning, not the agent's. A new thought that lands inside the agent's persistent web of concerns can come back with the same similarity score as a perfectly off-topic but topically-adjacent one, because the embedding space has no notion of what this particular agent has been writing about for months. The result is a salience signal that is plausible-on-paper and indifferent in practice: the agent cannot tell, from the score alone, whether a thought is on its own line of inquiry or just somewhere in the same neighbourhood.

Solution

Run a periodic mining pass over the agent's own corpus (e.g. last N weeks of thoughts plus the long-term insight store). Aggregate frontmatter tags and content frequency to extract the top-N concept tokens with weights. Persist this vocabulary as a small JSON cache. Downstream scoring components consume the cache as an additional axis: a thought is scored both on generic embedding similarity to recent context and on overlap with the cached self-vocabulary. Refresh on a cadence proportional to corpus volatility (e.g. weekly for a stable agent, after every dream-consolidation cycle for a more volatile one).

When to use

• The agent has an own-writing corpus large enough to mine (weeks of thoughts).
• Downstream scoring needs an own-frame axis beyond generic similarity.
• Refresh cadence is feasible on the deployment's compute budget.