V · MemoryExperimental·

Self-Corpus Vocabulary

also known as Personal-Concept Lexicon, Own-Writing Lexicon

Mine a small bounded vocabulary from the agent's own writing and cache it as the conceptual axis for scoring new thoughts, so relevance reflects the agent's actual frame rather than a generic embedding space.

Context

A long-running agent accumulates a corpus of its own output: thought traces, insights, journal entries, notes. Some downstream component wants to score new thoughts for relevance, novelty, or kinship with the agent's existing concerns. The default tool is a generic embedding space, which gives a sensible answer about semantic similarity but tells the agent nothing about its own preoccupations — 'is the agent still pulling at the things it has been pulling at?' is a different question from 'is this semantically close to the previous paragraph?'

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.

Forces

  • The agent's own corpus is the only source that knows its frame.
  • Vocabularies that grow unbounded become a different problem (everything matches).
  • The vocabulary must refresh as the agent's frame shifts.
  • Mining must be cheap or it cannot run on a schedule.
  • Storage must survive across sessions, like the corpus it derives from.

Example

An agent has been journalling for three months. Once a week, a mining job aggregates frontmatter tags and high-frequency content tokens across recent thoughts and the long-term insight store, picks the top thirty concepts with weights, and writes them to a small JSON cache. When the agent receives a new thought, the salience scorer combines generic embedding distance to recent context with overlap against the cached vocabulary. A thought that uses three of the top-thirty concepts scores higher than a thought with similar embedding distance but no overlap, because the cached vocabulary says 'this is on the line of inquiry'.

Diagram

Solution

Therefore:

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).

What this pattern forbids. Scoring components cannot use only the generic embedding space for own-frame relevance; the agent's learned vocabulary must be available as a separate axis so generic similarity does not displace own-frame fit.

And the patterns that stand alongside it, or against it —

  • complementsVector Memory★★— Store memories as embeddings in a vector index and retrieve the most semantically similar items at query time.
  • complementsCluster-Capped Insight Store·— Cap the number of insights per stem-token cluster and archive the oldest variants by mtime so the long-term store keeps the active research edge instead of accumulating near-duplicates.
  • complementsSalience Attention Mechanism★— Score every candidate memory item with a weighted salience function so each tick attends to a small, relevant top-k subset rather than re-reading all memory.
  • complementsDream Consolidation Cycle★— Run a deeper, slower reflection pass distinct from per-tick reflection — reading hours of recent thoughts, promoting themes, releasing affective residue, and clearing working memory — so the agent does not accumulate residue indefinitely.
  • complementsSemantic Memory★— Maintain a dedicated store of what the agent holds to be true about the user and the world, separate from event records (episodic) and learned how-to (procedural).

Neighbourhood

Click any neighbour to follow the language. Scroll to zoom, drag to pan.

References

Provenance