Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

===== When a new query or user message arrives, SAIQL does something like: =====
# Interpret the intent - “Is this about trading? About Nova’s self-rewrites? About a specific bug? About Erica’s presentation?” - Convert that into a set of semantic filters: topics, entities, time ranges, importance scores.
# Query the LoreToken graph (while it’s still compressed) - FIND relevant micro-memory (last few turns about this topic) - FIND meso summaries of related sessions - FIND macro facts that always matter (identity, key rules, constraints)
# Rank and compose a fresh context - Pick only the most relevant pieces from each layer. - Avoid duplicates/overlap (no repeating the same explanation 10 times). - Respect a strict token budget when building the actual prompt.
# Emit a compact, structured prompt to the model: - A handful of targeted summaries + a few verbatim snippets + the actual user query.

Then, after the model responds:
# Write back a new LoreToken: - What was asked, what was answered, what decisions were made. - Link it into the graph so future queries can find it via SAIQL.

That is “rolling context” in action: for each new turn, SAIQL rolls in fresh, relevant context and rolls out the stale, irrelevant clutter — while the LoreToken store behind it keeps growing in depth and structure.