Q
What goes into a PackedContext?
Critical top-3 nodes (>75% score), ~5 patterns/bugfixes, ~5 concepts/approaches, plus token/time accounting, curated, not dumped.
Glossary · PackedContext
PackedContext.
2–4K tokens that beat 50K raw.
Direct answer
A PackedContext is a fixed-budget assembly of the best knowledge for one query: top critical nodes, best patterns and bugfixes, supporting concepts, selected submodularly (relevance minus redundancy) under a token cap, typically 2000/4096 across Knowledge/Thread/LowConf sections. It replaces two failure modes at once: whole-file dumps (expensive, noisy) and flat instruction files (stale, unbounded). Same task, a fraction of the per-turn tokens at higher signal density, the 12–25x ratio is a per-node design estimate, not a measured session average.
In Anvaya
How We Implement It.
01PackedContextAnvaya's Quorum packer renders PackedContext as XML the model consumes directly; mind_query serves it over MCP under a 4096-token budget.
Questions
Asked About PackedContext.
Q
Why submodular selection?
Pure top-K retrieval returns near-duplicates. Maximal-marginal-relevance picks items that add new information given what's already packed.
Q
How is this different from RAG top-K?
RAG retrieves; packing budgets, diversifies, sections by confidence, and renders for the model's attention order.
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