Information Chunking for Agent Memory
Structure inputs into digestible topical segments (chunks) before feeding to short-term memory rather than throwing the full input at the model; reduces overload and increases accuracy (~40% improvement observed in customer-service deployment).
Problem
Unchunked inputs into STM trigger the context-window-dumb-zone and lost-in-the-middle effects: degradation that starts well before the nominal context limit. The model can't prioritize, attention mechanisms get confused, retrieval quality drops.
Solution
Before feeding context into STM, run a chunker: split the input into topic-coherent, size-bounded segments. Tag each chunk with topic / source metadata so retrieval can prioritize. Feed only relevant chunks at decision time. Bornet's measured impact: 40% accuracy improvement in a customer-service deployment. Pair with context-window-packing, episodic-summaries, context-window-dumb-zone, contextual-retrieval.
When to use
- Long inputs (multi-turn history, large documents).
- Topical structure exists in the input.
- Engineering capacity for chunker maintenance.
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