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Switching between natural language and structured graphs can boost multi-agent LLM performance by over 12 percentage points while slashing token usage by more than threefold.
Forget simply scaling context windows: structuring conversational memory as a temporally-grounded property graph unlocks significantly more accurate long-term reasoning.
LLMs can nearly double their accuracy on knowledge-intensive tasks by remembering and reusing past problem-solving steps, even across domains with no shared vocabulary.
