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Instead of stuffing raw context with dialogue history or brittle retrieval vectors, treating user personalization as particle-filtered hypothesis tracing over natural language resolves the tension between fleeting intent and long-term preferences.
Reversing input text can unlock hidden contextual information, leading to richer embeddings and substantial performance gains in training-free LLMs.
