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arXiv · 2603.15599

SmartSearch: How Ranking Beats Structure for Conversational Memory Retrieval

Jesper Derehag·Carlos Calva·Timmy Ghiurau
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Abstract

Recent conversational memory systems invest heavily in LLM-based structuring at ingestion time and learned retrieval policies at query time. We show that neither is necessary. SmartSearch retrieves from raw, unstructured conversation history using a fully deterministic pipeline: NER-weighted substring matching for recall, rule-based entity discovery for multi-hop expansion, and a CrossEncoder+ColBERT rank fusion stage -- the only learned component -- running on CPU in ~650ms. Oracle analysis on two benchmarks identifies a compilation bottleneck: retrieval recall reaches 98.6%, but without intelligent ranking only 22.5% of gold evidence survives truncation to the token budget. With score-adaptive truncation and no per-dataset tuning, SmartSearch achieves 93.5% on LoCoMo and 88.4% on LongMemEval-S, exceeding all known memory systems under the same evaluation protocol on both benchmarks while using 8.5x fewer tokens than full-context baselines.

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Jesper Derehag, Carlos Calva, Timmy Ghiurau. 2026-03-16. SmartSearch: How Ranking Beats Structure for Conversational Memory Retrieval. https://arxiv.org/abs/2603.15599

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