Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence
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Computer Science > Information Retrieval
arXiv:2610.13277 (cs)
[Submitted on 9 Oct 2026]
Title:Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence
View a PDF of the paper titled Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence, by Han Luo and 7 other authors
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Abstract:Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries. Most embedding-based retrievers compare single-vector representations of queries and memories, even when relevance depends on a small part of the content. Such local evidence can be matched to queries using the contextual token representations already computed by the same embedding models. Our method uses sparse token matching to improve retrieval without additional training. Each query token retrieves a small set of matching tokens across candidate memories. The matching scores are aggregated for each memory and combined with whole-memory similarity to rank memories. An optional low-rank query adapter can be trained with supervision while keeping the encoder and stored memory representations fixed. Our method improves retrieval across eight benchmarks and multiple model families without additional training. Query adaptation further increases the gains to 13.3 and 14.3 percentage points in average Recall@10 and NDCG@10 on Qwen3-Embedding-4B. These retrieval gains translate into higher question-answering accuracy on long-term memory benchmarks.
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.13277 [cs.IR] |
| (or arXiv:2610.13277v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13277
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence, by Han Luo and 7 other authors
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