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PolyMemDB: A Polyglot Database System for AI Memory Management
Databases · Submitted 26 Aug 2026
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim To ensure factual consistency and reduce hallucinations, it features a probabilistic inference engine that integrates temporal decay with semiring aggregation, resolving long-term factual conflicts, providing detailed data provenance, and enabling users to trace reasoning chains transparently.
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The abstract, sentence by sentence
With the widespread adoption of personal intelligent agents, users generate massive, heterogeneous data during long-term interactions. Leveraging this data as long-term memory helps reduce token overhead and deliver personalized experiences. However, existing memory systems face two primary limitations: they rely on single-storage paradigms that fragment multi-dimensional data, and they lack fine-grained data provenance to resolve long-term factual conflicts, thereby worsening LLM hallucinations. In this demonstration, we introduce PolyMemDB, a novel system tailored for managing agent memory. PolyMemDB has a polyglot storage architecture designed to track and manage various memory types, including graph, vector, probability and spatial-temporal data. To ensure factual consistency and reduce hallucinations, it features a probabilistic inference engine that integrates temporal decay with semiring aggregation, resolving long-term factual conflicts, providing detailed data provenance, and enabling users to trace reasoning chains transparently.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | With the widespread adoption of personal intelligent agents, users generate massive, heterogeneous data during long-term interactions. | background 1.00 | 0.07 | 0.02 | asserted 1.00 |
| s1 | Leveraging this data as long-term memory helps reduce token overhead and deliver personalized experiences. | background 0.99 | 0.09 | 0.06 | asserted 0.89 |
| s2 | However, existing memory systems face two primary limitations: they rely on single-storage paradigms that fragment multi-dimensional data, and they lack fine-grained data provenance to resolve long-term factual conflicts, thereby worsening LLM hallucinations. | gap 0.76 | 0.05 | 0.08 | asserted 1.00 |
| s3 | In this demonstration, we introduce PolyMemDB, a novel system tailored for managing agent memory. | contribution 1.00 | 0.80 | 0.08 | asserted 1.00 |
| s4 | PolyMemDB has a polyglot storage architecture designed to track and manage various memory types, including graph, vector, probability and spatial-temporal data. | method 0.56 | 0.77 | 0.08 | asserted 1.00 |
| s5 | To ensure factual consistency and reduce hallucinations, it features a probabilistic inference engine that integrates temporal decay with semiring aggregation, resolving long-term factual conflicts, providing detailed data provenance, and enabling users to trace reasoning chains transparently. | method 0.81 | 0.81 | 0.36 | asserted 1.00 |
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Original abstract
With the widespread adoption of personal intelligent agents, users generate massive, heterogeneous data during long-term interactions. Leveraging this data as long-term memory helps reduce token overhead and deliver personalized experiences. However, existing memory systems face two primary limitations: they rely on single-storage paradigms that fragment multi-dimensional data, and they lack fine-grained data provenance to resolve long-term factual conflicts, thereby worsening LLM hallucinations. In this demonstration, we introduce PolyMemDB, a novel system tailored for managing agent memory. PolyMemDB has a polyglot storage architecture designed to track and manage various memory types, including graph, vector, probability and spatial-temporal data. To ensure factual consistency and reduce hallucinations, it features a probabilistic inference engine that integrates temporal decay with semiring aggregation, resolving long-term factual conflicts, providing detailed data provenance, and enabling users to trace reasoning chains transparently.
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At the level of conflict resolution mechanisms, MRAgent [ref] constructs a “Cue-Tag-Content” graph and relies on an active reconstruction mechanism, enabling the llm to iteratively explore and prune paths based on intermediate states during the retrieval phase.
Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents · Comparison to Existing Systems · 0.92
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