CueMem: Cue-Guided Context Reconstruction for Long-Term Conversational Memory
CueMem introduces a cue-guided framework for long-term conversational memory, treating extracted memory records as retrieval cues rather than self-contained evidence The system links fine-grained memory cues to their source dialogue turns and reconstructs query-relevant context by expanding from source-turn anchors over a turn graph The turn graph captures both temporal proximity and semantic relatedness between dialogue turns to produce compact evidence contexts for LLM generation Experiments o
Analysis
TL;DR
- CueMem introduces a cue-guided framework for long-term conversational memory, treating extracted memory records as retrieval cues rather than self-contained evidence
- The system links fine-grained memory cues to their source dialogue turns and reconstructs query-relevant context by expanding from source-turn anchors over a turn graph
- The turn graph captures both temporal proximity and semantic relatedness between dialogue turns to produce compact evidence contexts for LLM generation
- Experiments on LoCoMo and LongMemEval benchmarks demonstrate consistent outperformance over representative long-term memory baselines
- Graph-based context reconstruction recovers supporting dialogue evidence while reducing query-time input tokens and latency compared to full-history LLM approaches
Why It Matters
This work addresses a fundamental bottleneck in building long-term conversational agents: the trade-off between memory compression and evidence fidelity. By shifting from self-contained memory summaries to cue-guided reconstruction, CueMem offers a principled way to maintain answer quality without incurring the prohibitive costs of full-history retrieval, making it directly relevant to anyone building persistent conversational AI systems.
Technical Details
- Memory Construction Phase: CueMem extracts fine-grained memory cues from individual dialogue turns and maintains explicit links between each cue and its source turn, enabling traceability back to original context
- Query-Time Reconstruction: The system retrieves query-relevant cues, maps them to source-turn anchors, and performs graph-based expansion over a turn graph that encodes temporal proximity and semantic relatedness among dialogue turns
- Turn Graph Architecture: The graph structure captures two key dimensions—temporal closeness and semantic similarity—allowing the system to reconstruct a compact yet evidence-rich context from the original dialogue
- Evaluation: Tested on LoCoMo and LongMemEval benchmarks, with results showing consistent superiority over representative long-term memory baselines
- Efficiency Gains: Analysis confirms that graph-based reconstruction reduces both input token count and latency at query time compared to feeding full dialogue history directly to the LLM
Industry Insight
- The cue-guided reconstruction paradigm could become a standard pattern for long-context applications, offering a middle ground between naive full-history retrieval and aggressive summarization that loses critical details
- The turn graph approach generalizes beyond conversational memory to any domain requiring evidence reconstruction from structured temporal data, such as legal document analysis or medical record querying
- The demonstrated token and latency savings make this approach particularly attractive for production deployments where cost and response time are critical constraints, suggesting strong commercial viability for customer support and personal assistant applications
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