AuthorsYulin Hu, Yanyan Zhao, Zimo Long et al.
TL;DR
CUE-Mem benchmarks long-term user memory from recurring implicit multimodal cues, revealing explicit–implicit accuracy gaps up to 42.5 points in textualized systems.
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THE PROBLEM
Across textualized memory systems on CUE-Mem, Qwen3.6-35B-A3B shows EI-Gaps of 42.5 points on Entity Recall and 36.5 on Long Pattern.
These gaps mean multimodal assistants misplace recurring background cues, breaking user memory for preferences and habits and undermining personalized assistance over long histories.
HOW IT WORKS
CUE-Mem builds synthetic histories via Script Synthesis, Multimodal Dialog Synthesis, and Data Review, then probes memory with Entity Recall and Long Pattern tasks.
Think of CUE-Mem as a stress-test where explicit cues are like labeled files, while implicit cues are faint traces scattered across a giant card catalog of images and audio.
By separating explicit and implicit evidence, CUE-Mem exposes how long-term memory systems fail to preserve and retrieve subtle multimodal signals that a plain context window cannot reliably capture.
DIAGRAM
This diagram shows how CUE-Mem constructs synthetic text-image-audio histories through three stages before task generation.
DIAGRAM
This diagram shows how CUE-Mem feeds histories into textualized and native multimodal memory paradigms and measures explicit–implicit gaps.
PROCESS
Script Synthesis
CUE-Mem expands persona seeds into profiles and event timelines, assigning modalities and evidence types that later drive Entity Recall and Long Pattern tasks.
Multimodal Dialog Synthesis
CUE-Mem generates text-image-audio turns, placing explicit cues in foreground content and implicit cues as recurring background signals across sessions.
Data Review
CUE-Mem applies automatic checks and human review to ensure multimodal coherence, long-term consistency, and valid implicit cues before evaluation.
Task Design
CUE-Mem builds Entity Recall, Long Pattern, Personalized Recommendation, and Answer Refusal questions with annotated clues to probe long-term user memory.
KEY CONTRIBUTIONS
Text-image-audio benchmark for implicit cues
CUE-Mem introduces 2,674 QA pairs over 20 users, 648 sessions, 496 images, and 2,228 audio clips to test long-term memory from subtle multimodal evidence.
Four-task memory decomposition
CUE-Mem defines Entity Recall, Long Pattern, Personalized Recommendation, and Answer Refusal, each with explicit and implicit variants to isolate different memory capabilities.
Analysis of textualized and native multimodal memory
CUE-Mem benchmarks Full Memory, NaiveRAG, Generative Agents, Reflexion, MemGPT, A-Mem, MemoryOS, and multimodal RAG, revealing persistent explicit–implicit gaps and retrieval noise.
RESULTS
Memory Avg. Qwen3.6-35B-A3B Oracle Evidence
80.8%
+42.5 points over Full Memory implicit (38.3%)
Memory Avg. GPT-5.4-mini Oracle Evidence
78.4%
+40.9 points over Full Memory implicit (37.5%)
Human Full implicit Memory Avg.
82.8%
+44.5 points over Qwen3.6-35B-A3B Full Memory implicit (38.3%)
EI-Gap Qwen3.6-35B-A3B Full Memory
38.6 points
difference between explicit 76.9% and implicit 38.3% Memory Avg.
These metrics come from CUE-Mem’s main textualized memory results table, which tests long-term user memory across explicit and implicit evidence. The numbers show CUE-Mem exposes a large gap between oracle evidence and what current memory systems can recover from real multimodal histories.
BENCHMARK
These metrics come from CUE-Mem’s main textualized memory results table, which tests long-term user memory across explicit and implicit evidence. The numbers show CUE-Mem exposes a large gap between oracle evidence and what current memory systems can recover from real multimodal histories.
BENCHMARK
Memory Avg. accuracy (%) across Entity Recall, Long Pattern, and Personalized Recommendation for different memory systems under implicit evidence.
KEY INSIGHT
On CUE-Mem, Answer Refusal accuracy jumps from 63.2 to 97.2 for Qwen3.6-35B-A3B when moving from explicit to implicit evidence.
This is surprising because better refusal scores usually signal improved calibration, but in CUE-Mem they instead reveal that systems simply fail to access implicit memories and default to abstaining.
WHY IT MATTERS
CUE-Mem gives researchers a controlled way to stress-test whether long-term memory systems can form user memories from recurring background visual and acoustic cues.
With CUE-Mem, builders can now quantify preservation, retrieval, and use of implicit multimodal evidence, guiding designs for selective memory retention beyond naive context-window scaling.
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