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Intuition with Evidence: Adaptive Dual Memory for Multimodal Agents

Summary

The paper proposes a dual-memory framework that combines a model-native latent memory as an intuitive prior with a multi-indexed episodic memory for exact evidence reconstruction through retrieval-conditioned joint alignment, using a training-free routing mechanism driven by JSD sensitivity and confidence proxies to adaptively compose the two at inference.

SourcearXiv Computer VisionAuthor: Qiuhe Hong, Jingya Wang, Cuzhen Geng, Shenyao Peng, Munan Ning, Yuyang Liu, Yonghong Tian
Intuition with Evidence: Adaptive Dual Memory for Multimodal Agents
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[Submitted on 8 Oct 2026]

Title:Intuition with Evidence: Adaptive Dual Memory for Multimodal Agents

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Abstract:As multimodal agents engage in sustained interactions, long-term memory serves two distinct but complementary functions: fine-grained evidence recall and internalized interpretation for global understanding. Although episodic and latent memory address these demands through explicit historical records and internalized experience, neither paradigm alone can accommodate the diverse memory affinities across tasks, owing to limitations in episodic evidence coverage and integration, and in latent factual grounding. To bridge this divide, we propose a dual-memory framework, unifying a model-native latent memory as an intuitive prior with a multi-indexed episodic memory for exact evidence reconstruction via retrieval-conditioned joint alignment. At inference, the framework adaptively composes these memories through a training-free routing mechanism driven by Jensen-Shannon Divergence (JSD) sensitivity and confidence proxies for query-dependent memory affinity. Extensive evaluations across multimodal benchmarks demonstrate state-of-the-art performance, outperforming single-paradigm baselines overall (+4.67\% over vanilla Episodic), with further analysis highlighting episodic strengths in evidence-localized tasks and latent contributions to integrating dispersed information and compensating for imperfect retrieval.

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2610.33019 [cs.CV]

(or arXiv:2610.33019v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.33019

arXiv-issued DOI via DataCite

Submission history

From: Qiuhe Hong [view email] [v1] Thu, 8 Oct 2026 16:02:56 UTC (2,917 KB)

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Key points

  • Unifies latent memory and multi-indexed episodic memory to offset the coverage and grounding limits of single-paradigm memory
  • Uses a training-free Jensen-Shannon Divergence sensitivity and confidence routing mechanism for query-dependent memory selection
  • Reports state-of-the-art multimodal benchmark results, +4.67% over a vanilla episodic baseline

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