Multimodal Memory Systems

• Multimodal Memory Systems are computational architectures that integrate diverse sensory inputs—vision, language, and audio—into unified, efficient memory representations.
• They employ advanced techniques like deep learning, graph-structured indexing, and associative retrieval to support robust cross-modal reasoning and long-horizon event understanding.
• These systems enable applications such as video Q&A, agent planning, and personalized memory augmentation while addressing challenges in scalability, security, and real-time retrieval.

A multimodal memory system is a computational architecture or algorithmic framework that encodes, stores, retrieves, and leverages information spanning multiple sensory modalities—such as vision, language, audition, and sensor streams—within agents, foundational models, or lifelong learning systems. Multimodal memory systems address the limitations of traditional, text-centric memory augmentation by directly supporting the storage and retrieval of high-dimensional perceptual data, symbolically structured knowledge, and temporally grounded episodic traces. These systems integrate techniques from deep learning, cognitive science, representation learning, and information retrieval to enable sophisticated cross-modal reasoning, long-horizon event understanding, and robust recall of real-world experiences.

1. Foundational Principles and Motivation

Multimodal memory systems are inspired by the observation that intelligent agents, both biological and artificial, operate in environments saturated with high-dimensional multimodal information. While classical retrieval-augmented generation (RAG) architectures focus primarily on large-scale external text retrieval (Jain et al., 17 Oct 2025, Hu et al., 2022), there is mounting evidence that text-only memory fails to capture essential context for tasks such as video reasoning, multimodal planning, and lifelong adaptation (Liang et al., 29 Dec 2025, He et al., 2024). Motivated by the alignment with human memory systems in cognitive neuroscience, recent work seeks to (i) integrate cross-modal representations, (ii) exploit temporal and spatial structure, and (iii) enable efficient context-driven or concept-driven retrieval (Jain et al., 17 Oct 2025, Liu et al., 3 Dec 2025, Jiang et al., 22 Sep 2025).

Distinct from storing raw sensory streams—an approach both inefficient and noisy—modern multimodal memory emphasizes compressed, structured, or conceptually tagged representations. Examples include graph-structured memory for context-driven indexing (Jain et al., 17 Oct 2025), entity-centric multimodal graphs for semantic and episodic knowledge (Long et al., 13 Aug 2025), and hybrid memory banks pairing symbolic and latent features (Liang et al., 29 Dec 2025, He et al., 2024, Zou et al., 20 Mar 2025).

2. Representational Classes and Memory Organization

Contemporary multimodal memory systems span a spectrum of representations, trading off abstraction for expressivity:

• Symbolic-centric memories: Raw perceptual inputs are transformed by pretrained experts (e.g., visual-LLMs, ASR) into high-level symbols—object labels, timestamps, event captions, or scene graphs—and stored in compact, human-readable forms. This paradigm, exemplified by systems such as DoraemonGPT and LifelongMemory, aligns well with text-based RAG workflows and supports low-latency retrieval (Liang et al., 29 Dec 2025, Jiang et al., 22 Sep 2025).
• Latent or feature-level memories: High-dimensional feature embeddings or continuous dense representations are stored directly, typically using archiectures such as token merging, Q-Formers, or learned memory slots. Approaches like MA-LMM (He et al., 2024), CoMEM (Wu et al., 23 May 2025), and M3 (Zou et al., 20 Mar 2025) integrate these representations for rapid attention-based retrieval and robust perceptual coverage.
• Graph-structured and hybrid memories: Hierarchical multimodal knowledge graphs (Liu et al., 3 Dec 2025), entity-centric graphs (Long et al., 13 Aug 2025), and dual-stream memory banks (Bo et al., 26 Nov 2025) combine symbolic, spatial, temporal, and latent features. Edges may encode semantic, temporal, or identity relations; nodes retain cross-modal embeddings and backpointers to raw data, supporting both multihop reasoning and fine-grained recall.
• Neurally-inspired and associative frameworks: Some architectures, e.g., HippoMM (Lin et al., 14 Apr 2025) and Willshaw memory (Simas et al., 2022), are rooted in cognitive neuroscience, implementing mechanisms such as pattern separation, completion, and Hebbian association for robust multi-modal recall, adaptive abstraction, and fault-tolerant memory augmentation.

3. Memory Write, Read, and Retrieval Algorithms

The operational mechanisms of multimodal memory systems involve coordinated encoding, memory management, and adaptive retrieval:

Encoding and Insertion: Multimodal signals are processed by modality-specific or jointly trained encoders (e.g., Visual Encoder $f_v$ and Audio Encoder $f_a$ in M3-Agent (Long et al., 13 Aug 2025)). Information is stored based on content similarity, event segmentation, or attention-guided cues. Systems such as BitMar (Aman et al., 12 Oct 2025) leverage quantized, edge-optimized fusion encoders, while graph-based models utilize semantic tag extraction or triplet generation (Jain et al., 17 Oct 2025, Yeo et al., 2 Dec 2025).

Retrieval and Attention: Retrieval is driven by multi-signal relevance scoring (e.g., temporal, geolocation, semantic similarity in Pensieve (Jiang et al., 22 Sep 2025)), self-attention over memory banks (He et al., 2024), or hybrid graph walks. Attention mechanisms may combine memory slots or nodes via softmax-weighted pooling (Aman et al., 12 Oct 2025, Wu et al., 23 May 2025), or apply structured graph search combined with ranking and reranking (Yeo et al., 2 Dec 2025, Liu et al., 3 Dec 2025).

Cross-modal Bridging: Associative bridging frameworks—such as the audio-visual key-value associative memory in (Kim et al., 2022)—explicitly align the addressing distributions of different modalities via KL-divergence losses, allowing retrieval of one modality (audio) from a cue in another (video).

Lifecycle Management: Long-term memory continually consolidates, prunes, and distills salient knowledge. Adaptive forgetting procedures (e.g., relevance- or age-based node removal in MemVerse (Liu et al., 3 Dec 2025)) and periodic distillation to parametric models guarantee bounded memory growth and rapid response.

4. Architectures and Exemplar Systems

A diverse range of architectures implements the above principles:

System	Memory Structure	Modalities/Features
AUGUSTUS (Jain et al., 17 Oct 2025)	Graph-structured multimodal contextual memory	Semantic tags, graph associations
Pensieve (Jiang et al., 22 Sep 2025)	Annotated multimodal episodic memory, multi-signal retrieval	Images, OCR, captions, geo, time
MA-LMM (He et al., 2024)	Dual memory banks (raw visual, Q-Former queries)	Video (long-form), visual queries
M3-Agent (Long et al., 13 Aug 2025)	Entity-centric multimodal memory graph (episodic+semantic)	Video, audio, text
HippoMM (Lin et al., 14 Apr 2025)	Short/long-term store, cross-modal associative recall	Audiovisual streams; neural-inspired
MemVerse (Liu et al., 3 Dec 2025)	Hierarchical MMKG (core, episodic, semantic), with parametric distillation	Images, audio, video, text
CoMEM (Wu et al., 23 May 2025)	Continuous memory bank (dense slots, Q-Former)	Multimodal, multilingual knowledge
VideoAgent (Fan et al., 2024)	Temporal and object-centric memories	Video segment features, event captions, object tracks
BitMar (Aman et al., 12 Oct 2025)	Quantized episodic memory (sliding window), per-layer conditioning	Image, text; low-bit for edge devices

This table illustrates the architectural diversity across recent research, highlighting the emergence of graph-structured, continuous, and hardware-optimized multimodal memory.

5. Applications and Empirical Evaluations

Multimodal memory systems are deployed for a variety of high-complexity tasks, with demonstrated advantages over text-only or unimodal agents:

• Long-horizon video and event understanding: MA-LMM (He et al., 2024), WorldMM (Yeo et al., 2 Dec 2025), HippoMM (Lin et al., 14 Apr 2025), and VideoAgent (Fan et al., 2024) exceed prior state-of-the-art accuracy on long video QA, with robust scaling to multi-hour or egocentric data (e.g., +8.4% average over previous SOTA in WorldMM).
• Recall-based QA over episodic memory: Pensieve (Jiang et al., 22 Sep 2025) achieves up to +14 points in end-to-end QA accuracy by integrating temporal, location, and semantic memory signals.
• Agent planning and multi-stage reasoning: JARVIS-1 (Wang et al., 2023) and M3-Agent (Long et al., 13 Aug 2025) exploit memory-augmented in-context learning for open-world, multi-task planning, supporting multi-turn retrieval, backward-chaining, and adaptive recall.
• Personalized and edge memory augmentation: BitMar (Aman et al., 12 Oct 2025) combines low-bit encoders, compact episodic memory, and sliding-window attention, supporting real-time image-text reasoning under resource constraints, while Memento (Ghosh et al., 28 Apr 2025) fuses physiological and environmental signals for memory cueing in wearable, multimodal setups.

Empirically, the incorporation of multimodal memory yields substantial gains in QA, event recall, planning reliability, and operational efficiency (latency, compute), as shown in detailed experimental sections across (Jain et al., 17 Oct 2025, Jiang et al., 22 Sep 2025, He et al., 2024, Liu et al., 3 Dec 2025, Aman et al., 12 Oct 2025).

6. Challenges and Open Directions

Despite progress, the field faces fundamental challenges:

• Representation and storage: Unified formats capable of integrating text, vision, audio, and other modalities without semantic loss remain an open problem. The trade-off between compactness and expressivity—feature-level versus symbolic memories—demands further calibration (Liang et al., 29 Dec 2025).
• Scalable, real-time retrieval: Efficient long-horizon temporal dependency modeling, hierarchical index structures, and modality-aware retrieval operators are underdeveloped (Yeo et al., 2 Dec 2025, Liu et al., 3 Dec 2025).
• Cross-modal reasoning and alignment: Ensuring that retrieval and reasoning respect temporal alignment, contextual salience, and cross-modal associations is critical; current systems employ hybrid attention, associative bridging, or dual-stream memory but lack standardized protocols.
• Memory lifecycle management: Adaptive consolidation, forgetting, and parametric distillation require robust, scalable algorithms to maintain relevance and prevent catastrophic forgetting (Liu et al., 3 Dec 2025). For real-world operation, systems must avoid memory bloat and semantic drift.
• Security and robustness: While memory security is recognized (extraction and poisoning attacks, privacy sanitization), cross-modal vulnerabilities and defense mechanisms remain underexplored (Liang et al., 29 Dec 2025).

7. Connections to Cognitive Science and Biological Inspiration

Several systems purposefully draw parallels to—and architectural inspiration from—biological memory. HippoMM (Lin et al., 14 Apr 2025) formalizes hippocampal mechanisms (pattern separation/completion, consolidation, cross-modal association), while Memento (Ghosh et al., 28 Apr 2025) incorporates physiological signal-driven cueing, echoing evidence-based episodic recall. Associative frameworks (e.g., Willshaw (Simas et al., 2022)) implement Hebbian-style, fault-tolerant storage across modalities. Such biologically-inspired architectures aim to reproduce critical functions of human memory: robust completion, interference reduction, context-driven retrieval, and integrated multimodal abstraction.

The intersection between computational and cognitive models remains an active area for both theoretical and applied research, with emerging systems striving for increasingly integrated, error-aware, and general-purpose multimodal memory (Bo et al., 26 Nov 2025, Liang et al., 29 Dec 2025).