Multimodal Memory Systems

• Multimodal memory systems are architectures that integrate diverse sensory data, such as vision, language, and audio, to support robust reasoning.
• They employ modality-specific encoders, semantic tagging, and structured graph representations to augment and efficiently retrieve information.
• Quantitative studies show these systems yield significant performance gains in recall QA, video analysis, and long-horizon agent tasks.

Multimodal memory refers to computational memory architectures and systems where information from multiple sensory or data modalities—such as vision, language, audio, structured signals, and even physiological measurements—is encoded, stored, retrieved, and leveraged for reasoning, question answering, planning, and other agentic tasks. Unlike unimodal (typically text-based) memory subsystems, multimodal memory enables richer context integration, long-term cross-modal association, temporal reasoning, and more robust human-aligned behavior in artificial agents. Recent advances have spanned retrieval-augmented neural architectures, lifelong agent memory, biomimetic consolidation, context augmentation for personal memory, and scalable graph-structured knowledge banks.

1. Core Principles and Formal Notation

Modern multimodal memory systems instantiate repositories where each "memory entry" is a structured tuple or node encompassing raw sensory data (e.g., image, audio clip), corresponding textual or semantic annotations, and metadata such as timestamps, geolocation, and inferred context labels. For example, the formally defined memory entry in Memory-QA is: $M_i = (I_i, C_i, T_i, L_i)$ where $I_i$ is an image, $C_i$ the user command ("remember this object"), $T_i$ the timestamp, and $L_i$ the location string (Jiang et al., 22 Sep 2025).

Each memory entry may be further augmented offline with multimodal or external context, such as OCR text, automatic captions, semantic event tags, or cluster assignments, and encoded into a fixed- or variable-length feature space by modality-appropriate encoders: $$\mathbf{e}_i = F(I_i, C_i, X_i, T_i, L_i) \in \mathbb{R}^d$$ where auxiliary text augmentation $X_i$ may combine OCR, image captions, and completion via VLM prompting.

Multimodal memories also encompass structured or hierarchical arrangements: entity-centric graphs (Long et al., 13 Aug 2025), knowledge triples and schema graphs (Liu et al., 3 Dec 2025), dual-stream logical/visual memory banks (Bo et al., 26 Nov 2025), object-temporal dual-indexing (Fan et al., 18 Mar 2024), and scene-centric 3D spatial Gaussians with memory attention (Zou et al., 20 Mar 2025). Methods span external fixed-size key–value memories, content- and context-addressable matrices, dynamic lists/graphs, and hybrid parametric-retrieval fused systems.

2. Memory Encoding, Storage, and Augmentation Strategies

The encoding and insertion of new multimodal memories typically proceeds through multiple stages:

• Multimodal Feature Extraction: Modality-specific encoders (e.g., CLIP/VLMs for vision and text, Whisper for audio) extract high-dimensional embeddings. For example, in WorldMM episodic memory, factual event nodes are represented as graph-structured, multi-scale captions and triplets with per-node embeddings (Yeo et al., 2 Dec 2025).
• Semantic Tagging and Contextual Augmentation: Input items are enriched with semantic tags, QA-guided captions, OCR, and invoked completion, yielding more robust future retrieval (Jiang et al., 22 Sep 2025).
• Temporal and Location Metadata: Timestamps and geolocations are attached and leveraged for time- and location-aware retrieval and reasoning (Jiang et al., 22 Sep 2025, Jiang et al., 22 Sep 2025, Li et al., 12 Sep 2024).
• Graph-Structured and Hierarchical Organization: Knowledge is organized into context graphs, knowledge triplets, or scene graphs, with explicit relations and aggregation of similar or conceptually-linked events (Liu et al., 3 Dec 2025, Yeo et al., 2 Dec 2025).
• Compression and Principal Component Selection: In high-dimensional or spatial memories, principal scene components (via PCA or similarity reduction) are retained to achieve scalable storage and efficient downstream retrieval (Zou et al., 20 Mar 2025).
• Entity-Centric and Object-Based Memory: For agent systems, object identities, category labels, and appearance trajectories form a parallel memory track to event-centric temporal representations (e.g., VideoAgent's dual memory) (Fan et al., 18 Mar 2024).

3. Retrieval, Reasoning, and Integration Mechanisms

Retrieval from multimodal memory is typically performed via a combination of content-based similarity scoring, multi-signal fusion, and contextually-aware selection. Retrieval signals include:

• Semantic Similarity: Dot-product or cosine similarity between the query embedding and memory embeddings.
• Temporal and Location Filters: Date-range, recency decay, and geolocation-based scores linearly fused with semantic similarity (Jiang et al., 22 Sep 2025).
• Contextual Augmentation: For complex queries, systems decompose into atomic filters (time, people, place), composite contexts (events, routines), and semantic knowledge, retrieving candidate subclusters across memory (Li et al., 12 Sep 2024).
• Adaptive and Hierarchical Retrieval: Controllers or agents iteratively select which memory (episodic, semantic, or visual) and which scale to query next, halting upon sufficiency (Yeo et al., 2 Dec 2025).
• Cross-Modal Bridging: Associative bridges between modality-specific memories (e.g., visual–audio) allow recollection of missing modalities by aligning their addressing distributions (Kim et al., 2022).

Answer generation is performed by prompting a LLM or policy network with the concatenated query and retrieved context, often with explicit chain-of-thought or JSON-formatted identification of used memory items (Jiang et al., 22 Sep 2025, Fan et al., 18 Mar 2024). Reinforced agents may perform multi-turn retrieve–reason–retrieve cycles, optimizing long-horizon task completion (Long et al., 13 Aug 2025).

4. Memory Update, Consolidation, and Forgetting

To support bounded resource usage and long-term adaptation, multimodal memory architectures employ strategies such as:

• Soft-Slot Update and Reinforcement: Episodic slots are updated by soft assignment (cosine similarity), incrementally blending new experiences into existing slots and tracking reinforcement weights for retention (Long et al., 13 Aug 2025).
• Clustering and Abstraction: Periodic consolidation merges related episodic traces into higher-order semantic clusters, reducing redundancy and promoting abstraction (Long et al., 13 Aug 2025, Lin et al., 14 Apr 2025).
• Grow-and-Refine Schema Acquisition: Dual-stream memories (logical and visual) are updated with new error schemas only if dissimilar to previous entries, or merged with similar ones, ensuring compaction and avoidance of catastrophic forgetting (Bo et al., 26 Nov 2025).
• Adaptive Forgetting: Selective decay or pruning of seldom-accessed, obsolete, or low-utility nodes (heuristically or via retention windows) ensures memory does not grow unbounded (Liu et al., 3 Dec 2025).
• Periodic Parametric Distillation: Essential knowledge from hierarchical long-term memory is distilled into a fast, small LLM for efficient recall during real-time operation (Liu et al., 3 Dec 2025).

Biologically-inspired frameworks, such as HippoMM, explicitly mirror hippocampal processes: pattern separation (adaptive segmentation), pattern completion (semantic replay), and hierarchical retrieval to balance fine perceptual recall with efficient semantic answering (Lin et al., 14 Apr 2025).

5. Knowledge Sources, Personalization, and Dataset Construction

Contemporary multimodal memory systems are trained and evaluated over datasets spanning domain knowledge, personal experiences, or ecological memory reports:

• Benchmarks: MemoryQA (wearable lifelogging images + recall QA) (Jiang et al., 22 Sep 2025), M3-Bench (robot/web egocentric video QA) (Long et al., 13 Aug 2025), HippoVlog (long-form vlogs for event understanding) (Lin et al., 14 Apr 2025), and ScienceQA/Text–Video retrieval (Liu et al., 3 Dec 2025) provide scale and diversity in modalities.
• Personal and Conversational Memories: OmniQuery augments personal media collections (photos, screenshots, videos) with structured context for complex, context-dependent personal QA (atomic, composite, semantic) (Li et al., 12 Sep 2024). MeMo captures multiparty conversational memory with first-person remembered moments, audio/video features, and socio-affective questionnaires (Tsfasman et al., 7 Sep 2024).
• Embodied and Open-World Agents: JARVIS-1 stores Minecraft play experience as multimodal key–value tuples (task text, game state images/symbolics, plan steps) for long-horizon planning (Wang et al., 2023).
• Biologically and Physiology-Inspired Memory Aids: Memento leverages wearable sensor fusion (EEG, GSR, PPG) to detect event-related potentials and index short-term high-attention moments, delivering targeted visual cues supporting real-world wayfinding and search (Ghosh et al., 28 Apr 2025).

6. Quantitative Results, Ablations, and Impact

Across diverse tasks and evaluation settings, systems employing explicit or well-architected multimodal memory show significant gains:

• Memory-QA/Pensieve: +14 percentage points over state-of-the-art on recall QA over lifelog images (Jiang et al., 22 Sep 2025).
• WorldMM: +8.4% average absolute gain over prior best on long video QA benchmarks (Yeo et al., 2 Dec 2025).
• REVEAL: +1.5 points on VQA-v2 (overall accuracy), +3.1 on OK-VQA, and marked CIDEr gains on COCO captioning when including diverse multimodal memory sources (Hu et al., 2022).
• M3-Agent: +6.7/+7.7/+5.3 percentage points over baselines on robot/web long-video tasks and VideoMME-long (Long et al., 13 Aug 2025).
• HippoMM: Outperforms VideoRAG by 14 pp on average accuracy and reduces response time 5.5× (Lin et al., 14 Apr 2025).
• OmniQuery: Raises perceived accuracy for personal context QA from 43.1% to 71.5% (Li et al., 12 Sep 2024).
• ViLoMem: Dual-stream error-aware semantic memory adds 2–6 pp on math and 1–4 pp on cross-domain multimodal benchmarks, preventing error recurrences (Bo et al., 26 Nov 2025).
• BitMar: Demonstrates that quantized 1.58-bit episodic memory recoups up to 4 pp in multimodal reasoning on edge devices at 7.5× throughput and 80% lower energy vs. naive transformer (Aman et al., 12 Oct 2025).
• Memento: Fusion-based ERP memory detection elevates route recall by 20–23% and halves cognitive load in wayfinding tasks compared to free recall (Ghosh et al., 28 Apr 2025).

Ablation studies consistently find that augmentation (context or QA-guided captions), multisignal retrieval (temporal, location, semantic), and explicit memory consolidation contribute the greatest marginal improvements (Jiang et al., 22 Sep 2025, Li et al., 12 Sep 2024, Lin et al., 14 Apr 2025, Bo et al., 26 Nov 2025). Memory compression, abstraction, and selective forgetting further enable scaling to real-world, lifelong, or resource-constrained environments.

7. Limitations and Directions for Future Research

Current multimodal memory architectures face limitations:

• Visual Intelligence and Generalization: Prototype systems may omit challenging elements such as open-set face recognition, finer-grained relationship extraction, or complex event composition (Li et al., 12 Sep 2024). Visual schema induction remains coarse for some high-precision submodalities (Bo et al., 26 Nov 2025).
• Privacy and User Data Retention: On-device model compression, federated learning, and privacy-preserving retrieval are required for deployment in personal, clinical, or AR contexts (Li et al., 12 Sep 2024, Ghosh et al., 28 Apr 2025).
• Real-World Robustness and Ecological Validity: Systems must be validated with head-mounted or wearable sensors in unconstrained real-world navigation, not just desktop VR (Ghosh et al., 28 Apr 2025).
• Memory Growth and Lifelong Learning: Explicit decay, dynamic consolidation, or cross-model memory transfer are nascent (Liu et al., 3 Dec 2025, Bo et al., 26 Nov 2025).
• Agentic Integration and Error Feedback: Human-in-the-loop correction, explicit error tagging, and adaptive reinforcement for error-aware memory are active topics (Bo et al., 26 Nov 2025, Long et al., 13 Aug 2025).

A plausible implication is that future progress will involve more deeply integrated cross-modal perception/reasoning systems, online consolidation and memory distillation, biologically-motivated error correction, and privacy-by-design for human-aligned long-term artificial memory.