Hybrid Multimodal Memory (HMM)

• Hybrid Multimodal Memory (HMM) is a memory architecture that integrates heterogeneous data sources such as vision, text, and audio into unified, queryable systems.
• It employs hybrid encoding by combining structured knowledge graphs with experience pools and associative methods to enable robust pattern completion and retrieval.
• Empirical results show that HMM architectures enhance classification, retrieval, and generative performance while balancing interpretability and computational speed through effective memory consolidation.

Hybrid Multimodal Memory (HMM) is a class of memory architectures that integrate heterogeneous information sources—such as vision, text, audio, and structured knowledge—into a unified, queryable substrate. Distinguished from unimodal or flat associative memory models, HMM systems employ hybridization at three key levels: (1) data representation, fusing multiple input modalities; (2) memory organization, combining structured knowledge graphs with experience or chunk pools; and (3) access mechanisms, supporting both content-addressable and retrieval-augmented inference. This paradigm underpins a range of recent advances in biologically inspired pattern completion, lifelong learning agents, and generalist multimodal planning systems (Simas et al., 2022, Li et al., 7 Aug 2024, Liu et al., 3 Dec 2025).

1. Memory Architectures and Modal Encoding

Hybrid Multimodal Memory frameworks instantiate modality fusion via explicit coding schemes and multi-part memory layouts. In associative models, each pattern is parsed into $M$ discrete modalities (e.g., image + label) and mapped into high-dimensional binary vectors using modality-specific sparse encoders (e.g., "What-Where" for vision, Noisy X-Hot for discrete symbols). These are concatenated to produce a global sparse distributed representation: $x = [ x^{(v)} \;\Vert\; x^{(d)} ] \in \{0,1\}^N$ where visual and label modalities occupy disjoint subspaces of size $N_v$ and $N_d$, respectively (Simas et al., 2022).

Alternatively, agent-oriented HMMs decompose their memories into:

• Hierarchical Directed Knowledge Graphs (HDKG): Nodes represent entities (e.g., objects, tools), edges encode directed relations (e.g., crafting recipes), and subgraphs track task-specific dependencies (Li et al., 7 Aug 2024, Liu et al., 3 Dec 2025).
• Abstracted Multimodal Experience Pools (AMEP): Sequences or pools of key experience tuples, each capturing visual, textual, and contextual summaries, are compressed via pretrained encoders and stored as joint embeddings.

This compositionality enables cross-modal completion and retrieval—critical for behaviors such as cue-based inference or missing data reconstruction.

2. Memory Storage, Update, and Retrieval Mechanisms

Associative Willshaw-Type Memories

Classical HMM approaches utilize a single associative memory (e.g., Willshaw matrix) to store auto-associations of concatenated multimodal codes. Memory update follows a local, one-pass Hebbian rule: $$M = \bigvee_{\mu=1}^P (x^\mu \otimes x^\mu), \quad M_{ij} = \min\left(1, \sum_{\mu=1}^P x^\mu_i x^\mu_j \right)$$ Partial cue retrieval iteratively applies thresholded updates to reconstruct the full pattern, enabling the inference of absent modalities (Simas et al., 2022).

Graph-Structured and Pool-Based HMM

For agentic settings, HMM write procedures maintain two stores:

• HDKG Update: On receipt of new relational information (e.g., crafting formula), an edge $(u \to v)$ is added if absent; optionally, node embeddings are updated.
• AMEP Update: Experiences are summarized over a sliding window, with diverse keyframes and sub-goal text jointly encoded and appended to pools if similarity and threshold criteria are satisfied.

Retrieval procedures

• Graph Subgraph Extraction: Given task target $x$, extract subgraph $\mathcal{D}_x$ via BFS up to depth $L$.
• Experience Retrieval: Cosine similarity between joint embeddings and current context or query, returning top-$K$ matches.

A formal description of these algorithms, including pseudocode for write/read, is provided in (Li et al., 7 Aug 2024).

Consolidation, Distillation, and Retrieval

Long-term HMM modules organize entities/relations into hierarchical graphs (core, semantic, episodic), while recent context is cached in a short-term queue. Periodic distillation compresses essential knowledge into parametric model weights, speeding up recall but possibly reducing interpretability (Liu et al., 3 Dec 2025).

3. Content Completion, Classification, and Generative Capabilities

HMM architectures support:

• Content-Addressable Completion: Supplying a modality-partial cue (e.g., vision only) prompts the memory to reconstruct missing modalities by leveraging learned inter-modality correlations. For classification, decoding the inferred label subvector after retrieval provides robust recognition (Simas et al., 2022).
• Pattern Generation: Generative routines iteratively seed the memory with label-conditioned cues and prune/sparsify intermediate results to produce novel, label-consistent samples via memory-driven completion.
• Multimodal Reasoning and Planning: Agents orchestrate HDKG and AMEP retrievals at planning and reflection points. Planners extract sub-task dependency graphs; reflectors match current execution state to successful prior episodes, using retrieved context to bias LLM decisions (Li et al., 7 Aug 2024).

Experimental metrics quantify these abilities with recall MSE, classification accuracy, and retrieval latency across diverse benchmarks (MNIST, ScienceQA, LoCoMo, MSR-VTT) (Simas et al., 2022, Liu et al., 3 Dec 2025).

4. Organization: Short-Term, Long-Term, and Parametric Memory

Hybrid Multimodal Memory in modern systems distinguishes between:

• Short-Term Memory (STM): FIFO queue over recent experience “chunks” (images, text, etc.), suitable for transient, local context. Indexed via sparse keyword matches or dense embedding similarity.
• Long-Term Memory (LTM): Hierarchical multimodal knowledge graphs or indexed pools, updated via continual consolidation and node/edge merging to prevent unbounded growth. Adaptive forgetting prunes entities based on usage frequency: $$S_t(v) = \gamma (\alpha S_{t-1}(v) + (1-\alpha) \cdot 1)$$ pruning nodes with low importance scores.
• Parametric Memory: Periodically, memory contents are distilled into model parameters $\Theta$ via retrieval-augmented cross-entropy/RAG and KL-divergence minimization, ensuring fast, forward-pass recall (Liu et al., 3 Dec 2025).

This division balances interpretability, scalability, and inference speed.

5. Quantitative Results and Empirical Benchmarks

Performance evaluations demonstrate the impact of HMM designs:

System/Metric	Classification (MNIST)	ScienceQA Acc.	MSR-VTT R@1	Minecraft ("Diamond" SR)
Willshaw HMM (Simas et al., 2022)	100% (autoassoc); 84% (test)	N/A	N/A	N/A
MemVerse HMM (Liu et al., 3 Dec 2025)	N/A	85.48% (GPT-4o-mini+MemVerse)	90.4%	N/A
Optimus-1 HMM (Li et al., 7 Aug 2024)	N/A	N/A	N/A	11.6%
Human-Level (Minecraft)	N/A	N/A	N/A	16.98%

Key findings:

• Willshaw-type HMM achieves perfect recall on stored MNIST patterns up to 50k codes, with classification peaking at 84% on unseen test data (Simas et al., 2022).
• MemVerse’s HMM module improves GPT-4o-mini's ScienceQA performance by 8.66 percentage points and boosts text-to-video retrieval from 29.7% to 90.4% R@1 (Liu et al., 3 Dec 2025).
• Optimus-1 achieves 11.6% success on hard "Diamond" benchmarks, substantially narrowing the gap to human-level (16.98%) (Li et al., 7 Aug 2024).

Retrieval latency is reduced by distillation: MemVerse achieves mean retrieval times of 2.28 s (parametric), outperforming LTM (8.26 s) and RAG (20.17 s) (Liu et al., 3 Dec 2025).

6. Limitations, Extensions, and Future Directions

Documented limitations include:

• Scalability: Sparse-coded Willshaw variants are underpowered for high-resolution or continuous-valued data; encoding quality is a limiting factor (Simas et al., 2022).
• Interpretability/Speed Trade-off: Parametric recall is fast but increasingly opaque; explicit graph/pool retrieval supports traceability but at higher computational cost (Liu et al., 3 Dec 2025).
• Generalization: Saturation effects degrade associative memory generalization as capacity is reached; memory consolidation and distillation intervals affect retrievability and stability.

Extensions include supporting arbitrary modalities (audio, sensor, text), generalization to tasks such as time-series completion and anomaly detection, and adaptive merging/pruning schemes for dynamic environments. Iterative decoding and multimodal joint inference remain active research directions for extending the versatility of HMM systems (Simas et al., 2022, Li et al., 7 Aug 2024, Liu et al., 3 Dec 2025).