Decentralized Memory Retrieval

• Decentralized memory retrieval is a distributed approach that enables efficient storage, indexing, and retrieval of information without a central controller, ensuring scalability and privacy.
• It leverages coded caching, agent-based routing, and decentralized AI memory architectures to optimize retrieval efficiency and system robustness across diverse networked environments.
• Blockchain-secured scoring and biologically inspired models enhance trust and dynamic adaptation in multi-agent systems by enabling context-driven and secure data retrieval.

Decentralized memory retrieval refers to the class of architectures, algorithms, and systems that enable efficient storage, indexing, and retrieval of memory or information in a distributed manner, without reliance on a central controller. This paradigm encompasses methods from networked coding, distributed AI memory, peer-to-peer information retrieval, decentralized reinforcement learning, blockchain-inspired memory architectures, and biologically plausible neural models. The common objective is to maximize retrieval efficiency, robustness, scalability, and semantic fidelity while maintaining system autonomy and, when needed, privacy or trust guarantees across independently operated memory “agents”.

1. Foundational Models: Coded Caching and Randomized Placement

Decentralized coded caching, initiated by Maddah-Ali and Niesen, demonstrates that overlapping random memory allocation combined with coded multicast delivery enables near-optimal retrieval rates under minimal coordination (Maddah-Ali et al., 2013). In the canonical model, a server holds $N$ files and serves $K$ users with local caches of $MF$ bits. The placement phase is fully decentralized: each user independently caches a random subset of $M/N$ bits for every file—bitwise, each is placed with probability $q = M/N$. During the delivery phase, for any set $S \subseteq [K]$ of users, the server broadcasts XORs of the pieces each user needs and not cached by the rest of $S$, enabling simultaneous satisfaction of all requests in $S$.

The achieved worst-case rate is given by

$$R_{decentralized}(M) = K\left(1-\frac{M}{N}\right)\min\left\{\frac{N}{KM}\left[1-(1-\frac{M}{N})^K\right], \frac{N}{K}\right\}$$

or, equivalently,

$$R_{decentralized}(M) = \sum_{s=1}^K \binom{K}{s} q^{s-1}(1-q)^{K-s+1} = \frac{1-q}{q}\left[1-(1-q)^K\right]$$

This strategy is provably within a constant factor ($C=12$) of the centralized optimum (Maddah-Ali et al., 2013).

Enhancements via MDS-coded prefetching eliminate singleton (low-multicast-gain) transmissions and further improve the memory-rate tradeoff, approaching the centralized performance as the code length increases (Wei et al., 2018). Here, each file is MDS-encoded, and users cache random coded symbols. Delivery proceeds by roundwise multicast of XORs over user subsets, truncated when all users have acquired enough symbols for decoding.

2. Agent-Based and Semantic Retrieval in Decentralized Networks

Peer-to-peer and social-network models embed actors and documents into multi-dimensional spatio-temporal and social-semantic spaces, enabling agent-based “memory retrieval” via context-guided routing (Groh et al., 2012). Each peer locally indexes its “memory” and publishes only summary knowledge flags (e.g., topic expertise) to a distributed overlay indexed by spatial, temporal, and semantic coordinates. Upon receiving a query, an agent first searches its local store, then routes the request to relevant neighbors by context similarity—a composite of semantic relevance ($R_{\mathrm{sem}}$), social closeness ($C_{\mathrm{soc}}$), and spatio-temporal proximity ($P_{st}$):

$$S(q,u) = \alpha R_{\mathrm{sem}} + \beta C_{\mathrm{soc}} + \gamma P_{st}, \quad \alpha + \beta + \gamma = 1$$

Routing is performed greedily in the “small-world” graph induced by these metrics, guaranteeing efficient ($O(\log N)$ hops) discovery of relevant information even under full decentralization.

Empirical analysis on Twitter-scale networks confirms that geo-social pre-filtering achieves high recall and precision, especially for “unconscious” (interest-driven) queries and in privacy-sensitive retrieval, since raw data is never exposed globally (Groh et al., 2012).

3. Distributed AI Memory Architectures

Recent AI systems replace centralized vector indices or flat document stores with decentralized, meaningful memory architectures. SHIMI (Semantic Hierarchical Memory Index) structures agent memory as rooted trees of semantic abstractions (Helmi, 8 Apr 2025). Each node encodes a concept or category, with retrieval proceeding top-down: queries descend the hierarchy by matching on LLM-based or embedding-based similarity until reaching leaf nodes, which supply payload entities. Each agent maintains an independent SHIMI tree, synchronizing via Merkle-DAG summaries, Bloom filter-guided partial sync, and CRDT-based conflict resolution. This supports eventual consistency and bandwidth-efficient sharing—minimum sync cost $C_{sync} = O(|T_d| + |Ops_{T_d}|)$, with observed $>90\%$ reduction relative to naïve full replication. SHIMI achieves top-1 retrieval accuracy of 90%, outperforms flat RAG baselines in explainability, and is natively compatible with decentralized cognitive collaboration.

In decentralized differentiable search index (De-DSI), memory is encoded entirely as learnable query-to-docID mappings (Neague et al., 18 Apr 2024). Multiple DSI models are trained on disjoint shards of data, with aggregation at inference via decentralized beam search and softmax score pooling. Each peer holds only local data, gossiping examples within its shard. Global retrieval merges candidates from all models to form the final result set, achieving top-5 accuracy $>90\%$ at 10,000 documents per shard and comparable performance in recalling long (magnet-link) IDs.

4. Decentralized Memory Retrieval in Multi-Agent and Cognitive Systems

Multi-agent reinforcement learning exploits decentralized memory retrieval for rapid coordination in MARL tasks. MAICC employs a two-stage embedding architecture: a centralized model (CEM) is first trained to produce team-level trajectory representations; decentralized models (DEMs) for each agent are then trained by KL-divergence distillation, mimicking CEM outputs under observation/action masking (Jiang et al., 13 Nov 2025). At test time, agents embed their local sub-trajectories via the DEM and retrieve most relevant trajectories from a mixed memory (combining decayed offline and accumulating online buffers):

$\beta_t = \exp(-\lambda t/T)$

controls the weighting between offline and online trajectory sources. The retrieval score incorporates both cosine similarity in embedding space and a hybrid utility term blending normalized team and predicted individual returns:

$$\mathcal S(\tau^c, \tau^q) = \mathrm{cossim}(z^c, z^q) + \alpha\,\mathrm{norm}(\mathcal R(\tau^c)) + (1-\alpha)\,\mathrm{norm}(\tilde{\mathcal R}_j(\tau^c))$$

This mechanism yields rapid adaptation, avoids “lazy agent” behavior, and supports credit assignment across agents. On Level-Based Foraging and StarCraft Multi-Agent Challenge benchmarks, this decentralized retrieval achieves marked gains in early-stage adaptation and reward compared to single-agent and prior multi-task baselines (Jiang et al., 13 Nov 2025).

5. Robust Retrieval and Trust in Decentralized RAG Systems

Decentralized retrieval-augmented generation (dRAG) architectures tackle retrieval over independently operated sources of varying reliability, using dynamic reliability and usefulness scores maintained transparently on blockchain (Lu et al., 10 Nov 2025). Each source $s_i$ exposes retrieval APIs and registers its public key on-chain; post-retrieval, the orchestrator updates source scores based on correctness and informativeness (Shapley value or conditional $V$-information) of retrieved snippets. The retrieval and rerank algorithm combines neural reranking and the normalized source reliability $\sigma(R_i)$:

$$\text{score}_{\text{final}} = (1 - \alpha) \cdot \text{score}_{\text{rerank}} + \alpha \cdot \sigma(R_i)$$

Smart contracts ensure auditable, tamper-evident recording of source reliability, supporting both sampling and reranking. Empirical results confirm that dRAG achieves $+10.7\%$ improvement over centralized RAG in noisy data environments, and converges to near-upper-bound performance under reliable sources, all with approximately 56% marginal cost savings through batched on-chain updates (Lu et al., 10 Nov 2025).

6. Biological Analogs and Cognitive Models

Decentralized memory retrieval also finds strong analogs in neural models and systems neuroscience. Modular autoassociators model cortical memory as a dilute, randomly coupled network of feature-specific modules (Mari, 2021). Each module locally recalls features via intramodular Hebbian dynamics, while intermodular associations enable global pattern retrieval through a decentralized activation cascade. Retrieval proceeds through oscillatory “robustness” regimes favoring spread and stabilization, with spurious activation prevented by requiring compound support from multiple independent neighbors. The information capacity of such modular networks saturates the theoretical upper bound $P_c \propto F^2 / \ln F$ for $F$ features per module. Fluctuations in intermodular connectivity are conjectured to induce spontaneous semantic hierarchy emergence.

Blockchain-inspired models of episodic memory posit that the brain’s encoding, organization, sharding across subsystems, and probabilistic “consensus” for conscious recollection closely parallel distributed ledger mechanisms—event linking, pattern separation (“hashing”), functional sharding, and metacognitive reality-monitoring (Cho et al., 2018). These insights motivate architectural analogs and experimental predictions for neural and technical decentralized retrieval.

7. Summary Table: Key Methodological Dimensions

Approach	Retrieval/Storage Paradigm	Decentralized Mechanism
Coded Caching (Maddah-Ali et al., 2013, Wei et al., 2018)	Random non-coordinated placement, coded delivery	No placement coordination, multicast XORs
SHIMI (Helmi, 8 Apr 2025)	Hierarchical semantic tree, top-down search	Merkle-DAG/Bloom/CRDT sync
De-DSI (Neague et al., 18 Apr 2024)	DSI ensemble, docID generation	Data sharding, gossip training
Spatio-Temporal Small Worlds (Groh et al., 2012)	Local agent stores, context-based routing	Greedy small-world traversal
dRAG (Lu et al., 10 Nov 2025)	Multi-source, reliability-scored retrieval	Blockchain-secured scoring
MAICC (Jiang et al., 13 Nov 2025)	Decentralized trajectory retrieval for MARL	Fine-grained embedding, selective memory
Modular Cortex (Mari, 2021)	Sparse modular attractor network	Cued cascade, distributed associations

8. Outlook and Cross-Domain Relevance

Decentralized memory retrieval regroups distinct traditions—information theory, distributed systems, computational neuroscience, and large-scale AI—around the goal of efficient, robust, and trustworthy knowledge access without centralization. Advances in decentralized code design, semantic hierarchies, context-guided routing, reliability certification, and biologically plausible autoassocative dynamics continue to transform memory architectures in both technical and computational biological domains.