# Membria: A Decentralized Knowledge Framework for Tiny LLMs

The rapid adoption of lightweight, on-device Large Language Models (Tiny LLMs) has created a critical bottleneck in personalization, knowledge freshness, and reasoning capabilities. While millions of users deploy these models for everyday tasks, their ability to learn and reason remains limited, requiring costly and slow interventions via centralized APIs or fine-tuning.

We introduce Membria, a decentralized, verifiable, and efficient knowledge reasoning framework built around a Knowledge Cache Graph (KCG) and Cache-Augmented Generation (CAG). Instead of relying on retraining, Membria enables Distillation on Demand (DoD), where knowledge is distilled, validated, and recorded in a public, immutable knowledge cache. This approach empowers Tiny LLMs to dynamically retrieve, reason over, and consume high-quality, validated knowledge via fast Selective Contextual Reasoning (SCR) pipelines, dramatically reducing inference costs, latency, and vendor lock-in.

By creating an ecosystem where knowledge grows, self-validates, and becomes reusable, Membria transforms AI reasoning into an open, democratic, and self-improving infrastructure for millions of Tiny LLMs.

The rise of Tiny LLMs is transforming the AI landscape by bringing generative intelligence to billions of devices. However, this explosion of local inference introduces fundamental bottlenecks:

• Static and Outdated Models: Once deployed, Tiny LLMs quickly become stale, as they cannot natively update their knowledge base or integrate new information without retraining.
• Costly and Centralized Updates: Existing methods for updating models, such as LoRA or QLoRA fine-tuning, require cloud GPUs, expert intervention, and significant time and money.
• Dependency on Centralized APIs: Users and applications frequently fall back on large LLM APIs (e.g., GPT, Claude, Gemini) to access fresh or complex knowledge, incurring high costs and introducing privacy, latency, and control issues.
• Absence of Shared Memory: Tiny LLMs operate in isolation, lacking access to a federated, verified, and continuously growing knowledge layer they can rely on.

These limitations block the scalability of Tiny LLM reasoning capabilities, fragment knowledge across devices, and create systemic inefficiencies. There is a critical need for a new approach where Tiny LLMs can dynamically acquire, reason over, and integrate fresh, verified knowledge without retraining, centralization, privacy loss, or inefficiency.

The primary purpose of integrating a system like Membria with Tiny LLMs stems from the need to overcome their inherent limitations without losing their efficiency advantages.

Based on their pre-trained knowledge, standalone Tiny LLMs are capable of:

• General Text Generation: Creating coherent text, drafting emails, or simple content.
• Basic Summarization: Condensing provided text into shorter versions.
• Simple Q&A: Answering questions with answers likely contained within their training data.
• Limited Reasoning: Performing simple reasoning if concepts are well-represented in their training.

However, they are limited by their static knowledge, a higher propensity for hallucination, and an inability to access private or real-time data sources.

Integrating a Tiny LLM with Membria significantly expands its capabilities:

• Factually Grounded Q&A: Answering questions based on specific, up-to-date external documents, drastically reducing hallucinations.
• Context-Aware Analysis: Summarizing and analyzing large volumes of external or private documents the LLM was not trained on.
• Personalization: Delivering personalized responses based on user history or private contextual data managed by Membria.
• Domain-Specific Expertise: Functioning as an expert in niche domains (legal, medical) by accessing specialized knowledge bases.
• Real-Time Information Processing: Incorporating live data feeds or recently updated information into responses.
• Complex Reasoning: Synthesizing information from multiple sources provided by Membria to answer complex queries.

In essence, Membria empowers Tiny LLMs to perform tasks that would otherwise require much larger, more resource-intensive models, by intelligently providing the right information at the right time. This combination offers a powerful and practical solution for deploying AI that is both capable and efficient.

To overcome the personalization and reasoning bottleneck for Tiny LLMs, we propose the Knowledge Cache Graph (KCG) combined with Cache-Augmented Generation (CAG), a decentralized knowledge reasoning layer designed for scalable, efficient, and continuous learning without retraining.

• Knowledge Cache Graph (KCG): A decentralized, immutable, and verifiable knowledge graph layer, built on permanent storage. It stores distilled knowledge entries, verified entity relations, and embedded key-value caches for fast retrieval.
• Cache-Augmented Generation (CAG): An advanced pipeline where Tiny LLMs first query local or Gateway KV caches from the KCG layer and use Selective Contextual Reasoning (SCR) to reason over retrieved knowledge, minimizing reliance on external APIs.
• Distillation on Demand (DoD): A mechanism that allows Tiny LLMs to trigger on-demand distillation of new knowledge when gaps are detected. This new knowledge is then validated and recorded into the KCG, making it reusable for all network participants.

The KCG+CAG ecosystem is composed of several interconnected layers and actors, forming a decentralized and efficient knowledge reasoning pipeline.

• Knowledge Cache Graph (KCG): The immutable knowledge layer stored on Arweave, containing distilled knowledge, semantic relations, KV-cache entries, and proof-of-knowledge metadata.
• Cache-Augmented Generation (CAG) Layer: The reasoning layer that enables Tiny LLMs to perform fast retrieval and reasoning over verified knowledge using SCR pipelines.
• Distillation on Demand (DoD) Agents: Specialized agents or Tiny LLMs that detect knowledge gaps and orchestrate the creation of new, distilled knowledge.
• Gateways: Federated nodes that act as intermediaries, responsible for knowledge validation, packaging, recording entries into the KCG, and managing off-chain indexes and caches for fast retrieval.
• Validator Nodes: Participants in consensus voting and knowledge verification, ensuring correctness and providing auditability.
• Hybrid KV-Cache Architecture: A multi-layered caching system including an on-device cache, a shared Gateway cache, and a public, immutable cache within the KCG.

This modular, multi-layered architecture ensures that Tiny LLMs can learn dynamically while Gateways and Validators enforce knowledge quality, all while reducing inference costs and latency.

Application & Agent Layer  
└── Tiny LLMs, User Devices, Enterprise Agents  
  
CAG Reasoning Layer (on top of Peaq)  
└── Cache-Augmented Generation, SCR Pipelines, DoD Agents, Gateway Cache & Index  
  
KCG Core Layer (on Peaq)  
└── Knowledge Cache Graph, Distilled Knowledge, Relations, KV Layer (on-chain)  
  
Peaq Protocol Layer  
└── Validator Network, Consensus, Staking, ZK Proof Layer, Subgraph Indexing

The system introduces an optimized workflow that allows Tiny LLMs to acquire, reason over, and integrate fresh knowledge efficiently.

1. DoD Request Trigger & Local Evaluation: A DoD Agent identifies a knowledge gap. It first performs a Self-Knowledge Checkpoint to intelligently decide whether to answer using its internal knowledge, the cache, a local RAG system, or to escalate to external distillation. This local-first approach prevents unnecessary computation.
2. SCR Reasoning Pipeline via Gateway: If the knowledge is not available locally, the agent triggers an SCR pipeline via a Gateway to retrieve information from the KCG and perform local reasoning.
3. Fallback to Big LLM (if necessary): Only for novel, ambiguous, or high-confidence queries does the agent escalate the request to external Big LLM APIs.
4. Knowledge Distillation Proposal: Based on the reasoning output, the agent synthesizes a distilled knowledge proposal with a summary, sources, and context.
5. Gateway Validation and Recording: The proposal is submitted to a Gateway for validation. If validated, the Gateway records the entry into the immutable KCG on Arweave and updates its own high-speed caches.
6. Confirmation and Reward Distribution: The DoD agent receives confirmation, and incentives are distributed to participating Gateways and Validators.

This workflow, which defaults to SCR and local processing first, dramatically reduces calls to expensive Big LLMs, ensures the fastest possible retrieval for repeated queries, and creates a globally shared and verifiable knowledge memory.

The KCG serves as the immutable, decentralized knowledge memory layer. It is optimized to store, retrieve, and reason over distilled knowledge efficiently while ensuring verifiability and traceability.

• KCG Data Types:
  • Distilled Knowledge Entries: Core units of validated knowledge with summaries, evidence, and metadata.
  • Entities: Discrete concepts that serve as graph nodes for semantic linking.
  • Relations: Semantic links between entities and knowledge entries (isA, relatedTo).
  • Reasoning Chains: Multi-step logical explanations that allow Tiny LLMs to shortcut complex reasoning.
  • FAQ Patterns: Standardized question-answer pairs for frequent queries.
• Data Format: All data is stored as JSON-LD following linked data principles, ensuring compatibility with semantic web standards.
• Indexing Strategies:
  • KV-Cache Layer: Entries are indexed by query embeddings, canonical questions, and topics for ultra-fast retrieval.
  • Semantic Graph Indexes: Gateways and off-chain nodes maintain semantic graphs of relations and entities for complex reasoning.
  • Proof-of-Knowledge and Metadata: Each entry contains an immutable transaction ID (from Arweave), validator signatures, and provenance records.

Membria’s architecture incorporates advanced caching strategies to maximize reasoning efficiency, minimize costs, and enable dynamic knowledge integration for Tiny LLMs.

SCR enables Tiny LLMs to perform lightweight, dynamic reasoning over external knowledge caches without modifying model weights. The pipeline consists of four steps:

1. Semantic Retrieval: Retrieve relevant knowledge entries from the KV-cache and semantic graph indexes.
2. Confirmation and Filtering: The Tiny LLM or Gateway filters, confirms, and deduplicates retrieved entries to ensure contextual fit and factual accuracy.
3. Contextual Reasoning: Construct an enriched prompt using the confirmed knowledge, allowing the Tiny LLM to perform high-quality reasoning locally.
4. Fallback to DoD: Only if SCR fails or lacks sufficient confidence is a DoD escalation to a Big LLM triggered.

• On-Device Tiny LLM KV-Cache: Located directly on the user's device for fast, personalized, sub-20ms access.
• Gateway KV-Cache: A shared, community-level cache managed by Gateways with 50-200ms access.
• Public KV-Layer in KCG: The immutable, permanent cache on Arweave.
• Data Obsolescence and Versioning: Outdated data is not deleted from the immutable KCG. Instead, new versions are created, and the mutable Gateway indexes are updated to point to the current information. The system can mark and deprioritize older entries.
• Cold Layer Management: Gateways manage cache layers (hot, warm, cold). Entries with low usage or freshness are moved to "off-chain cold storage" on disk, from which they can be recalled if they become relevant again.

• Segmented KV Buffer & Prioritized Paging: To manage memory efficiently, each DoD Agent maintains a segmented buffer divided by scope (Session Memory, Local Knowledge Cache, Global Shared KV Layer). Before inference, the agent performs semantic prefiltering and priority ranking to select the top entries to load into active memory, evicting least-recently-used items if memory is constrained.
• Persistent Memory for Tiny LLMs: To prevent model "amnesia" between sessions, the agent uses a hybrid storage solution. Reasoning outcomes are distilled into a local vector database (like Qdrant + SQLite) for semantic retrieval, while key attention snapshots are persisted to a fast disk-based store (like LMDB) for partial context reconstruction on session restart.
• Local Knowledge & Event Storage: On the device, the agent uses a lightweight infrastructure for its memory. This includes a local event graph (in SQLite) to log actions and reasoning chains, a minimal ontology layer for semantic classification, and an efficient local database (SQLite with JSON1 extensions) for caching and retrieval.

The ecosystem is supported by distinct roles, each playing a critical part in the knowledge pipeline.

DoD Agents are the orchestrators of knowledge creation. They can be deployed on a wide spectrum of hardware, adapting their role and capabilities accordingly. On low-power mobile devices, they act as edge interaction points, while on high-performance servers, they can be central knowledge hubs performing advanced analytics and knowledge distillation for the entire network.

• Responsibilities:
  • Identify knowledge gaps and initiate reasoning pipelines.
  • Perform local evaluation using the Self-Knowledge Checkpoint.
  • Aggregate outputs to propose new, distilled knowledge.
• Incentives: Earn rewards for valuable queries and benefit from lower costs via local caching.

Gateways are federated nodes that serve as the crucial intermediaries and active coordinators of knowledge quality.

• Responsibilities:
  • Validate, package, and record knowledge proposals into the KCG.
  • Operate high-performance KV-caching services and off-chain semantic indexes.
  • Enforce quality and anti-spam measures.
• Gateway Reasoning Orchestration & Knowledge Gap Detection: Gateways monitor query traffic to detect knowledge gaps and "hot topics" (e.g., frequent cache misses, spikes in similar queries). When a pattern of deficiency is detected, they can proactively generate and broadcast incentivized distillation tasks.
• Incentivized Task Routing: These proactively generated tasks include a bounty and resource requirements, creating an open market for reasoning services. Agents can accept or reject tasks based on their device status and the offered reward.
• Discovery and Efficient Selection: Agents discover suitable Gateways via a decentralized service registry (e.g., based on the Peaq Protocol), selecting them based on proximity, load, and reputation, ensuring efficient routing.

Validators are the guardians of the ecosystem's integrity and consensus.

• Responsibilities:
  • Participate in voting and validation of proposed knowledge entries.
  • Ensure factual correctness and semantic consistency.
  • Provide audit trails and proof-of-knowledge attestations.
• Incentives: Earn validation rewards from token flows and receive staking benefits.

Membria establishes trust and verifies information without a central authority through a robust, multi-layered process of validation, consensus, and cryptographic proofs. This is the foundation for ensuring data integrity.

1. Gateway Validation (First Line of Defense): Gateways serve as the critical first checkpoint. They perform a series of automated checks on all incoming data submissions.
   • Data Integrity Checks: Type checking, range constraints, uniqueness, completeness, and checksums.
   • Data Formatting Checks: Syntax validation (JSON), schema compliance (JSON Schema, OpenAPI Specification), and validation of specific formats like emails or dates using regular expressions.
   • Specifics for JSON-LD and Metadata: Gateways validate JSON-LD syntax and structure, resolve its @context, and can validate the data against additional schemas like SHACL or standard metadata formats like Dublin Core and Schema.org.
   • The Process: This automated process involves security checks, parameter validation, content-type validation, and schema compliance checks. If any check fails, the gateway rejects the request with an error, preventing invalid data from reaching the network.
2. Validator Node Consensus (Final Agreement): For a knowledge entry to be permanently added to the KCG, it must be approved by the Validator Nodes. The system uses a hybrid consensus model that combines the strengths of multiple approaches.
   • Proof-of-Stake (PoS) is used for validator selection and incentives, ensuring a decentralized and economically secure pool of participants.
   • Byzantine Fault Tolerant (BFT) protocols are used by a smaller, rotated committee of validators to achieve rapid and irreversible agreement on the content of KCG blocks.
   • Finality: Once the BFT committee reaches consensus, the data is immutably added to the KCG, ensuring its finality.
3. Proof-of-Knowledge (PoK): To enhance privacy and verifiability, the system uses PoK protocols, often in the form of Zero-Knowledge Proofs (ZKPs).
   • Core Concept: A Prover convinces a Verifier of possessing specific knowledge (a "witness") without revealing the knowledge itself. This is formalized by the existence of a hypothetical "knowledge extractor."
   • zk-SNARKs: A powerful type of ZKP that provides succinct (small), non-interactive proofs, ideal for blockchain applications.
   • Use Cases: PoK can be used to prove that a private computation was performed correctly, that a validator has checked a fact against a confidential database without revealing it, or to validate contributions in federated learning (ZKPoT) without sharing the ML models.

This integrated hybrid model ensures that only well-formed, policy-compliant, and verified knowledge becomes a permanent part of the shared graph.

To protect against misinformation and abuse, the system employs a layered defense:

• Local Prefiltering: DoD agents use a tiny reward model (TinyRM) to locally filter out obviously flawed or low-quality reasoning chains.
• Distributed Validation: A quorum of Gateway or Validator nodes (e.g., √N sampling) performs inference-based fact-checking on proposed knowledge, cross-checking claims against existing KCG entries.
• Consensus-Based Acceptance: Reasoning is accepted if the quorum reaches a confidence threshold, and the result is cached to prevent redundant checks.
• Spam Detection and Anti-Abuse: Gateways track statistical metadata to detect suspicious patterns. Responses are scored for coherence and novelty, and any flagged entries are tagged with a SPAM label in the ontology layer to prevent their future use.

Code snippet

graph TD
A[DoD Agent sends Reasoning] --> B[TinyRM Local Filter]
B -->|Valid| C[Gateway Selection]
C --> D[Quorum Gateways (√N)]
D --> E[SLM-based Fact-Checking]
E --> F{Consensus Reached?}
F -->|Yes| G[Store in Gateway Cache]
G --> H[Write to KCG]
F -->|No| I[Reject Reasoning]
I --> J[Log as Disputed]

To maintain long-term integrity, Membria features an economically incentivized challenge system.

1. Challenge Initiation: Any network participant with sufficient stake can challenge an entry in the KCG by locking their stake as collateral and providing evidence.
2. Verification Phase: A committee of validators re-evaluates the challenged assertion, weighing evidence from both the challenger and the original entry.
3. Resolution: The outcome is decided by a stake-weighted vote.
   • If the challenge succeeds: The entry is marked as incorrect, the challenger recovers their collateral plus a reward, and the validators who approved the incorrect entry are penalized (slashed).
   • If the challenge fails: The challenger's collateral is slashed, deterring frivolous disputes.

This mechanism relies on the economic incentives of the network's participants to maintain truth, rather than a centralized arbitration body. In extreme cases, appeals can be made by re-challenging with a higher stake or, as a last resort, through a community-driven general audit or hard fork.

The KCG+CAG ecosystem introduces a deflationary, utility-driven token model designed to reward key participants, sustain decentralized infrastructure, and ensure long-term economic balance.

• DoD Query Payments: Every DoD request initiated by a DoD Agent incurs a fixed token fee. This covers API calls, storage costs, and validation.
• Validator & Gateway Rewards: Validators and Gateways are compensated in tokens for validating proposals, maintaining caches, and hosting retrieval services.
• Token Burning Mechanism: A percentage of each DoD request fee is burned (removed from circulation), creating deflationary pressure.

The network treasury, which funds infrastructure like off-chain indexing nodes and dispute resolution rewards, is replenished through a sustainable and balanced multi-source approach.

• Transaction Fee Allocations: A portion of DoD query fees is directed to the treasury, directly correlating funding with system usage.
• Dedicated Vesting Fund: A significant portion of the total token supply is reserved under a long-term (e.g., 20-year) vesting program. A predictable amount is unlocked periodically to provide stable funding for critical security functions like the dispute resolution mechanism.
• Slashing Penalties: A portion of tokens slashed from malicious actors can be routed to the treasury, acting as both a punitive measure and a source of replenishment.

The integrity and fairness of the ecosystem are ensured by a decentralized governance model and a distributed network of Validator Nodes.

• Roles: Validators are responsible for knowledge validation, consensus voting, proof-of-knowledge verification, and participating in governance to influence protocol upgrades and dispute resolutions.
• Infrastructure: Validators can be permissionless community nodes or elected via governance mechanisms, running lightweight nodes capable of verifying proposals and participating in voting.

• DAO-Driven or Federated Governance: The protocol may adopt DAO governance models, where token holders vote on key parameters, or federated validator councils for efficient decision-making.
• Responsibilities: Key responsibilities include managing protocol upgrades, validator slashing mechanisms, dispute resolution, and treasury management.

The Membria architecture can be seamlessly deployed on top of the Peaq Protocol, leveraging its existing decentralized infrastructure for enhanced functionality and faster development.

Benefits of Deploying over Peaq:

• Validator Network and Governance: Membria can adopt Peaq's validator network, staking mechanisms, and DAO governance.
• ZK Proof Layer: Peaq's ZK Layer can serve as the foundation for query privacy and proof-of-knowledge attestations.
• Optimized Querying: Utilizing Peaq's subgraph and indexing services can accelerate SCR pipelines without duplicating infrastructure.

Peaq Protocol provides a flexible and extensible semantic layer ideal for Membria.

• Core Ontology Features: Peaq supports typed knowledge nodes (@type), domain and tag metadata (@domain, @tags), supertype hierarchies, and semantic relationships, all of which are compatible with RDF-like logic.
• Subgraph Indexers: Each domain can define a custom Subgraph Indexer to parse and index knowledge entries by type or topic, enabling efficient, scoped querying.
• Suitability for Membria: Peaq’s native ontology support allows Membria to filter reasoning by topic, define custom types (ReasoningStep, DoDTrace), and enforce validation rules per knowledge category, maintaining clarity and modularity in a distributed environment.

While Membria's core focus is real-time knowledge updates via CAG/SCR, it can be synergistically combined with periodic, low-frequency model adaptation using techniques like LoRA/QLoRA.

An automated nightly pipeline could extract new, high-value knowledge accumulated in the KCG and use it to fine-tune the base Tiny LLM. This process "embeds" stable, frequently used reasoning patterns into the model itself. The benefits include:

• Improved Base Understanding: The model becomes better at generalizing and understanding the types of knowledge in the KCG.
• More Efficient SCR: A better-adapted model may require less context from the SCR pipeline to achieve high-quality responses.
• Adaptation to Evolving Knowledge: The model's "base" adapts to macro-level shifts in the KCG's content over time.

This combination allows the model to learn in two ways: instantly through caching (CAG) and periodically through fine-tuning (LoRA), creating a highly adaptive and efficient learning system. Using QLoRA for this process is particularly effective, as it allows for significant model enhancement with minimal or even no increase in the final model size on the device.

The KCG+CAG ecosystem bridges the gap between heavy, centralized LLM inference and lightweight, efficient Tiny LLMs operating at the edge. By introducing an open, decentralized, and verifiable knowledge graph, coupled with Cache-Augmented Generation (CAG) and Selective Contextual Reasoning (SCR), we enable Tiny LLMs to stay continuously updated, smart, and capable - without costly retraining or vendor lock-in.

This paradigm shift turns reasoning and knowledge augmentation into an open, reusable, and community-driven resource, breaking free from centralized control and enabling AI models to reason dynamically using decentralized knowledge caches.

We envision a world where:

• Tiny LLMs become truly autonomous learners, continuously improving and reasoning at the edge.
• Knowledge becomes a public good, verifiable and accessible to all, stored immutably in the Knowledge Cache Graph (KCG).
• Users, agents, and validators collaborate in a self-reinforcing ecosystem, where knowledge grows organically, costs decrease, and reasoning becomes more reliable, democratic, and sovereign.

By adopting the KCG+CAG architecture, we take a significant step toward democratizing AI reasoning, decentralizing knowledge creation, and empowering users everywhere to control, enhance, and benefit from their own intelligent agents.

"Tiny LLMs" are a segment of Small Language Models (SLMs) designed for efficiency and on-device deployment.

• Parameter Range: 4 billion to 30 billion parameters.
• Architectures: Primarily Transformer-based (Decoder-only or MoE), often enhanced with optimization techniques like Grouped-Query Attention (GQA), Quantization (INT4/INT8), and Pruning.
• Computational Resources: Designed to be runnable on modern multi-core CPUs and consumer-grade GPUs (e.g., with 8-24GB of VRAM). They are increasingly targeted for devices with Neural Processing Units (NPUs).
• Advantages: Efficiency, cost-effectiveness, accessibility, customization, and on-device deployment for lower latency and enhanced privacy.
• Limitations: Reduced generalization compared to larger models, a smaller inherent knowledge base, and a higher dependency on fine-tuning for specific tasks.
• Examples:
  • Phi-3 Family (Microsoft)
  • Gemma Family (Google)
  • Llama 3 8B (Meta)
  • Mistral 7B (Mistral AI)
  • Qwen2-7B (Alibaba Cloud)

Membria offers a unique combination of real-time, on-device inference, structured knowledge caching, and decentralized learning that sets it apart from existing solutions.