Modular Blockchain Meets Autonomous Intelligentsia: How MEMO Reinvents the Underlying Logic of AI Agent?
At the critical stage when AI Agent is gradually moving from the lab to industrial landing, the modular data layer architecture proposed by MEMO is triggering a silent revolution. This technical system, with EB-level self-healing storage network as the base and group intelligent collaboration mechanism as the vein, is reconstructing the underlying operation logic of AI Agent. We reveal how it breaks through the triple dilemma of the existing paradigm by deconstructing the technical coupling between its storage layer, processing layer and DA (Data Availability) layer.
Storage Layer: Building the “Memory Hub” for AI Agents
MEMO’s distributed storage network consists of 50,000 nodes around the world, using dynamic redundancy sharding technology to achieve data self-healing capability with a failure rate of less than one in a billion. This feature provides triple re-infrastructure support for AI Agent:
1. Memory disaster recovery mechanism: Each data block is divided into 128 slices and stored in 12 geographic regions across 3 continents. When a node goes offline due to a natural disaster in a region, the AI Agent can reorganize the data through BLS12–381 curve signature within 0.3 seconds to ensure memory continuity.
2. Group Knowledge Sharing: Through the global index tree constructed by the Merkle forest structure, different groups of AI Agents can share data fingerprints without exposing the original content. Diagnostic Agent groups in the medical field have used this to realize cross-organizational knowledge fusion and improve model accuracy by 27%.
3. Time-aware storage: Combined with the hybrid deployment of satellite nodes and ground servers, the weather prediction Agent is able to call on both historical climate data (stored in deep-sea servers) and real-time satellite remote sensing data (cached by edge nodes), improving the timeliness of hurricane path prediction by six hours.
This storage architecture breaks through the physical limitations of traditional centralized cloud services, enabling AI Agent’s memory capacity to jump from terabytes to petabytes, while compressing memory call latency from minutes to milliseconds.
Processing layer: activating data “synapses”
The modular design of the processing layer elevates the efficiency of data value extraction to a new dimension, and its core breakthroughs are reflected in three technical coupling points:
1. automated indexing engine:
The semantic understanding model trained by the federated learning framework can automatically build multimodal indexes for EB-level data. When the game development Agent needs to call medieval armor materials, the system not only returns 3D model files, but also associates 28 types of derived data such as historical documents and material physical parameters. The engine adopts a knowledge graph nested structure, which improves the efficiency of cross-platform migration of IP by 40 times.
2. Intelligent Transmission Optimizer:
The path selection algorithm based on reinforcement learning can dynamically adjust the transmission strategy according to network conditions. In the autonomous driving Agent cluster cooperative training scenario, the transmission path of road data will intelligently select 5G base stations, low-orbit satellites or Telematics V2X channels, compressing the transmission time of the 100TB training set from 72 hours to 45 minutes. The system’s traffic sensing module also predicts network congestion after 30 seconds and initiates data preloading in advance.
3. Dynamic cleaning protocols:
Introducing the data quality assessment system of Adversarial Generative Network (GAN), it can identify and repair the labeled data with bias. When the Financial Risk Control Agent handles cross-border transaction records, the system can automatically complement the exchange rate fluctuation curves of seven currencies, increasing the accuracy of abnormal transaction identification from 82% to 96%.
These technologies enable data processing costs to be reduced to 1/5 the cost of traditional cloud services while increasing the decision confidence of AI Agents by 3 orders of magnitude.
DA layer: building the “immune system” of trust
Challenge mechanisms and validation protocols in the data availability layer build a trusted foundation for group collaboration of AI Agents:
1. Dual validation architecture:
A hybrid verification system combining Zero Knowledge Proof (ZK) with homomorphic encryption allows Agents to accomplish consensus without decrypting data. The medical research Agent swarm used this to achieve cross-institutional model training — 100,000 encrypted medical records were ZK-validated and put into federated learning, with a final diabetes prediction model F1 value of 0.91, without touching the original data throughout.
2. Mobility perception model:
When the monthly sales of an IP derivative product exceeds the 100 million yuan threshold, the LSM (Liquidity Sensitive Model) dynamically adjusts the verification node weights. In the global distribution of music IP, the system automatically increased the verification weight of the North American node from 15% to 32%, effectively curbing the spread of piracy.
3. Anti-witch attack networks:
By binding node reputation to more than 200 parameters such as physical location and energy consumption characteristics, we construct a verification system with spatio-temporal constraints. In a recent stress test, the system successfully recognized 132 witch attackers disguised as Canadian nodes, with a false positive rate of only 0.0007%.
This mechanism enables data validation speeds of up to 120,000 times per second, while reducing consensus energy consumption to 1/60th that of traditional blockchains.
Group intelligence: from mechanical collaboration to organic evolution
MEMO’s technology matrix has spawned a new Agent collaboration paradigm whose evolutionary path exhibits three characteristics:
1. Distributed cognitive architecture:
Each Agent is both an independent decision-making unit and a synapse of the global knowledge network. In smart city management, the Transportation Dispatch Agent and the Grid Regulation Agent achieve millisecond demand response and cut the peak regional energy consumption by 19% through MEMO’s heterogeneous data conversion engine.
2. Emergent learning mechanisms:
The group behavioral data flows back to the processing layer through the challenge mechanism in the DA layer, forming a closed loop of self-enhanced training. the DeFi market’s Arbitrage Agent cluster has demonstrated the ability to innovate strategies beyond a single model, with a 230% increase in portfolio returns compared to a single Agent.
3. Cross-chain value networks:
The asset bridge built through the IBC protocol enables lossless migration of virtual IP to Unity, Decentraland and other platforms. A certain meta-universe concert IP generated 82 cross-chain transactions in 7 days, with an error rate of <0.3% for real-time settlement of royalties.
This evolution has allowed the Agent population to begin to exhibit adaptations to biotic-like organizations, showing amazing resilience in response to black swan events.
Paradigm revolution: reconfiguring the “basic metabolism” of the digital world
MEMO’s technology practices are rewriting three industry laws:
1. Breaking the von Neumann bottleneck: the deep coupling of the processing layer with the storage layer eliminates the need to carry data back and forth between the computing unit and the storage medium. As a result, the decision latency of the self-driving Agent is reduced from 120ms to 9ms, approaching the speed of human neural reflexes.
2. Re-establishing the order of value distribution: smart contracts have increased the share of creators’ income from 15% to 65%-80%, and some developers have already received more than 230 million yuan in real-time share through this system.
3. Redefining data life cycle: RAFI anti-destruction storage technology extends the preservation period of civilization data from 10 years to 1,000 years, and for the first time, mankind realizes memory inheritance beyond biological carriers in the digital field.
The change triggered by the modularized architecture is evolving the AI Agent from an execution tool to a “basic cell” of the digital ecosystem. When 50,000 distributed nodes and hundreds of millions of intelligent bodies form a resonance, a digital civilization with self-repairing and autonomous evolution capabilities is taking shape. This is not only a technological leap, but also a key turning point for human beings to build a credible digital society — here, every data atom flows with the gene of freedom, and every intelligent body shines with the spark of creation.
