Artificial Intelligence Augmentation

Modern AI—from large language models to machine learning–driven routing optimizations—boosts performance, user experience, and security in the Exeos overlay. By leveraging distributed infrastructure and intelligent data processing, the network offers low-latency, up-to-date information, and robust defense against bottlenecks or attacks.

Real-Time Retrieval-Augmented Generation

• Challenge: LLMs can be limited by static or outdated training data.
• Solution: Distributed vector databases on each node perform semantic searches in real time. Relevant passages are integrated into LLM prompts, grounding outputs in fresh context while caching popular queries to reduce latency.

Edge LLM Inference & Chaining

• Concept: Split complex tasks into smaller subtasks processed at the edge.
• Approach: Lightweight, locally deployed LLMs handle routine requests, while more challenging queries are chained to powerful model nodes. This method reduces latency and efficiently balances computational loads.

AI-Enhanced Caching

• Innovation: Beyond static CDNs, the Exeos overlay will cache inference outputs, embeddings, or partial computations.
• Benefit: By identifying semantic similarities between queries, the network can serve repeated requests instantly, reducing redundant processing and improving response times. Over time, the system adapts by prefetching popular topics and rewarding nodes with high accuracy.

AI-Assisted Routing & Security

• Routing: Local ML models predict congestion, enabling proactive re-routing and dynamic load balancing.
• Security: Distributed anomaly detection on each node filters suspicious traffic near its source, mitigating threats (e.g., DDoS) before they impact the network.

Data Collection for AI

• Method: Nodes collaborate to distribute web scraping tasks, gathering fresh data (news, product info, user content) from across the network.
• Outcome: This decentralized data collection fuels ongoing AI training, while on-chain governance and reputation systems ensure ethical and high-quality data aggregation.