The Bubble Engine

At the core of DAPPOS's Intelligence Layer is the Bubble Engine, a Web3-focused reinforcement learning (RL) engine that drives perpetual evolution and adaptive intelligence. Powered by advanced mechanisms like Contextual Retrieval-Augmented Generation (RAG) and Compound Memory, the Bubble Engine transforms fragmented Web3 data into coherent, actionable intelligence.

Contextual RAG: Provenance-First Intelligence Aggregation

The Bubble Engine's Contextual RAG forms the backbone of its information processing, unifying diverse sources—web content, internal documents, and on/off-chain signals—into a provenance-first context layer that prioritizes traceability and reliability. This hybrid retriever combines dense (semantic) and sparse (keyword-based) techniques with domain-specific routing to orchestrate sub-queries efficiently. It deduplicates redundant results, reranks them using cross-encoders for relevance, and assembles a minimal sufficient evidence pack tailored to the query's needs.

The generation process is citation-locked, producing 1]-style inline references with graceful fallbacks when evidence is limited. Dynamic budgets intelligently adjust retrieval depth and parallel agent execution based on detected uncertainty levels, optimizing for efficiency in volatile Web3 scenarios. Episodic memory captures short-term learnings from recent interactions, while long-term memory preserves confirmed facts for future recall. Looking ahead, DAPPOS sets the stage for multimodal RAG (incorporating images, videos, and audio) and graph-based extensions, enabling richer contextual intelligence that connects entities, relationships, and temporal dynamics across the blockchain landscape.

Compound Memory: Durable and Episodic Knowledge Evolution

Complementing Contextual RAG, the Bubble Engine's Compound Memory integrates durable knowledge—core, verified Web3 facts and models—with episodic learnings accumulated over time. This approach ensures the engine retains timeless domain expertise (e.g., DeFi protocols and blockchain fundamentals) while dynamically incorporating transient insights, such as real-time market shifts or user-submitted alphas. As new knowledge is verified and integrated, the memory system evolves, creating a compounding effect that enhances reasoning accuracy and personalization.

Handling Misinformation and User Incentives

To maintain integrity in Web3's deception-prone environment, the Bubble Engine employs advanced fake information handling, cross-verifying sources against on-chain data and community flags to detect and mitigate bots, pumps, or manipulated signals. Additionally, Web3 incentives motivate users to share high-fidelity insights, accelerating collective intelligence growth.

In summary, the Bubble Engine redefines adaptive AI for Web3 by blending continuous RL with sophisticated retrieval and memory systems, delivering hyper-relevant, trustworthy insights that evolve in lockstep with the ecosystem.