ESCA Memory Architecture
Last updated
Last updated
ESCA implements a sophisticated hierarchy of memory layers, enabling AI agents to operate as lifelong learning companions.
Cognitive Architectures for Language Agents" (Yao et al., 2023° ) discusses the structuring of AI agents through frameworks like CoALA, which leverages modular memory components and decision-making procedures. This is relevant as it shows how agents can utilize both working and long-term memories to manage immediate context for effective interaction.
Below are the four key components:
Purpose: Real-time interaction processing.
Key Features:
Tracks user engagement during live learning sessions.
Adapts recommendations and teaching methods based on active input.
Example: “You’ve struggled with this concept. Let’s explore an alternative explanation.”
Purpose: Analyzes recent learning activity.
Key Features:
Retains short-term history of completed modules, mistakes, and corrections.
Identifies emerging learning patterns and trends.
Provides performance analytics and progress tracking.
Example: “You’ve improved by 15% in understanding Chain-of-Thought prompts over the last week.”
Purpose: Builds a holistic profile of the learner over time.
Key Features:
Tracks skill development, strengths, and weaknesses.
Logs learning preferences and achievements. (Personalization)
Provides career and learning path recommendations based on historical data.
Example: “Your interest in 'Trading with AI' suggests exploring advanced topics in hedge fund strategies and machine learning modelling.”
Integration with RAG Techniques:
Inspired by "MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery” (Qian et al., 2024), LTM employs dual-system memory, where lightweight LLMs maintain a global view and expressive LLMs focus on context-specific retrieval and synthesis.
Purpose: Captures significant milestones and learning events.
Key Features:
Retains major achievements, certifications, and projects.
Logs collaborative experiences and breakthroughs.
Highlights significant moments for reflection and motivation.
Example: “You achieved your highest project score on collaborative AI systems last month. Also, congrats on your new badge!”
Open Campus Integration:
Open Campus is a Layer 3 blockchain specifically designed for educational purposes. It allows for the creation of decentralized applications (DApps) that can enhance the educational experience. EDU Chain supports a "Learn Own Earn" model, empowering learners and educators by enabling them to own their academic records and achievements on the blockchain.
Launched in January 2024, this decentralized identifier (DID) allows users to create unique profiles that securely store their educational credentials. The Open Campus ID facilitates personalized learning experiences by enabling adaptive learning pathways based on individual needs. It also supports the issuance of verifiable credentials in a privacy-preserving manner, ensuring that learners have full control over their data.
By integrating Open Campus ID into ESCA Memory Architecture, we enable the AI Principals to itneract with EDU Chain, retrieve and post learner updates seasmlessly.
