Temporal Aware AI memory: Why time is a key in a memory
Temporal Aware AI memory: Why time is a key in a memory
Why is time all you need?
About the Book
When we started building AI memory for the personal assistant agent, it was quite straightforward. We used knowledge graphs, and everything was heavy, and it was super easy. You could find a lot of articles on how to turn conversations into graphs. But we noticed quite quickly that after a couple of weeks of the honeymoon period, users started giving us bad feedback, and we also noticed that something wasn't working in the memory.
This non-working component was actually time awareness, because the LLM or the agent didn't know what happens when, and whether it was relevant information or not. Memory is actually quite complex and temporal, and we differentiate between past and future and what's more important.
So this book is actually about time and temporal awareness in memory—how to make the LLM and agent understand time, and how to actually make knowledge graphs temporal. Yes, exactly—knowledge graphs, because knowledge graphs actually matter and they are the core of memory. You'll also learn about events and how to build proper episodic memory, why events are important, and together with time, we'll talk about causality and why causality relations are so important for the agent primarily. So it's a book about causality and time.
About the Author
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Table of Contents
- What it is About
- To Inpatient Reader
- About Book
- About Author
- About Cover
- AI Memory
- Why You Should Build Memory System for AI Agents
- AI Memory is Not RAG, RAG is Not Enough for AI Agents
- Memory Systems & Time
- Built vs. Buy: Finding the Right Memory System
- Zep and the Architecture of Temporal Memory: When Knowledge Graphs Learn to Remember Time
- Conclusion: temporal memory as intelligent infrastructure
- Time and Memory in MEM0: The Architecture of Artificial Recollection
- Congee
- The Human Brain
- How memory work
- How Brain see time
- Semantic memory
- Episodic memory
- How brain forget
- Construction of Recal
- Memory mutation and rewriting
- Post Human Memory
- Temporal Memory challenges
- The Temporal Challenge
- How Language Reshapes Our Experience of Time
- Time disambiguation in Conversations
- How People Talk About Time: Extracting Temporal Information from Human Speech
- Abstract Time
- Time building blocks
- Clocks & Time
- The Keepers of Time
- When There Is No “Now”: A History of Distributed Timekeeping from Monasteries to Machine Learning
- From Timestamps to Truth: How We Learned to Count What Matters
- Beyond Vectors: When Reality Forks Into Many Timelines
- Time, causality, and memory in distributed systems
- Synthesis: Building causally-consistent memory systems
- The Hierarchical Clock — Weaving Order from Chaos
- Forgetting in AI Agent Memory Systems
- Temporal Modeling Foundations
- Time-Aware Personal Knowledge Graphs
- Evolution of Knowledge Graphs and AI Agents
- Time-Aware Knowledge Graphs for Episodic Memory
- Temporal Semantics for AI Memory Agents
- Advanced Temporal Representations
- Event Graphs: Modeling Reality’s Temporal and Causal Fabric
- Event Graphs as Self-Referencing DAGs
- Entity-State Model and Event Graphs for Time-Aware AI Agents Memory
- Advanced Graph Structures
- Beyond Traditional Graphs
- Temporal Relationship Reification in Conceptual Modeling
- The Evolution of Memory Architecture: From Simple Graphs to Complex Temporal Structures
- The Limitations of Traditional Knowledge Graphs
- The Promise and Limitations of Hypergraphs
- Discovering Metagraphs: Power and Impracticality
- Pragmatic Solutions: Named Graphs and Bipartite Graphs
- The Complete Cycle: Back to Directed Graphs
- Architectural Principles and High-Level Ontology
- Implementation Considerations
- Reflection: Lessons from the Journey
- Conclusion: A Foundation for Temporal Memory
- Layered Knowledge Graphs and the Architecture of Memory
- Temporal Reasoning and Analysis
- Temporal Reasoning in AI Agent Memory: Allen’s Interval Algebra and Event Graphs
- Relations Between Events in AI Memory: From Temporal to Causal Understanding
- Relations Between Events in AI Memory: From Temporal to Causal Understanding
- Implementation and Systems
- Synergy in Time: Combining Anchor Modeling and 6NF for Robust Temporal Databases
- Time Traveling for Knowledge Graphs and AI Agents
- The Role of Logs and Append-Only Storage
- Time Travel for Property Graphs
- Time Travel for Property Graphs with Layered Graphs
- Personal Knowledge Graphs: Bringing Knowledge Back to the Individual
- The Personal Knowledge Graph: You at the Center
- Sovereignty and Portability
- Scale and the Concept of Small Data
- The Power of Personal Knowledge Assets
- Semantic Spacetime: Understanding Graph Relationships in Knowledge Representation
- Why similarity and Vectors Emmbedings not always work
- Memory Ontology Design
- Designing Memory Structure Through Ontological Principles
- Concept Graphs on Event “Steroids” for AI Memory
- Bigraphs for Semantic Spacetime: A Promising Framework for Representing Meaning and Structure
- FARO: Modeling Complex Relationships Between Facts and Events
- Memory Design
- The Time Tree - Modeling Temporal Ambiguity
- The Problem with Precise Timestamps
- The Ontology of Memory — Building a Semantic Foundation
- Layered Visibility: Controlling Complexity
- Relations
- Cognitive Processes
- What it is About
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