How to Make LLMs Understand Cause and Effect through Reflective Intelligence
- Mark Miller
- Jul 22
- 2 min read
Most AI today is built to predict what comes next, but not why things happen. At Abide AI, we’re developing a new kind of intelligence - systems that can reflect on their own past actions, understand cause and effect, and adapt over time.
Our approach, called Reflective Intelligence, is powered by a mathematical framework that tracks how small changes ripple through complex systems. This allows AI to explain its decisions, stay consistent over time, and work better with humans and other agents.
What you’ll learn:
Why prediction isn't enough for truly intelligent systems
How modeling causality changes how AI learns and adapts
Real-world applications where Reflective AI outperforms LLMs
Abi's session, "How to Make LLMs Understand Cause and Effect, will be streamed globally from Lisbon, Portugal on September 16-17, 2025 as part of The Artificial UnIntelligence Conference.
About Abi Aryan
Abi Aryan is the founder of Abide AI and a machine learning engineer with over nine years of experience developing production-level ML Systems. She is also the author of two upcoming books - LLMOps: Managing Large Language Models in Production, O'Reilly (now available to pre-order) and GPU Engineering for AI System, Packt.
Abi was a visiting research scholar at the Cognitive Sciences Lab at UCLA under Dr. Judea Pearl where she worked on developing intelligent agents. She has also authored research papers in AutoML, multi-agent systems, large language models and serves as an active reviewer for ACL, EMNLP, AABI and others.
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