compression-aware intelligence (CAI)

LLMs compress large amounts of meaning/context/latent assumptions into finite internal representations. When the semantic load is close to those limits, small surface changes can push the model into a different internal pathway even though the meaning hasn’t changed. The output stays fluent but coherence across prompts breaks

This is compression-aware intelligence and its a way of explicitly reasoning about what happens when meaning exceeds representational capacity. Helps explain why LLMs contradict themselves on semantically equivalent prompts