A New Field of Artificial Intelligence
Geometric Context Modeling is a new paradigm for AI that tracks where a conversation is in semantic space. It runs on any CPU, uses 52 bytes of memory regardless of conversation length, and processes language at 44,000 to 97,000 tokens per second.
Each point is a word. Each axis is a semantic dimension.
The trajectory is conversational context. The geometry is the meaning.
52
Bytes of context memory at any conversation length
97k
Tokens per second on a standard CPU
27
Experiments completed over three months
100%
Geometric convexity across 15 consecutive experiments
The Landscape
01 / 03
Meaning as probability distributions over token sequences. Predicts what word comes next. Requires GPU infrastructure. Context grows without limit.
Computational primitive: token probability distribution
02 / 03
Meaning as discrete facts, entities, and rules. Interpretable but brittle at the boundaries of what was explicitly encoded.
Computational primitive: discrete fact
03 / 03 — New
Meaning as position in a structured geometric space with defined axes, distances, and directions. Context is a trajectory. No token prediction required. Runs on any CPU.
Computational primitive: semantic position in structured space
Start Here
How meaning becomes geometry, why context is a trajectory, and how GCM relates to the transformer paradigm.
Key findingsGeometric convexity, the physical/cultural ceiling, and the sycophancy finding with a Gram cosine of 0.744.
Research seriesThe complete KAIA research series including the mathematical track proposal and Paper 7 on geometric generation.