KAIA: A Geometric Architecture for Semantic Reasoning in Resource-Constrained Environments

Abstract

This paper introduces KAIA (Knowledge Architecture for Intelligent Agents), a geometric architecture for semantic reasoning that operates entirely on CPU hardware without requiring GPU infrastructure. The architecture represents meaning as position in a 13-dimensional semantic axis space, maintains conversational context in a 52-byte fixed-size state space model, and stores and retrieves episodic memories through a dimensional database addressed by geometric position. Seven benchmarks for agent-oriented semantic reasoning are defined and validated: intent classification at 70%, context relevance ranking at 80%, semantic similarity scoring at 80%, analogy completion at 65% top-3, memory retrieval at 70% with 897,000 queries per second, and agent routing at 85%. Token throughput is 44,000 to 97,000 tokens per second on a single CPU core. The paper documents the reframing from next-token prediction benchmarks to agent-task benchmarks, which correctly characterizes what the architecture does.