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

We present KAIA (Knowledge Architecture for Intelligent Agents), a semantic reasoning architecture that replaces transformer-based next-token prediction with a fixed-size geometric representation of meaning. KAIA encodes meaning as position in a 13-dimensional semantic axis space, maintains context in a 52-byte State Space Model (SSM) that does not grow with sequence length, and retrieves from a dimensional database in O(1) time. The system runs at 44,000-97,000 tokens per second on the CPU, with no GPU required. We validate KAIA against a seven-benchmark suite designed for agent-oriented semantic reasoning: intent classification (70%), context relevance ranking (80%), semantic similarity scoring (80%), analogy completion (40/65% top-1/top-3), memory retrieval accuracy (70% at 897,000 queries per second), and agent routing (85%). A critical reframing emerges from the experimental record: transformer benchmarks measuring next-word prediction accuracy are the wrong evaluation framework for a semantic reasoning system. We propose and validate the correct benchmarks for this class of architecture.