Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra
Jul 12, 2026 · 16:25
Raahul Singh and Vanč Levstik of Phaidra argue that LLMs fail at scale with physical infrastructure data, a problem they call 'Semantic Blindness.' They show how deploying LLMs on 500,000 real sensors led to three failure modes: the topology trap (embeddings ignore physical causality), the illusion of scale (attention degrades with context), and the repetition kill switch (identical naming triggers model's repetition penalty). Their solution replaces raw context with a hierarchical tree, letting an LLM plan a search pattern and a deterministic resolver execute set operations via pre-indexed subtrees. This achieved 100% accuracy across 460,000 GPUs versus 30% with old approaches, cut tokens from 116 million to 390,000 per eval, and kept query cost flat at 9,000 tokens. They advocate starting with pure LLM for demos then migrating deterministic logic into code, inverting the traditional software evolution path.
