How Tree Structure Solves LLM Hallucination at Industrial Scale

Phaidra found that LLMs fail when reasoning about vast inventories of near-identical equipment names—even listing many triggers internal guardrails that halt output. A naive sharding approach hallucinated phantom devices and silently dropped real ones, eroding trust in mission-critical controls. The real bottleneck wasn’t capability but forcing the LLM to search instead of plan.

They exploited the natural tree structure of data centers. A linearizer collapses the massive graph into a compact set of root-to-leaf paths. A planner LLM then outputs a structured search plan—what to collect, the subtree scope, and filters—without ever scanning names. A deterministic resolver executes set intersections on pre-indexed subtrees, guaranteeing perfect recall and accuracy.

Tested on production systems, the new approach jumped correctness from 30% at 64 GPUs to 100% across 460,000 GPUs, with a 300x token reduction and flat cost per query. This inverts the typical AI path: begin with an LLM (Software 3.0) for exploration, then harden deterministic parts into code (1.0) for reliability.

The approach relies on a strict hierarchy; flatter or more dynamic topologies would need adaptation. The broader lesson is that LLMs are weakest where data is large and well-structured, and the skill is knowing what to move out of the model.