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Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra
Use LLMs to turn ambiguous requests into search plans, then resolve entities with deterministic indexes and set operations. This avoids context bloat and silent misses at production scale.
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Source Summary
Phaidra’s direct LLM approach fell from **80% correctness at 64 GPUs** to about **30% at 460,000 GPUs**. Its replacement uses a planner model, pre-indexed hierarchy and deterministic set operations, maintaining **100% correctness** in the reported tests.
Practical Implication
Keep model judgment at the fuzzy boundary: interpret intent, choose scope and formulate filters. Move exact retrieval, counting, deduplication and intersections into code whenever your domain has a hierarchy, graph or schema.
Agent-Ready Context
Phaidra’s direct LLM approach fell from **80% correctness at 64 GPUs** to about **30% at 460,000 GPUs**. Its replacement uses a planner model, pre-indexed hierarchy and deterministic set operations, maintaining **100% correctness** in the reported tests. Keep model judgment at the fuzzy boundary: interpret intent, choose scope and formulate filters. Move exact retrieval, counting, deduplication and intersections into code whenever your domain has a hierarchy, graph or schema. The results come from Phaidra’s workload: six production systems plus scaled tests involving data-center equipment. The claim of perfect recall depends on accurate indexes, filters and structured plans; vague requests may still require model-generated search patterns.
Context Map
agentdata#harness-engineering#tool-use#agent-reliabilityUncertainty
The results come from Phaidra’s workload: six production systems plus scaled tests involving data-center equipment. The claim of perfect recall depends on accurate indexes, filters and structured plans; vague requests may still require model-generated search patterns.