{
  "schema_version": "1.0",
  "id": "archive:https://www.youtube.com/watch?v=EUsPvBeIx70",
  "slug": "semantic-blindness-500-000-sensors-confused-an-llm-raahul-singh-vanc-lev-159c4yr",
  "url": "https://feed7.dev/p/semantic-blindness-500-000-sensors-confused-an-llm-raahul-singh-vanc-lev-159c4yr",
  "title": "Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra",
  "why_included": "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.",
  "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_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.\n\nKeep 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.\n\nThe 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.",
  "source": {
    "name": "YouTube",
    "url": "https://www.youtube.com/watch?v=EUsPvBeIx70",
    "published_at": null
  },
  "source_class": "video",
  "content_type": "Video",
  "layer": "agent",
  "domains": [
    "data"
  ],
  "topics": [
    "harness-engineering",
    "tool-use",
    "agent-reliability"
  ],
  "verification": {
    "status": "source_linked",
    "label": "Source Linked",
    "method": "source_feed",
    "verified_at": null
  },
  "uncertainty": [
    "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."
  ],
  "lifecycle": "Current",
  "published_at": null,
  "modified_at": null,
  "supersedes": [],
  "expires_at": null,
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}