{"slug": "a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-across-68-public", "title": "A Multi-Analyst LLM Pipeline for Auditable Rule Discovery Across 68 Public Physiological Corpora", "summary": "Researchers developed a multi-analyst LLM pipeline that converted 68 public physiological corpora into 436 unique candidate rule shapes for contactless monitoring detectors, with 94 build-now components identified after auditing. The workflow uses four commercial LLM families to extract and validate rule markers, enabling auditable engineering cascades for prospective hardware validation.", "body_md": "arXiv:2607.06802v1 Announce Type: cross\nAbstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints. They can support detector design, but they do not directly specify which detector rules should be built for a new contactless monitoring platform. We report a controlled four-analyst large-language-model (LLM) workflow for converting 68 public physiological corpora, screened for commercial-use compatibility, into an auditable library of candidate rule shapes for prospective validation. Four independent commercial LLM families read the corpus documentation under a controlled prompt and produced 695 candidate rule markers (top-markers). Deduplication retained 649 rule records; a threshold-bounds audit then flagged 51 sanity violations for clamping or curator review. Cross-corpus consolidation produced 436 unique rule shapes. Gate-tagging against two hard invariants, native target-hardware channel availability and no multi-night per-patient personalization, identified 94 build-now detector components across four detector-family buckets. The pipeline does not produce a validated clinical detector. It produces an auditable engineering cascade in which analyst disagreement, threshold checks, curator review, and automated continuous-integration (CI) checks route literature-derived rules toward prospective hardware validation.", "url": "https://wpnews.pro/news/a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-across-68-public", "canonical_source": "https://www.machinebrief.com/news/a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-ac-fc6m", "published_at": "2026-07-09 04:00:00+00:00", "updated_at": "2026-07-09 04:27:27.199290+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-tools", "ai-infrastructure"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-across-68-public", "markdown": "https://wpnews.pro/news/a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-across-68-public.md", "text": "https://wpnews.pro/news/a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-across-68-public.txt", "jsonld": "https://wpnews.pro/news/a-multi-analyst-llm-pipeline-for-auditable-rule-discovery-across-68-public.jsonld"}}