{
  "id": 15905,
  "url": "https://arxiv.org/abs/2608.01711v1",
  "title": "Constructing Executable Analytical Knowledge Representations for Meta-Analysis Synthesis Using an Agentic Harness",
  "summary": "Meta-analysis synthesis highlights a fundamental challenge in knowledge-based scientific analysis: structured evidence does not by itself represent the analytical knowledge required for executable computation. Decisions about evidence assignment, analytical contrasts, outcome and time-point alignment, effect-size formulation, and methodological admissibility must be explicit before statistical execution. Existing automated approaches often embed these decisions in model outputs, generated code,",
  "authors": "Lingbo Li, Anuradha Mathrani, Teo Susnjak",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": "meta",
  "regions": null,
  "published_at": "2026-08-03T05:19:05.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15905",
  "original_url": "https://arxiv.org/abs/2608.01711v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}