{
  "video_id": "2d3e46ab05ae41cf",
  "channel": "export-arxiv-org-rss-cs-ai",
  "title": "Reasoning, Code, or Both? How Large Language Models Handle Variations in Math Questions",
  "scores": {
    "depth": 2,
    "novelty": 2,
    "aria_relevance": 1,
    "production_ready": 1
  },
  "avg_score": 1.5,
  "verdict": "summary_only",
  "one_line_reason": "Solide empirische Studie zu LLM-Robustheit bei Math-Tasks mit klarem Befund (CoT > PAL/SBSC), aber ohne Aria-Relevanz (kein Multi-Agent, Memory, Eval-Design für Agent-Systeme) und begrenzte Umsetzbarkeit für Production-Brain.",
  "model": "claude-haiku-4-5-20251001",
  "cost_usd": 0.0016319999999999998,
  "triaged_at": "2026-05-27T15:01:45.171854+00:00"
}