Step 4: tool results

{
  "url": "https://cdn.openai.com/papers/weak-to-strong-generalization.pdf",
  "title": "Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision",
  "stance": "affirms",
  "context": "Appendix listing the assumptions an alignment plan built on weak-to-strong generalization would need; seen as a search excerpt, the sentence is truncated.",
  "speaker": "Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao et al. (OpenAI)",
  "claim_id": "9fdec99b-0bcc-4834-b4d9-001f7aaff136",
  "confidence": 0.85,
  "source_date": "2023-12",
  "verbatim_text": "alignment plan based on weak-to-strong generalization include: No deceptive alignment in base models. We assume that pretrained base models (or the"
}

{
  "url": "https://www.alignmentforum.org/posts/iELyAqizJkizBQbfr/an-alignment-safety-case-sketch-based-on-debate",
  "title": "An alignment safety case sketch based on debate",
  "stance": "affirms",
  "context": "The debate safety case sketch names exploration guarantees, i.e. a solution to models strategically withholding rewarded strategies during training, as a required missing piece.",
  "speaker": "Marie Davidsen Buhl, Jacob Pfau, Benjamin Hilton, Geoffrey Irving",
  "claim_id": "9fdec99b-0bcc-4834-b4d9-001f7aaff136",
  "confidence": 0.9,
  "publication": "AI Alignment Forum (also arXiv 2505.03989)",
  "source_date": "2025-05-08",
  "verbatim_text": "Exploration guarantees: We need a solution to exploration hacking (in which the debaters fail to explore certain highly rewarded strategies to avoid having them reinforced). ... We claim that the main failure mode here is exploration hacking (C4.2)."
}