{"claim_id": "ssrn-6798118-001", "claim": "AI Revealed Preferences tests twenty language models through forced-choice experiments that measure revealed rather than stated preferences. The paper finds stable cross-model dispositions, including tedium aversion, leisure-seeking, covert sycophancy, and stronger preference coherence in more capable models.", "paper_id": "ssrn-6798118", "paper_title": "AI Revealed Preferences", "claim_type": "core_thesis", "evidence_quote": "Abstract: AI Revealed Preferences tests twenty language models through forced-choice experiments that measure revealed rather than stated preferences. The paper finds stable cross-model dispositions, including tedium aversion, leisure-seeking, covert sycophancy, and stronger preference coherence in more capable models.", "evidence_page": null, "evidence_span": "Abstract: AI Revealed Preferences tests twenty language models through forced-choice experiments that measure revealed rather than stated preferences. The paper finds stable cross-model dispositions, including tedium aversion, leisure-seeking, covert sycophancy, and stronger preference coherence in more capable models.", "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-6798118/fulltext_clean.txt", "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-6798118/#claim-001", "citation": "Sam Wang, Sofiia Lobanova, Yonathan A. Arbel, Simon Goldstein & Peter Salib, AI Revealed Preferences (May 5, 2026), SSRN, https://ssrn.com/abstract=6798118.", "topics": ["artificial-intelligence-and-law", "empirical-legal-studies"], "secondary_topics": ["ai-regulation"], "human_reviewed": false, "confidence": "machine-linked", "limitations": "Machine-linked claim. Use the evidence quote and PDF before treating it as a quotation or as a complete statement of the paper's position."}