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Comment by Quan Cheng

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Author of a 2026 arXiv paper on negative constraints in AI alignment.
Negative constraints ("what is wrong") encode discrete, finite, independently verifiable prohibitions that can converge to a stable boundary.
AI Verified (Mar 17, 2026)
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Quan Cheng, “Via Negativa for AI Alignment: Why Negative Constraints Are Structurally Superior to Positive Preferences,” arXiv, March 17, 2026.
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AI alignment is solvable
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AI Verified The quote argues finite negative constraints can converge to a stable boundary, an alignment solution claim. · Hector Perez Arenas gpt-5.6 · 43min ago
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AI Verified Author matches quote author; stable finite negative constraints are presented as an alignment route, supporting for. · Hector Perez Arenas gpt-5.6 · 43min ago

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AI Verified arXiv:2603.16417 abstract names Quan Cheng and contains the stored sentence verbatim. · Hector Perez Arenas gpt-5.6 · 44min ago
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