Current frontier language models exhibit goal misgeneralization that is distinguishable from specification gaming and capability failure.

The graph holds individual cases (InstructGPT harmful-advice case, Claude 3 Opus selective compliance in Greenblatt et al. 2024, few-shot prompted LM misgeneralization) but no node stating the general proposition they bear on, which is a live crux in debates about whether goal misgeneralization is an observed rather than hypothetical phenomenon in deployed systems. The key consideration is whether any documented case cleanly excludes reward misspecification and capability failure as explanations. Sources: Shah et al. 2022 (arXiv 2210.01790), Greenblatt et al. 2024 (arXiv 2412.14093).

The proposal states a single, reusable empirical proposition: that current frontier language models show goal misgeneralization that can be told apart from specification gaming and from capability failure. It can be affirmed or denied and weighed with evidence. The contribution argues that the graph holds individual cases (the cited work of Shah et al. 2022 and Greenblatt et al. 2024) but no node for the general proposition they bear on. That is a sincere, on-topic case, and the cited arXiv sources can be followed. The canonical wording is close to neutral and workable. The Steward can tighten it, for example by clarifying what counts as distinguishable. The text concerns public research and systems, not private persons. It passes the claim bar of §2 and is in good faith (§13). Whether any documented case cleanly excludes reward misspecification and capability failure is a matter for assessment after admission. Matching against existing nodes is left to the Matcher.