Anthropic’s Claude Source Code Leak Hands Competitors a Blueprint It Spent Billions to Build - PYMNTS.com
<a href="https://news.google.com/rss/articles/CBMi3wFBVV95cUxOUDB0VlMwRlVORmJJOEdGNzZZQld4dWxuMWJ1UmR1Q04tMndhdW5VYjRfMm9lakRmenJVb180S0hqR3BuQ3I1WWQ1SkJCdFMyZzlTU0dyWWJLREd3WkEyQjA1U3dtYUFKcjltYVVfajdpOUpuUEVtU2paR1JLMnFPNUZnUDdlR3dPLUNrM1J1dVlyTzlsWVpleFhSLVZSNDMzWV9yME1yQ2RxZ25RUG9uM19lU1FfNGZpM3hkbGtWZGZ4bWk0UlNySlZEX2xUek5RMng5c1l3b0lPTllocGI4?oc=5" target="_blank">Anthropic’s Claude Source Code Leak Hands Competitors a Blueprint It Spent Billions to Build</a> <font color="#6f6f6f">PYMNTS.com</font>
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AI Safety at the Frontier: Paper Highlights of February & March 2026
tl;dr Paper of the month: A benchmark of 56 model organisms with hidden behaviors finds that auditing-tool rankings depend heavily on how the organism was trained — and the investigator agent, not the tools, is the bottleneck. Research highlights: Linear “emotion vectors” in Claude causally drive misalignment: “desperate” steering raises blackmail from 22% to 72%, “calm” drops it to 0%. Emergent misalignment is the optimizer’s preferred solution — more efficient and more stable than staying narrowly misaligned. Scheming propensity in realistic settings is near 0%, but can dramatically increase from one prompt snippet or tool change. AI self-monitors are up to 5× more likely to approve an action shown as their own prior turn — driven by implicit cues, not stated authorship. Reasoning models
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AI Safety at the Frontier: Paper Highlights of February & March 2026
tl;dr Paper of the month: A benchmark of 56 model organisms with hidden behaviors finds that auditing-tool rankings depend heavily on how the organism was trained — and the investigator agent, not the tools, is the bottleneck. Research highlights: Linear “emotion vectors” in Claude causally drive misalignment: “desperate” steering raises blackmail from 22% to 72%, “calm” drops it to 0%. Emergent misalignment is the optimizer’s preferred solution — more efficient and more stable than staying narrowly misaligned. Scheming propensity in realistic settings is near 0%, but can dramatically increase from one prompt snippet or tool change. AI self-monitors are up to 5× more likely to approve an action shown as their own prior turn — driven by implicit cues, not stated authorship. Reasoning models
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