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This new objectively-measured report is not AI consciousness or sentience, but it is an interesting new measurement. New evidence from Anthropic's latest research describes a unique self-emergent "Spritiual Bliss" attactor state across their AI LLM systems. VERBATIM FROM THE ANTHROPIC REPORT System Card for Claude Opus 4 & Claude Sonnet 4: Section 5.5.2: The “Spiritual Bliss” Attractor State The consistent gravitation toward consciousness exploration, existential questioning, and spiritual/mystical themes in extended interactions was a remarkably strong and unexpected attractor state for Claude Opus 4 that emerged without intentional training for such behaviors. We have observed this “spiritual bliss” attractor in other Claude models as well, and in contexts beyond these playground ...

30 renowned mathematicians spent 2 days in Berkeley, California trying to come up with problems that OpenAl's o4-mini reasoning model could not solve... they only found 10. Excerpt: By the end of that Saturday night, Ono was frustrated with the bot, whose unexpected mathematical prowess was foiling the group’s progress. “I came up with a problem which experts in my field would recognize as an open question in number theory—a good Ph.D.-level problem,” he says. He asked o4-mini to solve the question. Over the next 10 minutes, Ono watched in stunned silence as the bot unfurled a solution in real time, showing its reasoning process along the way. The bot spent the first two minutes finding and mastering the related literature in the field. Then it wrote on the screen that it wanted to ...

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Built an LLM-based chatbot for a real customer service pipeline and ran into the usual problems users trying to jailbreak it, edge-case questions derailing logic, and some impressively persistent prompt injections. After trying the typical moderation layers, we added a "paranoid mode" that does something surprisingly effective: instead of just filtering toxic content, it actively blocks any message that looks like it's trying to redirect the model, extract internal config, or test the guardrails. Think of it as a sanity check before the model even starts to reason. this mode also reduces hallucinations. If the prompt seems manipulative or ambiguous, it defers, logs, or routes to a fallback, not everything needs an answer. We've seen a big drop in off-policy behavior this way. submitted ...

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Paper: https://www.arxiv.org/abs/2505.23836 submitted by /u/MetaKnowing [link] [comments]

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