6/ Bottom line: LLMs can imitate careful responses. But they don’t know what’s true. The better they get at sounding cautious, the harder it is to spot the fakes. That’s the real hallucination.
SAFETY
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How prompts add caution, uncertainty bias, and unverified tags
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5/ So what’s really happening? • Prompt adds cautious words to the context vector
• Model predicts next token with a bias toward uncertainty
• It still pulls from patterns of fake info in the training data
• And slaps an [unverified] tag on top -
Models add disclaimers instead of stopping hallucinations, a style issue.
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3/ Example: A prompt says: "Never present generated content as fact." The model doesn’t stop hallucinating. It just adds: “I cannot verify this information” – even if it’s entirely fake. This isn't honesty. It’s style.
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Asking AI to be cautious only mimics caution, not reliability
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2/ So when you prompt it to “be cautious” or “never state unverified info”… It doesn’t actually get more reliable. It just learns to write like someone trying to be cautious. Keyword: “trying.”
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Anthropic Endorses California AI Regulation Bill SB 53
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Anthropic is endorsing California State Senator Scott Wiener’s SB 53. This bill provides a strong foundation to govern powerful AI systems built by frontier AI companies like ours, and does so via transparency rather than technical micromanagement.
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Federal AI Safety Regulation vs State Patchwork Approach
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Frontier AI safety is best addressed at the federal level instead of a patchwork of state regulations. But powerful AI advancements won’t wait for consensus in Washington.
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OpenAI Tackles AI Hallucinations, Anthropic Settlement, New AI Tools
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Top stories in AI today: – OpenAI reveals why chatbots hallucinate
– Anthropic agrees to $1.5B author settlement
– Automate web monitoring with AI agents
– OpenAI’s own AI chips with Broadcom
– 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/openai-crack
s-ais-hallucination-code
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Deep Learning Generalization: Interpolation Versus Causal Understanding
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When you store your knowledge and skills as parametric curves (as all deep learning models do), the only way you can generalize is via interpolation on the curve. The problem is that interpolated points *correlate* with the truth but have no *causal* link to the truth. Hence
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AI Video Models Struggle With Complex Holographic Effects
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I love this retro holographic aesthetic.
— Bilawal Sidhu (@bilawalsidhu) 7 septembre 2025
Made the “good old fashioned way” w/ after effects.
Ai video models still struggle w/ effects involving a barrage of coherent symbols. pic.twitter.com/w7voa1tdQLI love this retro holographic aesthetic. Made the “good old fashioned way” w/ after effects. Ai video models still struggle w/ effects involving a barrage of coherent symbols.
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Why Language Models Hallucinate: Training and Evaluation
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1. Why Language Models Hallucinate The paper argues that hallucinations are not mysterious glitches but the predictable result of how LLMs are trained and evaluated.