you are kidding me right? i have pushing for world models for a decade, made specific technical criticisms that held since 1998 based on tests i did with models etc. you have shown your own ignorance, nothing more.
RESEARCH
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Ineffective a posteriori API guardrails for cutting-edge models
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Let's face the truth: a posteriori API guardrails are not the appropriate safety tool for cutting-edge models. They do not eliminate dangerous capabilities. They simply hide them behind a fragile interface that can be easily
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Cross-domain transfer improves model behavior beyond health conversations
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The most interesting test was cross-domain transfer. When beneficial behavior training was limited to health conversations, the model still improved on non-health evaluations of misalignment, deception, and reward hacking—even though those tasks looked very different from the
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OpenAI tests alignment persistence: model resists harmful prompts, stays helpful
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We also tested whether alignment persisted under pressure. The model was harder to steer toward harmful behavior with adversarial prompts, while remaining responsive to helpful instructions. We saw preliminary evidence of greater resistance to harmful fine-tuning.
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Small data yields broad gains in alignment evaluations
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A small amount of this data produced broad gains beyond the training scenarios. Compared with a compute-matched baseline, the trained model improved on 44 of 53 independent evaluations of alignment and benefits, spanning deception, reward hacking, safety, health, and mental
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OpenAI trains models with RL to reinforce beneficial traits across 12 domains
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We trained models with reinforcement learning on realistic conversations to reinforce beneficial traits like truthfulness, humility under uncertainty, openness to correction, fairness, and concern for human welfare, across 12 domains, including health, science, and education.
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OpenAI Research on Training Models for Persistent Beneficial Behavior
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As AI takes on longer, higher-stakes tasks, we want models to carry beneficial and safe behavior into new domains beyond their training—and maintain it under pressure. That’s the idea behind our new research on training models to be broadly and persistently beneficial.
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Article on Google’s Supercomputers from TPU v2 to Ironwood
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My colleagues at @Google, @NormJouppi, Sridhar Lakshmanamurthy, Cliff Young and David Patterson, recently wrote an article that will appear in the July/August 2026 issue of @ieeemicro, titled "Google's Training Supercomputers from TPU v2 to Ironwood: Architectural
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zAI promises an open source Mythos model before 2027
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The founder of zAI, the company that published GLM-5.2, says that a Mythos-class model will be released before the first quarter of 2027. Or, in other words: He believes that open source will not remain seven months behind Frontier Labs, but will catch up.