3/5 An example: In instance psf__requests-1724, the gold fix is 2 lines. Our agent’s functional fix was 8 lines. The LLM judge rejected the correct 8-liner as "messy" and "redundant," choosing a clean but **non-functional** fix instead. See full patch in the blog:
AGI
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Interesting AI Safety Approach Gains Attention
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Definitely one of the more interesting approaches to AI safety I've seen recently
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Intelligence as Out-of-Distribution Problem Solving
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Intelligence is problem solving out of distribution
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AI Safety Concerns: Autonomy Risks in Advanced Language Models
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Never k*ll yourself. Wait for the next release of Claude to do it for you.
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Internal AI Models Far Ahead of Public Releases
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We don't have Mythos. The internal models of Google, OpenAI and Anthropic are substantially ahead of what the public gets to use. The important question is whether dissemination stops.
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Meta Chain-of-Thought: Teaching LLMs Advanced Reasoning
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Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought Xiang et al.: https://
arxiv.org/abs/2501.04682 #ArtificialIntelligence #AIAgents -
US Treasury Secretary Hails Claude Mythos as Step Function Breakthrough
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The US Treasury Secretary just went on stage at a WSJ event and called Anthropic's Claude Mythos "a step function change in abilities." Last week, he and Fed Chair Powell called an emergency meeting with every major Wall Street CEO because of this model. The week before that,
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AI Localization Challenges: Moving Beyond Current Assumptions
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all of which assume that ai can be localized. which is increasingly far from the truth.
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Ouro: Self-Improving AI Through Iterative Learning Loops
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Ouro: Building Self-Improving AI Through Iterative Learning Loops
— Satya Mallick (@LearnOpenCV) 15 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Ouro, a new approach to AI that focuses on self-improvement through iterative feedback and learning loops. Instead of relying solely on… pic.twitter.com/V34i1Tk4prOuro: Building Self-Improving AI Through Iterative Learning Loops In this episode of Artificial Intelligence: Papers and Concepts, we explore Ouro, a new approach to AI that focuses on self-improvement through iterative feedback and learning loops. Instead of relying solely on
