you are joking, right? did you even peek at this study from Stanford?
RESEARCH
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Silencing Future Visions: Doug Engelbart’s Legacy and Innovation
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They usually try to get me to shut up about the future already, which is why Doug Engelbart got fired from his R&D lab.
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Model Diffing: Identifying Unique Risks in New AI Models
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If a new model shares a feature with a trusted model, that area probably doesn't need scrutiny. Model diffing isolates the features unique to the new model—where new risks are most likely to be located.
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Qwen vs Llama: CCP Alignment vs American Exceptionalism
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For example, when we compared Alibaba's Qwen to Meta's Llama, we found a "CCP alignment" feature unique to Qwen and an "American exceptionalism" feature unique to Llama.
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NVIDIA’s World Simulation AI Outperforms Google’s Capabilities
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Oh, that's not the only one that exists. NVIDIA showed me one running underneath their desk that simulated the world from a single picture and a single prompt, and it was fucking badass. Google does have a lot of advantages in the world, and I expect them to be showing off some
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World Models Discussion on Latent Space Podcast with Insights
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It was great to talk with @swyx and @sunfanyun on @latentspacepod about world models. 🔥 Below I give a few more long-form thoughts on what is needed for successful world models. 🧵👇 latent.space/p/moonlake Fan-Yun Sun (@sunfanyun) @chrmanning and I went on @latentspacepod to talk about world models. piped.video/oBWRHnggscM?si=NndE… — https://nitter.net/sunfanyun/status/2039968839551889601#m
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Twin AI Systems Integration Reveals Hidden Conflicts
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Your separate twins might be fighting each other without you knowing. Integration reveals the conflicts.
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Think-Anywhere: LLMs Reasoning at Any Token Position During Coding
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Happy to see Think-Anywhere hitting 🔥 Top-2 Hot Paper on alphaXiv and 🚀 Trending Paper on Hugging Face! LLMs think before coding, but humans don't just plan upfront. We pause and reason during implementation whenever things get tricky. Therefore, we propose Think-Anywhere: letting LLMs invoke reasoning at any token position during code generation, on demand. The model discovers on its own where thinking is needed. 📄 Paper: arxiv.org/abs/2603.29957 💻 Code: github.com/jiangxxxue/Think-… PS: We are currently seeking Research Assistants. Outstanding performers will be eligible for direct PhD admission or receive recommendations for both academic and industry positions. If you are interested, please send your CV to yihong.dong001@gmail.com. Let's work together to conduct impactful and meaningful research!
→ View original post on X — @askalphaxiv, 2026-04-03 21:12 UTC
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Aspire to quality argumentation in AI discourse
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Periodic public service announcement, which unfortunately all too often seems necessary in this joint: Always aspire to the top, not the bottom, of @paulg
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Claude Code Demonstrates Neurosymbolic AI Beyond Pure LLMs
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Claude Code is suprisingly good. BUt also pretty clear neurosymbolic, not a pure LLM (which is what i have advocated for decades). my beef is with pure LLMs, not neurosymbolic approaches.