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#ai https://arxiv.org/abs/2511.02824

#ai This is more than language models. Somehow many forecasting models also almost fall inside the realm. Somehow the root of this is the latent space. Time series models even classical ones, have enlarged latent space, which is more or less embedding patterns with higher dimensions. However this paper is a bit fishy. I just can't trust the proof of theorem 2.2. Language Models are Injective and Hence Invertible https://arxiv.org/abs/2510.15511

#ai I ran into this quite interesting paper when exploring embeddings of time series. In the past, the manifold hypothesis has always been working quite well for me regarding physical world data. You just take a model, compress it, do something in the latent space, decode it, damn it works so well. The latent space is so magical. To me, the hyperparameters for the latent space has always been some kind of battle between the curse of dimensionality and the Whitney embedding theorem. Then there comes the language models. The ancient word2vec was already amazing. It brings in the questioin of why embeddings works unbelievably well in language models and it bugs me a lot. If you think about it, regardless of the model, embedding has been working so well. This hints that language embeddings might be universal. There is the linear representation hypothesis, but it is weird as it is missing the global structure. This paper provides a bit more clarity. The authors used a lot of assumptions but the proposal is interesting in the sense that the cosine similarity we used is likely a tool that depends on the distance on the manifold of the continuous features in the backstage. https://arxiv.org/abs/2505.18235v1

#ai AI to juniors is more or less a "fuck you in particular" thingy. https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/

#ai https://x.com/Google/status/1981403118803046686

#ai https://www.agidefinition.ai/

#ai Souly, Alexandra, Javier Rando, Ed Chapman, Xander Davies, Burak Hasircioglu, Ezzeldin Shereen, Carlos Mougan, et al. 2025. “Poisoning Attacks on LLMs Require a Near-Constant Number of Poison Samples.” arXiv [Cs.LG]. arXiv. http://arxiv.org/abs/2510.07192.

#ai OpenAI open weight models. The benchmarks are amazing. https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4

#ai PedanticAI already partially supports A2A . Agent2Agent (A2A) Protocol https://a2aproject.github.io/A2A/dev/

#ai Well, I figure many people actually "think" like this. The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf

#ai Extending Minds with Generative AI | Nature Communications https://www.nature.com/articles/s41467-025-59906-9

https://ai-2027.com “We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.” 不管怎样，这个页面的 interaction 很棒 #ai