What if the next leap in AI isn't about better words, but moving beyond words entirely? A massive team from NUS, Tsinghua, Tencent, & others presents a unified survey on the "Latent Space." They argue AI's core reasoning is shifting from explicit token-by-token generation to
RESEARCH
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Difference Between AI Agent and Agentic AI Explained
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AI Agent and Agentic AI, what is the difference?#AI #AgenticAI #AIAgent #artificialintelligence #agenticeconomy @lexfridman @KirkDBorne @Ronald_vanLoon @erikbryn @antgrasso @sallyeaves @Nicochan33 @HaroldSinnott @mvollmer1 @marcusborba @CatherineAdenle @vinod1975 @YuHelenYu… pic.twitter.com/qdfqsHyyxc
— Amitav Bhattacharjee (@bamitav) 22 avril 2026AI Agent and Agentic AI, what is the difference? #AI #AgenticAI #AIAgent #artificialintelligence #agenticeconomy @lexfridman @KirkDBorne @Ronald_vanLoon @erikbryn @antgrasso @sallyeaves @Nicochan33 @HaroldSinnott @mvollmer1 @marcusborba @CatherineAdenle @vinod1975 @YuHelenYu
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Latent Reasoning Tuning: LLMs Reasoning Without Full Step Output
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What if LLMs could reason without writing out every single step? Researchers from Harbin Institute of Technology propose a new method called Latent Reasoning Tuning (LRT). Instead of generating long, costly text chains, LRT teaches the model to use compact internal
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Gut Microbiome Proteins Linked to Aging, Metabolic Diseases, and Medications
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Nearly perfect (if unnerving). This is first shot, and the only real issue is the double hour hand.
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Speculative Decoding Optimization for Mixture of Experts Models
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Get more from speculative decoding in MoE models
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Local AI Models Cannot Match Leading Labs Models
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Nah, never found a single local model that can replace the best labs’ models.
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MoE-Based LLMs Enhance Speculative Decoding Effectiveness
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New Technical Report from @EkagraRanjan
: Contrary to what you might expect, MoE-based LLMs make speculative decoding even more effective. Read more on our blog: -
AI Model API No Longer Reports Reasoning Tokens Separately
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https://
gist.github.com/simonw/0f1a370
92fd0814bae75050eced48889
… seemed to work – the problem is it no longer reports "reasoning" tokens as a separate line item from output tokens so I couldn't tell if reasoning had happened or not until I turned on the reasoning summary -
Image Model Achieves Unprecedented Detail Quality
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insane detail that I did not think possible in an image model in 10 years. congrats!!
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LLMs Autonomously Develop Hash-like Random Number Extraction Algorithms
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One of the most intriguing findings from the paper's analysis is that LLMs can autonomously devise random number extraction algorithms akin to hash functions (such as Sum-Mod or rolling hashes) within the context. The longer the inference model "thinks," the greater the