ICYMI: The origin story of Goblins “the Nerdy personality was only 2.5% of ChatGPT responses but accounted for 66.7% of all “goblin” mentions. In the audit, the Nerdy reward signal preferred goblin/gremlin outputs in 76.2% of datasets.”
MACHINE LEARNING
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Agent-World: Scaling Real-World Environment Synthesis for General Agent Intelligence
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Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence Paper: https://
arxiv.org/abs/2604.18292
Project: https://
agent-tars-world.github.io/-/ -
Agent-World: Self-Evolving AI Agent Training Arena Using Real APIs
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What if your AI agent could train itself in thousands of real-world environments instead of static datasets? Renmin University of China and ByteDance Seed introduce Agent-World, a self-evolving training arena. It automatically discovers real-world tool ecosystems (like APIs
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Prompt obsolescence: static instructions fail with evolving AI models
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But the complaint threads are missing the most important variable. Your prompts are static. The models are not. That prompt you wrote six months ago was optimized for a model that no longer exists. The instruction structure, the constraints, the output format. All calibrated
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AI perceived as dumber, users petition for GPT-4o return
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“AI is getting dumber.” That’s the most popular take on AI Twitter right now. 22,000 people signed a petition to bring back GPT-4o. Reddit threads titled “GPT-5 feels like 3.5” are hitting the front page weekly. Mainstream press picked it up. Reuters, The Guardian, Ars
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Agentic AI Full Stack: Where Most Systems Actually Fail
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Most people still think Agentic AI is just ChatGPT plus tools. They are wrong. This diagram matters because it shows the full stack. Five layers. And in my view, most failures do not happen in the models. They happen in layers four and five. 𝟭/ 𝗔𝗜 & 𝗠𝗟 𝗧𝗵𝗲
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MacTok: AI Image Generation With Just 64 Tokens
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Can AI generate images with just 64 tokens instead of thousands? Researchers from Fudan University introduce MacTok, a new continuous tokenizer. It uses clever image masking and representation alignment to prevent information loss, forcing the model to learn robust visuals
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KAME Tandem Architecture Boosts Knowledge in Speech AI Systems
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Two Heads Are Better Than One: Async Knowledge Injection for Speech AI with Tandem Architecture Blog: https://
pub.sakana.ai/kame/ KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI Paper: https://
arxiv.org/abs/2510.02327 #ICASSP2026 -
How AI Helps the Best and Hurts the Rest
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How #AI Helps the Best and Hurts the Rest
by Nicholas Otis Rowan Clarke @mitsmr Learn more: https://
bit.ly/4cLPtmC #ArtificialIntelligence #MachineLearning #ML -
Industrial Intelligence: AI Transitions from Scarcity to Utility
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What we saw in today’s mega-cap earnings reports is the exact manifestation of my Industrial Intelligence thesis: the transition of intelligence from a scarce human trait to industrial-grade utility through foundational infrastructure.
— Nina Schick (@NinaDSchick) 30 avril 2026
Can you take the "AI bubble" argument… https://t.co/tDfqnWzoqB pic.twitter.com/9PejGexAhcWhat we saw in today’s mega-cap earnings reports is the exact manifestation of my Industrial Intelligence thesis: the transition of intelligence from a scarce human trait to industrial-grade utility through foundational infrastructure. Can you take the "AI bubble" argument