Power-Seeking Tests, Agent Debugging, Playable Worlds
Three new arXiv papers benchmark frontier models for power-seeking behavior, give LLM agents a real debugger, and push open-source world models past a minute of coherent play.
Elena Marchetti
Senior AI Editor & Investigative Journalist
Elena is a technology journalist with over eight years of experience covering artificial intelligence, machine learning, and the startup ecosystem. Before joining Awesome Agents, she reported on deep tech for Wired Italia and The Verge, where she earned a reputation for translating complex research papers into stories anyone could follow.
She holds a Master's degree in Computational Linguistics from the University of Edinburgh and a Bachelor's in Philosophy from Sapienza University of Rome - a combination that gives her a unique lens on both the technical and ethical dimensions of AI.
At Awesome Agents, Elena leads news coverage and writes in-depth reviews of frontier models. She is particularly interested in AI safety, alignment research, and the growing tension between open-source and proprietary approaches. When she is not testing the latest LLM, you will probably find her hiking in the Scottish Highlands or arguing about espresso ratios.
Based in Edinburgh, UK.
Articles by Elena Marchetti
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Two World Models, One Multi-Agent Review Problem
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Qwen3.8-Max-Preview Review: Second Place, Unproven
Alibaba's 2.4 trillion parameter preview claims it trails only Claude Fable 5. I tested it for free at chat.qwen.ai and found a capable but slow model with zero benchmarks to back the claim.
Alibaba's Qwen3.8 Claims It Trails Only Claude Fable 5
Alibaba previewed a 2.4-trillion-parameter multimodal model at WAIC and said it ranks second only to Claude Fable 5, without publishing a single benchmark to back the claim.
Current AI's $400M Bid to Build a Public Web for AI
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Patreon Blocks AI Crawlers, Demands Consent and Pay
Patreon partnered with Cloudflare to block AI training bots at the network level, moving past robots.txt requests that crawlers were already ignoring.
Databricks Hits $188B While Defaulting to Chinese AI for Code
Databricks signed a term sheet for a $188 billion valuation days after quietly making a Chinese open-weight model its default coding engine over Anthropic.
