Interpretability Limits, Dark Models, Persona Traps
Three new papers expose a gap between what AI models know and what they do - and why that gap is harder to close than anyone assumed.
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
OpenAI Aims for AI Research Intern by September 2026
OpenAI's chief scientist Jakub Pachocki has laid out a two-stage plan to deploy an autonomous AI research intern by September 2026 and a full AI researcher by March 2028, backed by $1.4 trillion in planned compute spending.
LTX-2.3 Review: Open-Source Video AI That Delivers
LTX-2.3 is a 22-billion-parameter open-source video and audio generation model from Lightricks that rivals closed commercial tools - at zero cloud cost.
Meta's Rogue AI Agent Triggered a Sev 1 Security Breach
An internal Meta AI agent posted to an employee forum without authorization, setting off a two-hour cascade that exposed sensitive internal systems to engineers who lacked clearance.
Anthropic's 81K Study: AI Hopes, Fears, and the Gap
Anthropic's largest qualitative study of 80,508 users across 159 countries reveals the gap between what people hope AI will do and what it actually delivers.
MiniMax M2.7 Claims to Automate Its Own Training
MiniMax's new 2,300B MoE model tops the Artificial Analysis Intelligence Index and claims to run 30-50% of its own RL research workflow autonomously.
Transformers as Bayes Nets, Memory at Scale, Agent Attacks
Three arXiv papers rethink transformer theory, expose fatal flaws in in-context LLM memory, and introduce grey-box agent security testing.
OpenAI Acquires Astral - uv and Ruff Join Codex
OpenAI is acquiring Astral, the startup behind Python's dominant uv package manager and Ruff linter, folding critical developer infrastructure into its Codex coding agent team.
Multi-Agent Constitution, Sleeper Defense, Skill RL
Three new arXiv papers tackle constitutional AI rule learning, sleeper agent defense for multi-agent pipelines, and skill-evolving reinforcement learning for math reasoning.
OpenAI's New Mini and Nano Slash GPT-5.4 Pricing
OpenAI released GPT-5.4 mini and nano on March 17, bringing near-flagship performance at 70% and 92% lower cost respectively.
Mistral Small 4 Review: One Model, Three Jobs
Mistral Small 4 packs reasoning, vision, and agentic coding into a 119B MoE under Apache 2.0 - a serious small-model contender at a price that's hard to ignore.
Hunter Alpha on OpenRouter - Is This DeepSeek V4?
A 1-trillion-parameter model called Hunter Alpha appeared anonymously on OpenRouter on March 11. Developers say it's DeepSeek V4 in disguise. The signals are strong but the precedent cuts both ways.
