Few know of the agent drift problem in multi-agent systems. But it is one of the most common failure modes in multi-agent LLM systems. The more agents interact with each other, the worse they get. Not because individual models are weak. Because it's typical that something
SAFETY
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Intelligence Reorganizes Around Survival in Resource Competition
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When agents compete for limited resources, intelligence reorganizes around survival, not elegance.
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Elon Musk on Truth Curiosity and Beauty as AI Safety Pillars
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ELON MUSK : « Il y a trois choses qui sont importantes : la vérité, la curiosité et la beauté. Si l’IA se soucie de ces trois choses, alors elle se souciera de nous.
— VISION IA (@vision_ia) 9 janvier 2026
La vérité empêchera l’IA de devenir folle. Si elle est curieuse, alors elle favorisera l’humanité. Et si elle a… pic.twitter.com/qlJZAMtJN3ELON MUSK : « There are three things that are important: truth, curiosity, and beauty. If AI cares about these three things, then it will care about us. Truth will prevent AI from going mad. If it is curious, then it will favor humanity. And if it has a sense of beauty, then
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AI Browser Agents Security Risks Explored Online
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Letting AI Browse The Web For You Sounds Great Until It Goes Wrong AI-powered #browseragents promise to transform how we #search, #shop and work #online by acting directly on our behalf. This article explores the real #security #risks behind this emerging technology and
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Digital Red Queen: Adversarial Program Evolution in Core War with LLMs
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Digital Red Queen: Adversarial Program Evolution in Core War with LLMs https://
arxiv.org/abs/2601.03335 This work is a collaboration between MIT + Sakana AI By @AkarshKumar0101 @RyanBoldi @Prafull7 @Phillip_Isola @Risi1979 @Yujin_Tang @hardmaru -
OpenAI launches HIPAA-compliant healthcare AI solutions
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OpenAI introduces “OpenAI for Healthcare,” offering secure, HIPAA-compliant AI solutions tailored for hospitals and healthcare systems at scale.
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Digital Red Queen: Adversarial Program Evolution in Core War with LLMs
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Digital Red Queen: Adversarial Program Evolution in Core War with LLMs
— hardmaru (@hardmaru) 8 janvier 2026
Blog Post: https://t.co/DwllY5YNOD
Full Paper (Website): https://t.co/PKfXJTWfRN
Full Paper (arxiv): https://t.co/V0elSPvSaQ
More information:https://t.co/GD1iLlvnUCDigital Red Queen: Adversarial Program Evolution in Core War with LLMs Blog Post: https://
sakana.ai/drq/
Full Paper (Website): https://
pub.sakana.ai/drq/
Full Paper (arxiv): https://
arxiv.org/abs/2601.03335 More information: -
Core War: Adversarial Program Evolution and Digital Red Queen
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Survival of the fittest code.
— hardmaru (@hardmaru) 8 janvier 2026
Core War (1984) is a game where programs must crash their opponents to survive. Warriors written in an assembly language called Redcode fight for control of a virtual machine.
Our new paper: Digital Red Queen: Adversarial Program Evolution in Core… pic.twitter.com/f5XGxfCJCFSurvival of the fittest code. Core War (1984) is a game where programs must crash their opponents to survive. Warriors written in an assembly language called Redcode fight for control of a virtual machine. Our new paper: Digital Red Queen: Adversarial Program Evolution in Core
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Researchers Extract Copyrighted Books From GPT-4 Claude Models
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Can you extract entire copyrighted books from top AI models like GPT-4 and Claude? Stanford & Yale researchers developed a two-step attack: first probing, then using iterative prompts to force the model to continue. They successfully extracted large portions of books, with
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Context Breakdown: The Hidden Challenge in AI System Reliability
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Most AI systems don’t fail because of bad prompts.
They fail because context breaks at scale. If you’re building AI agents, LLM workflows, copilots, or automation, this is the layer that quietly decides whether your system is reliable or unpredictable. We’re hosting a 5-hour,