Can AI Agents Agree? Frédéric Berdoz, Leonardo Rugli, Roger Wattenhofer: https://
arxiv.org/abs/2603.01213 #ArtificialIntelligence #AIAgents
SYSTEMS
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Can AI Agents Reach Consensus and Agreement?
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Future of AI: Single Agents Over Hierarchical Subagent Structures
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I think we rapidly move to a world where it's just gonna be an agent and subagent hierarchies will be less interesting. (but yeah, easier multi-win management would be neat)
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AI Scaling Crisis: Can Power Infrastructure Keep Pace?
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We obsess over AI model scaling laws. Nobody talks about the scaling crisis for the electricity to run them. 1 billion weekly LLM users generating billions of queries per day. Are we building AI faster than we can power it? pic.twitter.com/onf1zHDv01
— Lucian Fogoros (@fogoros) 22 mars 2026We obsess over AI model scaling laws. Nobody talks about the scaling crisis for the electricity to run them. 1 billion weekly LLM users generating billions of queries per day. Are we building AI faster than we can power it?
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Local LLM Bottlenecks: Bandwidth, Interconnect, and Inference Engines
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When running LLMs locally,
the bottleneck isn’t just “VRAM size” It’s: – memory bandwidth
– interconnect (PCIe vs NVLink vs RDMA)
– inference engine (vLLM, TensorRT-LLM, SGLang) Unified Memory is way slower than VRAM btw -
xAI Colossus data center to cost more, deliver more compute this year
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The xAI Colossus data center will cost more and deliver far more computing than that this year
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System 1 and System 2 reasoning paths in AI models
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Yeah, I think this is a possible path, system1 and system2. I already tried it with Gemini Live… and want to return to it some day.
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Understanding Multiple Forms of Intelligence in Ecosystems
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“There are 10 million other types of intelligence on this planet that we will never understand. The best we can do is participate in the collective intelligence which brings them all together as part of a singular ecosystem” open.substack.com/pub/george…
→ View original post on X — @nigewillson, 2026-03-21 14:17 UTC
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MSA: AI Models with Direct Long-Term Memory Integration
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Very impressive: MSA (memory sparse attentions) is a so exciting because it lets AI models directly store and reason over massive long-term memory inside their attention system, without relying on external retrieval or lossy compression, making them far more accurate and
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Deep dive series on Saining Xie’s seven-hour interview about AI
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I’ve started a new four-part deep dive series exploring a fascinating seven-hour interview with Saining Xie, hosted by Xiaojun Zhang. robonaissance.com/t/language… Saining Xie, cofounder and chief science officer of AMI Labs, believes the AI industry’s most successful technology is also its most seductive trap. His company has just raised $1.03 billion to prove it. 张小珺 Xiaojun Zhang (@zhang_benita) 和@sainingxie 一起挑战7小时播客！他刚和Yann LeCun踏上“世界模型”的创业旅程（AMI Labs）。这是他第一次Podcast、第一次访谈。 2026年2月雪后的一天，我们在纽约布鲁克林，从下午2点，开启了一场始料未及的马拉松式访谈，直到凌晨时分散去。 这篇访谈的中文标题叫做《逃出硅谷》，但他又不厌其烦地枚举了影响他学术生涯的每一个人，并反反复复口头描摹这些人的人物特征（侯晓迪、何恺明、杨立昆、李飞飞…）正是这些，让这篇“逃出硅谷”的对话充斥着人性的温度。 By the way, 下面是访谈的YouTube版本，我们提供了中英字幕。 And yes, 我们是在用播客给这个世界建模😎 A 7-hour podcast with Saining Xie. He has just begun a new journey on world models with Yann LeCun at AMI Labs. This was his first podcast appearance and his first long-form interview. A day after the snowfall in February 2026, in Brooklyn, New York, we started recording at 2 p.m. What followed became an unexpected marathon conversation that lasted until the early hours of the morning. The Chinese title of the interview is “Escaping Silicon Valley.” Yet throughout the conversation, he patiently listed the people who shaped his academic life, repeatedly sketching their personalities in vivid detail: Hou Xiaodi, Kaiming He, Yann LeCun, Fei-Fei Li, and others. These portraits are what give this “escape from Silicon Valley” conversation its human warmth. By the way, the YouTube version of the interview is below, with Chinese and English subtitles. And yes, we are using podcasts to model the world 😎 A 7-hour marathon interview with Saining Xie: World Models, AMI Labs, Ya… piped.video/rIwgZWzUKm8?si=edxa… 来自 @YouTube — https://nitter.net/zhang_benita/status/2033467851655512142#m
→ View original post on X — @shiqi_yang_147, 2026-03-21 09:54 UTC
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Edge Computing Integration Reduces Latency in Distributed Operations
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When connectivity integrates edge computing and unified control, organizations gain a clearer view of operations, reduce latency, and manage complexity with greater precision, especially in distributed environments like retail and logistics. https://t.co/Sycu2SbuBo
— Antonio Grasso (@antgrasso) 21 mars 2026When connectivity integrates edge computing and unified control, organizations gain a clearer view of operations, reduce latency, and manage complexity with greater precision, especially in distributed environments like retail and logistics.