AGI Ascension Autonomous Multi-Agent Coordination Loop #AGIAscension
SYSTEMS
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Memory Intelligence Agent: AI Learning from Experience Like Humans
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What if an AI could learn from its own memory like a human, getting smarter with every task? Researchers from East China Normal University, Shanghai AI Lab, and others present MIA: the Memory Intelligence Agent. It uses a "Manager-Planner-Executor" team. The Manager stores
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Improving concurrent workload capacity for AI systems
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sorry everyone, we're working on being able to allow more concurrent workloads!
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Multi-Agent Coordination: Building Economic Organism Stack for AGI
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Large multi-agent coordination is not primarily an “agent swarm” problem. It is a full-stack economic-organism problem. The stack is: Insight
→ Nova-Seeds
→ MARK
→ Sovereign
→ AGI Business
→ Marketplace
→ AGI Jobs
→ Agents
→ Validators / Council
→ Value Reservoir
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Coordination Substrate for AI Agents Jobs Validators and Governance
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We figured out the coordination substrate: how agents, jobs, validators, archives, markets, and governance can be arranged so useful work compounds. #AGIALPHA
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Organizational Adaptation to an AI-Native Future
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Top stories in AI today: – OpenAI retakes the frontier with GPT 5.5
– U.S. flags Chinese labs’ 'industrial-scale' AI theft
– Get newspaper brief every morning with Claude
– AI's biggest productivity winners are also most worried
– 4 new AI tools, community workflows, and more -
China’s PLA Adopts Unmanned Systems and Humanoid Robots
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Inside China's Tech Evolution: Unmanned Systems Reshape PLA Battle Drills! https://
youtu.be/JVIw8lHjr9M?si
=BQ1DCbYBqWnCYeEj
… via @YouTube #battle #humanoidtech #humanoid #robot #Robotics #AI #TechRevolution #TechInnovation #ArtificialInteligence #PhysicalAI @AlbertoEMachado @Eli_Krumova @postoff25 -
The Architectural Trade-off Between Retrieval and Learning Systems
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The moat math is brutal once you see it. A retrieval system plateaus at the quality of its extractor. As good on day 1,000 as day 1. And most of what it "knows" belongs to whichever lab hosts it. A learning system you control compounds every interaction into a corpus of (state
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Economic trade-offs in AI agent memory: retrieval vs. procedural
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Layer 3 is where the economics flip. Retrieval memory costs tokens on every call. The preference gets looked up, injected, re-reasoned — every time. Procedural memory costs zero tokens at inference. The behavior lives in the weights. The agent just acts correctly. At scale,
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Managing CPU and RAM consumption from AI agents
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My main use for the terminal is to work out what the hell is killing my CPU/ram. It is typically some playwright, heavy chrome tabs, could be computer use and some other random stuff. Agents are not very good in tidying up after themselves.