Decentralized Collective World Model for Emergent Communication and Coordination Nomura et al.: https://
arxiv.org/abs/2504.03353 #ArtificialIntelligence #DeepLearning #MachineLearning
MACHINE LEARNING
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Decentralized Collective World Model for Emergent Agent Communication
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AI Transforms Radiology Practice Without Replacing Radiologists
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AI hasn’t killed radiology, but it is changing it https://
washingtonpost.com/health/2025/04
/05/ai-machine-learning-radiology-software/
… #AI #Radiology #healthcare #GenAI #IoT #TechNews -
LLMs in Medicine: Evaluations, Advances and Future
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LLMs in medicine: evaluations, advances, and the future – Dr. Tanishq Abraham https://
buff.ly/p4SHmZI
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Google integrates GitHub with Gemini for code chat
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Google is working on Github integration for Gemini. Users will be able to connect Github repository and chat with Gemini about its content. This feature is quite similar to "Code Folder", but should simplify the workflow quite a lot.
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Defining Openness in Machine Learning Systems: Fine Details
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In case you care about the fine details of what "open" means in the context of ML systems. https://
arxiv.org/abs/2405.15802 -
Companion to The Self-Assembling Brain Neural Networks
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Nice companion to “The Self-Assembling Brain: How Neural Networks Grow Smarter”
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LLM evaluation needs ten times more smart people working on it
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Ten times more smart people should work on LLM evaluation now!
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Open-Reasoner-Zero: Scaling Reinforcement Learning on Base Models
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Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model Hu et al.: https://
arxiv.org/abs/2503.24290 #ArtificialIntelligence #DeepLearning #MachineLearning -
Efficient Reasoning in LLMs: Balancing Performance and Cost
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9). A Survey of Efficient Reasoning for LLMs This survey focuses on reasoning economy in LLMs, analyzing how to balance deep reasoning performance with computational cost.
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Hidden Factual Knowledge Encoded in Large Language Models
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10). Hidden Factual Knowledge in LLMs This study introduces a framework to measure hidden knowledge in LLMs, showing that models encode significantly more factual information internally than they express in outputs, up to 40% more.