α-AGI "α" typically represents the beginning or something foundational, giving it a sense of foundational excellence—"the Alpha of all AGIs." #AGI #AGIALPHA
@montreal_ai
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Overtrained Language Models Are Harder to Fine-Tune
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Overtrained Language Models Are Harder to Fine-Tune Springer et al.: https://
arxiv.org/abs/2503.19206 #ArtificialIntelligence #Deeplearning #MachineLearning -
Agentic Alpha Explorer Infinite Mode AI Agent
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∞ AGENTIC ALPHA EXPLORER (INFINITE MODE) ∞ #AGIALPHA
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Self-Organizing Agents in Open-ended Environments
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S-Agents: Self-organizing Agents in Open-ended Environments Chen et al.: https://
arxiv.org/abs/2402.04578 #ArtificialIntelligence #DeepLearning #MachineLearning -
Model Collapse in Deep Learning: New Research Insights
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Strong Model Collapse Dohmatob et al.: https://
arxiv.org/abs/2410.04840 #ArtificialIntelligence #DeepLearning #MachineLearning -
Google’s Prompt Engineering Whitepaper by Lee Boonstra
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"PROMPT ENGINEERING" Lee Boonstra, Google: https://
kaggle.com/whitepaper-pro
mpt-engineering
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Matrix Calculus for Machine Learning and Beyond Paper
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Matrix Calculus (for Machine Learning and Beyond) Paige Bright, Alan Edelman, Steven G. Johnson: https://
arxiv.org/abs/2501.14787 #ArtificialIntelligence #DeepLearning #MachineLearning -
Sample-Based Approach Improves Language Model Test-Time Alignment
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Sample, Don't Search: Rethinking Test-Time Alignment for Language Models Gonçalo Faria, Noah A. Smith: https://
arxiv.org/abs/2504.03790 #ArtificialIntelligence #DeepLearning #MachineLearning -
DeepSeek-R1 Reasoning: New Thoughtology for Large Language Models
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DeepSeek-R1 Thoughtology: Let's about LLM Reasoning Marjanović et al.: https://
arxiv.org/abs/2504.07128 #ArtificialIntelligence #Deeplearning #MachineLearning -
OLMoTrace: Tracing Language Model Outputs to Training Tokens
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OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens Liu et al.: https://
arxiv.org/abs/2504.07096 #ArtificialIntelligence #DeepLearning #MachineLearning