Attention Mechanisms in Machine Learning Infographic source: https://
linkedin.com/posts/kavishka
-abeywardana-01b891214_linear-attention-scales-beautifully-but-activity-7426665871839993856–hZL/
… Tutorial: https://
geeksforgeeks.org/artificial-int
elligence/ml-attention-mechanism/
… With NumPy and SciPy code: https://
machinelearningmastery.com/the-attention-
mechanism-from-scratch/
…
LLMS
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Resources and Code for Attention Mechanisms in ML
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Google could launch Gemini 3.1 Pro Preview
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BREAKING : GOOGLE MIGHT BE PREPARING GEMINI 3.1 PRO PREVIEW FOR RELEASE! The same reference has been spotted on the Artificial Analysys Arena earlier. Straight to 3.1
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Aletheia: Advanced AI Reasoning with Deep Think and Scaling
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The three main sources that power Aletheia are:
#1: an advanced version of Gemini Deep Think for tackling extremely hard reasoning problems
#2: a novel inference-time scaling law that extends from
Olympiad-level problems to PhD-level exercises
#3: intensive tool use such as -
Foundation Models Struggle with Advanced Mathematics Research
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Research-level mathematics draws on advanced techniques from vast literature, with papers often spanning dozens of pages. While foundation models possess a large knowledge base from pretraining, their understanding of advanced subjects remains superficial due to data scarcity,
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GLM-5 benchmark results available
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GLM-5 benchmark results are out – 77.8 on SWE-bench verified
– 50.4 on HLE with Tools
– 75.9 on BrowseComp -
Gemini Deep Think Accelerates Mathematical Scientific Discovery
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Here's the link to the joint blog post: https://
deepmind.google/blog/accelerat
ing-mathematical-and-scientific-discovery-with-gemini-deep-think/
…. See details of the two papers below! -
Five Thoughts on Kimi K2 Thinking Model
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5 Thoughts on Kimi K2 Thinking https://
buff.ly/UZ77hqY
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
MIT Backtracking Method Improves AI Agent Debugging Efficiency
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AI agents can be very effective when they use LLMs, but coding agents to work backwards to fix mistakes is time-consuming. MIT method executes AI agent programs by backtracking & making multiple attempts, helping coders work w/these systems efficiently: https://
bit.ly/45QNta0 -
Anticipating Performance Improvements Over GLM-4.7 Predecessor
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The upcoming days will tell once the reports are released. But I am assuming it's going to be better than their GLM-4.7 predecessor, which was Claude Sonnet 4.5-level
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Clarification: GLM-5 is the new version, not 4.5
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I think you misread: It's GLM-5 that is new, not 4.5.