Don’t miss out on this fun challenge! You’d get to learn current frontier research in-depth and implement them while getting a chance to win prizes. Opportunities like this don’t come often!
MACHINE LEARNING
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V-JEPA 2.1: Video Learning Without Labeled Data
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V-JEPA 2.1: Learning Video Understanding Without Labels
— Satya Mallick (@LearnOpenCV) 22 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore V-JEPA 2.1, a next-generation video learning model that shifts away from traditional supervised training. Instead of relying on labeled datasets,… pic.twitter.com/IQ0y6G8KILV-JEPA 2.1: Learning Understanding Without Labels In this episode of Artificial Intelligence: Papers and Concepts, we explore V-JEPA 2.1, a next-generation video learning model that shifts away from traditional supervised training. Instead of relying on labeled datasets,
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Hermes Agent Features Learning Loop and Persistent Memory
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Yes, mostly. Hermes Agent already has a built-in “learning loop” that creates/refines skills from tasks and keeps persistent memory for self-improvement.
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Beyond Words: AI’s Shift to Latent Space Reasoning
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What if the next leap in AI isn't about better words, but moving beyond words entirely? A massive team from NUS, Tsinghua, Tencent, & others presents a unified survey on the "Latent Space." They argue AI's core reasoning is shifting from explicit token-by-token generation to
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Telexistence introduces robotic motion data factory for AI training
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Telexistence Introduces Large-Scale #Robotic Motion Data Factory for #AI Training
— Ronald van Loon (@Ronald_vanLoon) 22 avril 2026
via @ZappyZappy7#ArtificialIntelligence #MI #ML #Tech #Innovation pic.twitter.com/yjY3A8mxtmTelexistence Introduces Large-Scale #Robotic Motion Data Factory for #AI Training
via @ZappyZappy7 #ArtificialIntelligence #MI #ML #Tech #Innovation -
Latent Reasoning Tuning: LLMs Reasoning Without Full Step Output
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What if LLMs could reason without writing out every single step? Researchers from Harbin Institute of Technology propose a new method called Latent Reasoning Tuning (LRT). Instead of generating long, costly text chains, LRT teaches the model to use compact internal
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Speculative Decoding Optimization for Mixture of Experts Models
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Get more from speculative decoding in MoE models
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MoE-Based LLMs Enhance Speculative Decoding Effectiveness
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New Technical Report from @EkagraRanjan
: Contrary to what you might expect, MoE-based LLMs make speculative decoding even more effective. Read more on our blog: -
LLMs Autonomously Develop Hash-like Random Number Extraction Algorithms
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One of the most intriguing findings from the paper's analysis is that LLMs can autonomously devise random number extraction algorithms akin to hash functions (such as Sum-Mod or rolling hashes) within the context. The longer the inference model "thinks," the greater the