AI Breakdown

• Nested Learning: The Illusion of Deep Learning Architecture

  In this episode, we discuss Nested Learning: The Illusion of Deep Learning Architecture by The authors of the paper "Nested Learning: The Illusion of Deep Learning Architecture" are: - Ali Behrouz - Meisam Razaviyayn - Peilin Zhong - Vahab Mirrokni. The paper introduces Nested Learning (NL), a new paradigm framing machine learning as multiple nested optimization problems with distinct context flows, explaining in-context learning in large models. It proposes more expressive optimizers as associative memory modules, a self-modifying sequence model that learns its own update rules, and a continuum memory system to improve continual learning. Together, these contributions enable a continual learning module called Hope, which shows promise in language modeling, knowledge integration, and long-context reasoning tasks.

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  8:22

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  8:22
• ARC Is a Vision Problem!

  In this episode, we discuss ARC Is a Vision Problem! by Keya Hu, Ali Cy, Linlu Qiu, Xiaoman Delores Ding, Runqian Wang, Yeyin Eva Zhu, Jacob Andreas, Kaiming He. The paper reframes the Abstraction and Reasoning Corpus (ARC) tasks as an image-to-image translation problem using a vision-centric approach. It introduces Vision ARC (VARC), a model based on a vanilla Vision Transformer trained from scratch on ARC data, which generalizes well to new tasks via test-time training. VARC achieves a 60.4% accuracy on the ARC-1 benchmark, outperforming previous scratch-trained methods and approaching human-level performance.

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  8:24

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  8:24
• Solving a Million-Step LLM Task with Zero Errors

  In this episode, we discuss Solving a Million-Step LLM Task with Zero Errors by Elliot Meyerson, Giuseppe Paolo, Roberto Dailey, Hormoz Shahrzad, Olivier Francon, Conor F. Hayes, Xin Qiu, Babak Hodjat, Risto Miikkulainen. The paper presents MAKER, a system that achieves error-free execution of tasks requiring over one million steps by decomposing them into subtasks handled by specialized microagents. This modular approach enables efficient error correction through multi-agent voting, overcoming the persistent error rates that limit standard LLM scalability. The findings suggest that massively decomposed agentic processes offer a promising path to scaling LLM applications to complex, large-scale problems beyond individual model improvements.

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  7:27

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  7:27
• DataRater: Meta-Learned Dataset Curation

  In this episode, we discuss DataRater: Meta-Learned Dataset Curation by Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver. The paper proposes DataRater, a meta-learning approach that estimates the value of individual training data points to improve dataset curation. By leveraging meta-gradients, DataRater optimizes data selection to enhance training efficiency on held-out data. Experiments demonstrate that filtering data with DataRater significantly boosts compute efficiency across various model scales and datasets.

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  9:20

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  9:20
• Mathematical exploration and discovery at scale

  In this episode, we discuss Mathematical exploration and discovery at scale by Bogdan Georgiev, Javier Gómez-Serrano, Terence Tao, Adam Zsolt Wagner. AlphaEvolve is an evolutionary coding agent that combines large language models with automated evaluation to iteratively generate and refine solutions for complex mathematical problems. It successfully rediscovered and improved known solutions across various math domains and can generalize results into universal formulas. When integrated with proof assistants, AlphaEvolve enables automated proof generation, demonstrating significant potential for advancing mathematical discovery and optimization.

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  8:12

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  8:12

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