Episode

Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners

Podcast
Machine Learning Street Talk (MLST)
Published
Feb 12, 2025
Duration seconds
4144
Processing state
processed
Canonical source
https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/Daniel-Franzen--Jan-Disselhoff---ARC-Prize-2024-winners-e2upr5k
Audio
https://anchor.fm/s/1e4a0eac/podcast/play/98413172/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-1-12%2Ff8b7b2e7-5a61-710b-2650-1958e0115cec.mp3
JSON
/v1/public/podcasts/machine-learning-street-talk/episodes/daniel-franzen-jan-disselhoff-arc-prize-2024-winners
Markdown
/podcast/machine-learning-street-talk/daniel-franzen-jan-disselhoff-arc-prize-2024-winners.md

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Summary

Daniel Franzen and Jan Disselhoff, the "ARChitects" are the official winners of the ARC Prize 2024. Filmed at Tufa Labs in Zurich - they revealed how they achieved a remarkable 53.5% accuracy by creatively utilising large language models (LLMs) in new ways. Discover their innovative techniques, including depth-first search for token selection, test-time training, and a novel augmentation-based validation system. Their results were extremely surprising. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting! https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** Jan Disselhoff https://www.linkedin.com/in/jan-disselhoff-1423a2240/ Daniel Franzen https://github.com/da-fr ARC Prize: http://arcprize.org/ TRANSCRIPT AND BACKGROUND READING: https://www.dropbox.com/scl/fi/utkn2i1ma79fn6an4yvjw/ARCHitects.pdf?rlkey=67pe38mtss7oyhjk2ad0d2aza&dl=0 TOC 1. Solution Architecture and Strategy Overview [00:00:00] 1.1 Initial Solution Overview and Model Architecture [00:04:25] 1.2 LLM Capabilities and Dataset Approach [00:10:51] 1.3 Test-Time Training and Data Augmentation Strategies [00:14:08] 1.4 Sampling Methods and Search Implementation [00:17:52] 1.5 ARC vs Language Model Context Comparison 2. LLM Search and Model Implementation [00:21:53] 2.1 LLM-Guided Search Approaches and Solution Validation [00:27:04] 2.2 Symmetry Augmentation and Model Architecture [00:30:11] 2.3 Model Intelligence Characteristics and…