The Benchmark With No Instructions — ARC-AGI-3 (winning team!)
Tim Scarfe travels to Zurich to sit down with the Tufa Labs ARC-AGI-3 team — founder Benjamin Crouzier, with Jeroen Cottaar, Dries Smit, Stefano Viel and Michal Tesnar — to work out what their leaderboard-topping system does and what the benchmark is really testing.The cut opens on the games: a walkthrough of the Locksmith game, where you read the rules of an unfamiliar world straight from raw frames. ARC-AGI-3 makes ARC interactive and agentic, so the model has to *discover* the goal rather than transduce a static grid. It stays easy for humans and breaks LLMs, and it runs through ever...
Test-Time Adaptation: the key to reasoning with DL (Mohamed Osman)
Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network.
SPONSOR MESSAGES:
***
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/
***
TRANSCRIPT + REFS:
https://www.dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFInal.pdf?rlkey=cjjmo7rgtenxrr3b46nk6yq2e&dl=0
Mohamed Osman (Tufa Labs)
https://x.com/MohamedOsmanML
Jack Cole (Tufa Labs)
https://x.com/MindsAI_Jack
How and why deep learning for ARC paper:
https://github.com/MohamedOsman1998/deep-learning-for-arc/blob/main/deep_learning_for_arc.pdf
TOC:
1. Abstract Reasoning Foundations
[00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview
[00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning
[00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture
[00:20:26] 1.4 Technical Implementation with Long T5 Model
2. ARC Solution Architectures
[00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions
[00:27:54] 2.2 Model Generalization and Function Generation Challenges
[00:32:53] 2.3 Input Representation and VLM Limitations
[00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration
[00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches
3. Advanced Systems Integration
[00:43:00] 3.1 DreamCoder Evolution and LLM Integration
[00:50:07] 3.2 MindsAI Team Progress and Acquisition by Tufa Labs
[00:54:15] 3.3 ARC v2 Development and Performance Scaling
[00:58:22] 3.4 Intelligence Benchmarks and Transformer Limitations
[01:01:50] 3.5 Neural Architecture Optimization and Processing Distribution
REFS:
[00:01:32] Original ARC challenge paper, François Chollet
https://arxiv.org/abs/1911.01547
[00:06:55] DreamCoder, Kevin Ellis et al.
https://arxiv.org/abs/2006.08381
[00:12:50] Deep Learning with Python, François Chollet
https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438
[00:13:35] Deep Learning with Python, François Chollet
https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438
[00:13:35] Influence of pretraining data for reasoning, Laura Ruis
https://arxiv.org/abs/2411.12580
[00:17:50] Latent Program Networks, Clement Bonnet
https://arxiv.org/html/2411.08706v1
[00:20:50] T5, Colin Raffel et al.
https://arxiv.org/abs/1910.10683
[00:30:30] Combining Induction and Transduction for Abstract Reasoning, Wen-Ding Li, Kevin Ellis et al.
https://arxiv.org/abs/2411.02272
[00:34:15] Six finger problem, Chen et al.
https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SpatialVLM_Endowing_Vision-Language_Models_with_Spatial_Reasoning_Capabilities_CVPR_2024_paper.pdf
[00:38:15] DeepSeek-R1-Distill-Llama, DeepSeek AI
https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B
[00:40:10] ARC Prize 2024 Technical Report, François Chollet et al.
https://arxiv.org/html/2412.04604v2
[00:45:20] LLM-Guided Compositional Program Synthesis, Wen-Ding Li and Kevin Ellis
https://arxiv.org/html/2503.15540
[00:54:25] Abstraction and Reasoning Corpus, François Chollet
https://github.com/fchollet/ARC-AGI
[00:57:10] O3 breakthrough on ARC-AGI, OpenAI
https://arcprize.org/
[00:59:35] ConceptARC Benchmark, Arseny Moskvichev, Melanie Mitchell
https://arxiv.org/abs/2305.07141
[01:02:05] Mixtape: Breaking the Softmax Bottleneck Efficiently, Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W.
http://papers.neurips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf
Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners
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 Performance
[00:37:23] 2.4 Tokenization and Numerical Processing Challenges
3. Advanced Training and Optimization
[00:45:15] 3.1 DFS Token Selection and Probability Thresholds
[00:49:41] 3.2 Model Size and Fine-tuning Performance Trade-offs
[00:53:07] 3.3 LoRA Implementation and Catastrophic Forgetting Prevention
[00:56:10] 3.4 Training Infrastructure and Optimization Experiments
[01:02:34] 3.5 Search Tree Analysis and Entropy Distribution Patterns
REFS
[00:01:05] Winning ARC 2024 solution using 12B param model, Franzen, Disselhoff, Hartmann
https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf
[00:03:40] Robustness of analogical reasoning in LLMs, Melanie Mitchell
https://arxiv.org/html/2411.14215
[00:07:50] Re-ARC dataset generator for ARC task variations, Michael Hodel
https://github.com/michaelhodel/re-arc
[00:15:00] Analysis of search methods in LLMs (greedy, beam, DFS), Chen et al.
https://arxiv.org/html/2408.00724v2
[00:16:55] Language model reachability space exploration, University of Toronto
https://www.youtube.com/watch?v=Bpgloy1dDn0
[00:22:30] GPT-4 guided code solutions for ARC tasks, Ryan Greenblatt
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
[00:41:20] GPT tokenization approach for numbers, OpenAI
https://platform.openai.com/docs/guides/text-generation/tokenizer-examples
[00:46:25] DFS in AI search strategies, Russell & Norvig
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-4th/dp/0134610997
[00:53:10] Paper on catastrophic forgetting in neural networks, Kirkpatrick et al.
https://www.pnas.org/doi/10.1073/pnas.1611835114
[00:54:00] LoRA for efficient fine-tuning of LLMs, Hu et al.
https://arxiv.org/abs/2106.09685
[00:57:20] NVIDIA H100 Tensor Core GPU specs, NVIDIA
https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/
[01:04:55] Original MCTS in computer Go, Yifan Jin
https://stanford.edu/~rezab/classes/cme323/S15/projects/montecarlo_search_tree_report.pdf