Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses.
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TRANSCRIPT + RESEARCH OVERVIEW:
https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0
Clem and Matthew-
https://www.linkedin.com/in/clement-bonnet16/
https://github.com/clement-bonnet
https://mvmacfarlane.github.io/
TOC
1. LPN Fundamentals
[00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview
[00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis
[00:06:55] 1.3 Induction vs Transduction in Machine Learning
2. LPN Architecture and Latent Space
[00:11:50] 2.1 LPN Architecture and Latent Space Implementation
[00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture
[00:20:25] 2.3 Gradient-Based Search Training Strategy
[00:23:39] 2.4 LPN Model Architecture and Implementation Details
3. Implementation and Scaling
[00:27:34] 3.1 Training Data Generation and re-ARC Framework
[00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search
[00:34:43] 3.3 Program Composition and Computational Graph Architecture
4. Advanced Concepts and Future Directions
[00:45:09] 4.1 AI Creativity and Program Synthesis Approaches
[00:49:47] 4.2 Scaling and Interpretability in Latent Space Models
REFS
[00:00:05] ARC benchmark, Chollet
https://arxiv.org/abs/2412.04604
[00:02:10] Latent Program Spaces, Bonnet, Macfarlane
https://arxiv.org/abs/2411.08706
[00:07:45] Kevin Ellis work on program generation
https://www.cs.cornell.edu/~ellisk/
[00:08:45] Induction vs transduction in abstract reasoning, Li et al.
https://arxiv.org/abs/2411.02272
[00:17:40] VAEs, Kingma, Welling
https://arxiv.org/abs/1312.6114
[00:27:50] re-ARC, Hodel
https://github.com/michaelhodel/re-arc
[00:29:40] Grid size in ARC tasks, Chollet
https://github.com/fchollet/ARC-AGI
[00:33:00] Critique of deep learning, Marcus
https://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf
Machine Learning Street Talk (MLST) 