Episode

978: A Post-Transformer Architecture Crushes Sudoku (Transformers Solve ~0%)

Podcast: Super Data Science: ML & AI Podcast with Jon Krohn
Published: Mar 27, 2026
Duration seconds: 639
Processing state: processed
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Summary

Leading LLMs like o3-mini and Claude 3.7 Sonnet fail completely at solving extreme Sudoku puzzles, scoring effectively 0% accuracy. Pathway's new BDH architecture achieves 97.4% accuracy by using a post-transformer design focused on internalized reasoning and constraint satisfaction.

Topics

Transformer Architecture
BDH Architecture
Machine Learning
Constraint Satisfaction
Artificial Intelligence
Neural Networks
Pathway
LLM Reasoning

Highlights

Failure mode: Transformers struggle with constraint satisfaction because they process information token-by-token, locking in decisions without the ability to backtrack
Main idea: The BDH architecture uses sparse positive activations, activating only about 5% of neurons to mimic biological efficiency
Technical breakthrough: Unlike attention mechanisms, BDH is a state-based model that maintains and updates an internal state, similar to biological synaptic updates
Practical takeaway: Moving beyond text-based chain-of-thought toward 'generative strategy' could enable AI to solve complex problems in medicine, law, and operations
Current limitation: BDH has been demonstrated at a billion-parameter scale, and while promising, it has not yet reached the massive scale of frontier models like GPT-4

Chapters

1:00 The 0% Accuracy Problem: Leading LLMs fail at extreme Sudoku puzzles that humans can solve easily, exposing a fundamental weakness in transformer-based reasoning.
1:45 Sudoku as a Reasoning Benchmark: Why Sudoku serves as a perfect test for constraint satisfaction, search, and backtracking capabilities in AI.
2:25 The Transformer Bottleneck: An analysis of how token-by-token processing and limited latent space prevent Transformers from holding multiple candidate strategies in parallel.
3:55 Internalized Reasoning with BDH: Comparing the BDH architecture to a chess grandmaster who navigates search spaces through internalized patterns rather than verbalized steps.
5:20 Sparse Activations and Biological Plausibility: How BDH uses sparse positive activations to achieve efficiency and mimic the energy-saving mechanisms of the human brain.
6:10 State-Based Modeling: Exploring how BDH maintains an internal state through mechanisms related to Hebbian learning, moving beyond standard attention.
7:35 The Future of Generative Strategy: The potential for post-transformer architectures to move from summarizing text to generating complex, constraint-aware strategies.