newsReddit r/MachineLearningTrust 52 · CommunityPublished 22d agoLive · 20d ago
I built a compiler that turns computation graphs into the weights of a vanilla transformer — no training anywhere [P]
I've been chasing the question of what algorithms a transformer can actually express -- separate from what it can learn. So I built a compiler: define a computation graph in ordinary Python, and it produces the weights of a transformer that executes the graph. The result is a standard Phi-3-architecture checkpoint that vanilla huggingface loads with no custom code and no trust_remote_code. Zero training in the pipeline. Write-up (origin + how the construc
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 53%The State-Prediction Separation Hypothesis →
- PossiblePossibly related (embedding) · 52%engineering87/llm-atlas →
- PossiblePossibly related (embedding) · 49%lucidrains/titans-pytorch →
- PossiblePossibly related (embedding) · 49%Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks →
- PossiblePossibly related (embedding) · 48%T^2MLR: Transformer with Temporal Middle-Layer Recurrence →
- PossiblePossibly related (embedding) · 45%Algebraic Decomposition Theory for Transformer Length Generalization →
Covers
Covers (incoming)
Related across the graph
repolucidrains/titans-pytorchpaperThe State-Prediction Separation HypothesispaperT^2MLR: Transformer with Temporal Middle-Layer Recurrencerepoengineering87/llm-atlaspaperInvariant Learning Dynamics of Transformers in Inductive Reasoning TaskspaperAlgebraic Decomposition Theory for Transformer Length Generalization