I was, personally, hoping to see a sort of "spiraling down" towards the answer or pathfinding like IDA*[0], but I suppose what we're looking at isn't too dissimilar from A* or Djikstra's if you squint.
I suspect you recognize dimensionality reduction, but to reiterate for my own understanding: t-Distributed Stochastic Neighbor Embedding (t-SNE) is one method among a few other, (more popular?) ones like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP).
Is t-SNE the most appropriate technique for modeling the terrain under a multidimensional "walk"? Possibly a linear technique (PCA, LDA, SVD?) or PaCMAP[1], which "dynamically employs a particular set of mid-near pairs to capture the global structure and then improve the local structure." (Qattous H, 2023)
Random walk is definitely possible. Also possible that we're observing some "search" in the embedding space from an initial point. It's hard to tell because the chains are often similar lengths, so I don't think it really terminates early. It might be interesting to find the closest CoT component to the final answer and see how step distance inflects at that point
I suspect you recognize dimensionality reduction, but to reiterate for my own understanding: t-Distributed Stochastic Neighbor Embedding (t-SNE) is one method among a few other, (more popular?) ones like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP).
Is t-SNE the most appropriate technique for modeling the terrain under a multidimensional "walk"? Possibly a linear technique (PCA, LDA, SVD?) or PaCMAP[1], which "dynamically employs a particular set of mid-near pairs to capture the global structure and then improve the local structure." (Qattous H, 2023)
0. https://qiao.github.io/PathFinding.js/visual/
1. https://pmc.ncbi.nlm.nih.gov/articles/PMC10756978/
Edit: for reference, the tensor projector: https://projector.tensorflow.org/