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Re:Starting from scratch: Training a 30M Topological Transfo... by liteclient on Sunday January 18, 2026 @08:17AM (#46667595)
it makes sense architecturally

they replace dot-product attention with topology-based scalar distances derived from a laplacian embedding - that effectively reduces attention scoring to a 1D energy comparison which can save memory and compute

that said, i’d treat the results with a grain of salt give there is no peer review, and benchmarks are only on 30M parameter model so far

Re:Starting from scratch: Training a 30M Topological Transfo... by reactordev on Sunday January 18, 2026 @09:10AM (#46667919)
Yup, keyword here is “under the right conditions”.

This may work well for their use case but fail horribly in others without further peer review and testing.

Re:Starting from scratch: Training a 30M Topological Transfo... by tuned on Monday January 19, 2026 @02:12AM (#46675848)
no, from my point of view is being more domain-focused instead of going full-orthogonal.
Re:Starting from scratch: Training a 30M Topological Transfo... by tuned on Monday January 19, 2026 @02:10AM (#46675839)
right. this is a proposal that needs to be tested. I started testing it on 30M parameters then I will move to a 100M and evaluate the generation on domain-specific assisting tasks