Rethinking positional encoding as a geometric constraint rather than a signal in

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>Hi everyone! I'm sharing a paper I've been working on that investigates how different positional encoding schemes (learned absolute, sinusoidal,

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1vju3ym/i_never_understood_positional_encoding_until_i/"> <img alt="I never understood positional

<!-- SC_OFF --><div class="md"><p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2604.21395v2"><strong>https://arxiv.org/abs/2604.21395v2</strong></a></p> <p>For years,

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wjnr7d/260711666_how_to_tame_grokking_representation/"> <img alt="[2607.11666] How to Tame Grokking:

<!-- SC_OFF --><div class="md"><p>I’ve been reading about Geometric Deep Learning lately (the whole grids, graphs, groups, manifolds idea), and something clicked that i wanted to