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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is novel geometry applied to a real constraint: tiny models waste capacity on the output matrix. HypRiLM beats baselines on WikiText-2, which is promising. If you're deploying edge models or exploring parameter-efficient architectures, the manifold-based decoding is worth testing. The gains are solid but models this small are niche.
This is a compression technique with decent experimental results but no clear advantage over existing methods like LoRA or MoE. The claim of stability is unproven at scale. If you're constrained by memory in training or inference, it's worth a look, but the paper doesn't make a compelling case for why this beats alternatives.