arXiv:2609.28409v1 Announce Type: cross
Abstract: Vector Symbolic Algebras project data structures into a hyperdimensional vector space through the application of their vector algebras to randomly ge...
By Mohamed Malek Abid, P. Michael Furlong
arXiv:2507. 11688v4 Announce Type: replace Abstract: Contemporary large models often exhibit behaviors suggesting the presence of low-level primitives that compose into modules with richer functionality, but these fundamental building blocks remain poorly understood.
By Travis Pence, Daisuke Yamada, Vikas Singh
arXiv:2608. 01528v1 Announce Type: new Abstract: Vector symbolic architectures (VSA) are widely used for reasoning in neuro-symbolic (NeSy) AI, yet high-dimensional codebooks often create severe memory bottlenecks that limit scalability and deployment.
By Weilun Wang, Wantong Li
RunningTensor generalizes linear attention and state‑space models by extending the recurrent memory from a second‑order tensor (matrix) to an order‑o tensor. The memory is updated via a rank‑1 outer product and read by contracting with o‑1 vector queries, with order‑2 recovering linear attention. Experiments on synthetic associative recall and real language tasks show that RunningTensor improves memory capacity from O(W²) to O(Wᵒ) and outperforms existing baselines.
By Luca Herranz-Celotti, Vincent Guigue
DanLing NestedTensor is a PyTorch tensor abstraction that embeds multi‑ragged structure directly into the tensor, allowing packed values to carry partition information and logical dimension order. This design enables broadcasting, feature transformations, and reductions to automatically respect ragged axes while preserving the same representation through autograd and both eager and compiled execution. Benchmarks on an A100 show significant speedups—up to 3.39× over padding for BERT models and 2.40–4.32× for a Pairformer‑style workload—while dramatically reducing peak memory usage.
By Zhiyuan Chen
arXiv:2606. 03465v1 Announce Type: cross Abstract: Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints.
By Artur Zagitov, Alexander Miasnikov, Maxim Krutikov, Vladimir Aletov, Gleb Molodtsov, Nail Bashirov, Artem Tsedenov, Aleksandr Beznosikov