arXiv Machine Learning
Jul 28

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions

arXiv:2506. 05678v3 Announce Type: replace Abstract: The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space models, reflects ongoing efforts to address the diverse temporal dependencies inherent in sequential data.

By Haotian Jiang, Zeyu Bao, Shida Wang, Qianxiao Li
arXiv AI
Aug 7

The Impossibility Triangle of Long-Context Modeling

arXiv:2605. 05066v2 Announce Type: replace-cross Abstract: We identify and prove a fundamental trade-off governing long-sequence models: no model can simultaneously achieve (i) per-step computation independent of sequence length (Efficiency), (ii) state size independent of sequence length (Compactness), and (iii) the ability to recall a number of historical facts proportional to sequence length (Recall).

By Yan Zhou