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cs.LG2026
Learning State-Tracking from Code Using Linear RNNs
Julien Siems, Riccardo Grazzi, Korbinian Pöppel +3
Over the last years, state-tracking tasks, particularly permutation composition, have become a testbed to understand the limits of sequence models architectures like Transformers a…
cs.LG2025
Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
Mark Schöne, Babak Rahmani, Heiner Kremer +3
State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neur…