5 papers
Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity
Loïc Cabannes, Pierre-Emmanuel Mazaré, Gergely Szilvasy +6
Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-cont…
Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility
Gergely Szilvasy, Manuel Faysse, Maria Lomeli +5
Under modern test-time compute and agentic paradigms, language models process ever-longer sequences. Efficient text generation with transformer architectures is increasingly constr…
Short window attention enables long-term memorization
Loïc Cabannes, Maximilian Beck, Gergely Szilvasy +6
Recent works show that hybrid architectures combining local sliding window attention layers and global attention layers outperform either of these architectures taken separately. H…
COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following
Swarnadeep Bhar, Omar Naim, Eleni Metheniti +4
Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detection of missing information, detect…
Stochastic activations
Maria Lomeli, Matthijs Douze, Gergely Szilvasy +7
We introduce stochastic activations. This novel strategy randomly selects between several non-linear functions in the feed-forward layer of a large language model. In particular, w…