1 citations · 1 across the 5 of their papers we have counts for
5 papers
Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
Sajad Movahedi, Vera Milovanović, Shlomo Libo Feigin +5
Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by loo…
Latent-DARM: Bridging Discrete Diffusion And Autoregressive Models For Reasoning
Lina Berrayana, Ahmed Heakl, Abdullah Sohail +3
Most multi-agent systems rely exclusively on autoregressive language models (ARMs) that are based on sequential generation. Although effective for fluent text, ARMs limit global re…
Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
Aleksandar Terzić, Nicolas Menet, Michael Hersche +2
Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the model's expressivity, as measured in terms of the…
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
Aleksandar Terzić, Michael Hersche, Giacomo Camposampiero +3
Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. Although these mode…
Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
Michael Hersche, Francesco di Stefano, Thomas Hofmann +2
Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge. This study focuses on efficiently sol…