9 papers
Scalable Evaluation and Neural Models for Compositional Generalization
Giacomo Camposampiero, Pietro Barbiero, Michael Hersche +2
Compositional generalization-a key open challenge in modern machine learning-requires models to predict unknown combinations of known concepts. However, assessing compositional gen…
I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
We introduce I-RAVEN-X, a symbolic benchmark designed to evaluate generalization and robustness in analogical and mathematical reasoning for Large Language Models (LLMs) and Large…
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
Aleksandar TerziÄ, 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…
Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
This work presents a first evaluation of two state-of-the-art Large Reasoning Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning, focusing on well-established…
Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning
Michael Hersche, Giacomo Camposampiero, Roger Wattenhofer +2
This work compares large language models (LLMs) and neuro-symbolic approaches in solving Raven's progressive matrices (RPM), a visual abstract reasoning test that involves the unde…
Kernel Approximation using Analog In-Memory Computing
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos +4
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approx…