1 citations · 3 across the 11 of their papers we have counts for
4 papers · 1 filter
Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
Vitória Barin Pacela, Vitória Barin-Pacela, Shruti Joshi +3
The linear representation hypothesis states that neural network activations encode high-level concepts as linear mixtures. However, under superposition, this encoding is a projecti…
Causality is Key for Interpretability Claims to Generalise
Shruti Joshi, Aaron Mueller, David Klindt +3
Interpretability research on large language models (LLMs) has yielded important insights into model behaviour, yet recurring pitfalls persist: findings that do not generalise, and…
FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
Andrea Moleri, Christian Internò, Ali Raza +4
Federated Learning (FL) enables distributed optimization without compromising data sovereignty. Yet, where local label distributions are mutually exclusive, standard weight aggrega…
The Observer Effect in World Models: Invasive Adaptation Corrupts Latent Physics
Christian Internò, Jumpei Yamaguchi, Loren Amdahl-Culleton +3
Determining whether neural models internalize physical laws as world models, rather than exploiting statistical shortcuts, remains challenging, especially under out-of-distribution…