35 citations · 59 across the 13 of their papers we have counts for
6 papers · 1 filter
A Polyphonic Conception of AI Understanding
Matthieu Queloz, Pierre Beckmann
When a doctor, a judge, or an engineer must decide whether to trust an AI model's output, they cannot avoid asking what the model understands. Purely mathematical or statistical de…
World Modeling in Transformers
Pierre Beckmann, Matthieu Queloz, Andre Freitas
Behavioral failures can make a transformer appear to lack a world model even when it has learned faithful representations of its environment. We demonstrate this in TaxiGPT, a tran…
Why We Care About Understanding: Competence through Predictive Compression
Matthieu Queloz, Pierre Beckmann
What is the relation between understanding and compression, and why does human understanding take such a heavily compressed form? Across information theory, machine learning, and A…
Deep Learning Models Also Recall Features
Pierre Beckmann
Recent work in mechanistic interpretability has studied how large language models recall facts stored in their weights. This paper argues that factual recall points to something br…
SciR: A Controllable Benchmark for Scientific Reasoning in LLMs
Pierre Beckmann, Marco Valentino, Andre Freitas
Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction. Reliably evaluating LLMs on these in scientific settings is cur…
Rejecting Cognitivism: Computational Phenomenology for Deep Learning
Pierre Beckmann, Guillaume Köstner, Inês Hipólito
We propose a non-representationalist framework for deep learning relying on a novel method: computational phenomenology, a dialogue between the first-person perspective (relying on…