4 citations · 8 across the 20 of their papers we have counts for
21 papers
Recursive Vision Language Models for General Symbolic Reasoning
Omid Nejati Manzari, Guillaume Lajoie, Hassan Rivaz
Hard symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs due to their fixed-depth autoregressive reasoning, which limits systematic searc…
Can In-Context Learning Support Intrinsic Curiosity?
Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the pro…
Simplifying the Modeling of Arbitrary Conditionals in Natural Language
Yinhan Lu, Eric Elmoznino, Léo Gagnon +3
Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right decoding and conditional likelihood com…
CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
Joachim Schaeffer, Thomas Jiralerspong, Alexander Panfilov +4
AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model. This partially tampers with the untru…
Causal Network Discovery from Interventional Count Data with Latent Linear DAGs
Yijiao Zhang, Hongzhe Li
The increasing availability of interventional data offers new opportunities for causal discovery, with gene perturbation studies providing a prominent example. Such data are typica…
Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers
Sarthak Mittal, Divyat Mahajan, Guillaume Lajoie +1
Modern learning systems increasingly rely on amortized learning - the idea of reusing computation or inductive biases shared across tasks to enable rapid generalization to novel pr…