1 citations · 2 across the 10 of their papers we have counts for
10 papers
Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning
Mar Gonzàlez I Català, Haitz Sáez de Ocáriz Borde, Davide Murari +3
Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accuracy, which ignores the path t…
Looped Language Models Improve Compositional Tool Calling
Andrei Cristian Popescu, Haitz Sáez de Ocáriz Borde, Pietro Liò
Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in composit…
Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readouts
Andrei Cristian Popescu, Haitz Sáez de Ocáriz Borde, Pietro Liò
Looped Transformers increase test-time computation by repeatedly applying a shared recurrent block. Learned halting objectives in looped Transformers typically use a single exit di…
The Stepwise Informativeness Assumption: When Do Entropy Dynamics Track Reasoning Correctness in LLMs?
Mar Gonzàlez I Català, Haitz Sáez de Ocáriz Borde, George D. Montañez +1
Recent work uses entropy-based signals at multiple representation levels to study reasoning in large language models, but the field remains largely empirical. From a theoretical pe…
Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling
Carlo Saccardi, Maximilian Pierzyna, Haitz Sáez de Ocáriz Borde +6
Kilometer-scale weather data is crucial for real-world applications but remains computationally intensive to produce using traditional weather simulations. An emerging solution is…
Algebraic Priors for Approximately Equivariant Networks
Riccardo Ali, Pietro Liò, Jamie Vicary
Equivariant neural networks incorporate symmetries through group actions, embedding them as an inductive bias to improve performance. Existing methods learn an equivariant action o…
Entropy-Lens: Uncovering Decision Strategies in LLMs
Riccardo Ali, Francesco Caso, Christopher Irwin +1
In large language models (LLMs), each block operates on the residual stream to map input token sequences to output token distributions. However, most of the interpretability litera…
Metric Learning for Clifford Group Equivariant Neural Networks
Riccardo Ali, Paulina Kulytė, Haitz Sáez de Ocáriz Borde +1
Clifford Group Equivariant Neural Networks (CGENNs) leverage Clifford algebras and multivectors as an alternative approach to incorporating group equivariance to ensure symmetry co…
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference
Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò
In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph…
Latent Graph Inference using Product Manifolds
Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero +1
Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows m…