3 citations · 6 across the 15 of their papers we have counts for
16 papers
Evaluating Open-Weight E-Commerce Agents with Environment-Grounded Verification
Nimit Shah, Haitz Sáez de Ocáriz Borde
A shopping conversation has many routes to the same cart, and a task-success rate reduces all of them to one score. We build a deterministic and reproducible e-commerce environment…
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…
Soro: A Lightweight Foundation Model and Chatbot for Tajik
Stanislav Liashkov, Haitz Sáez de Ocáriz Borde, Azizjon Azimi +3
We present Soro, a family of Tajik-specialized conversational large language models (LLMs) designed for real-world deployment under tight compute and connectivity constraints in Ta…
Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Yicen Li, Ruiyang Hong, Anastasis Kratsios +2
Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is inadequate when the hierarchy…
Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule
Haitz Sáez de Ocáriz Borde, Pietro Innocenzi, Flavio Savarino +2
We develop a 3D aerothermodynamic simulator for the Orion reentry capsule at hypersonic speeds, a timely case study given its role in upcoming lunar missions. The large computation…
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…
DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer
Yizhe Wu, Haitz Sáez de Ocáriz Borde, Jack Collins +2
3D scene understanding for robotic applications exhibits a unique set of requirements including real-time inference, object-centric latent representation learning, accurate 6D pose…
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
Haitz Saez de Ocariz Borde, Alvaro Arroyo, Ismael Morales +2
Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure. Rather th…
Closed-Form Diffusion Models
Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon
Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training s…
Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces
Haitz Saez de Ocariz Borde, Alvaro Arroyo, Ismael Morales +2
Recent studies propose enhancing machine learning models by aligning the geometric characteristics of the latent space with the underlying data structure. Instead of relying solely…
Projections of Model Spaces for Latent Graph Inference
Haitz Sáez de Ocáriz Borde, Álvaro Arroyo, Ingmar Posner
Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias. Latent graph inference focuses on learning an adequate graph structure to diffuse informat…
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…
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression
Haitz Sáez de Ocáriz Borde, Federico Barbero
We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able…
Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks
Haitz Sáez de Ocáriz Borde
Memory replay may be key to learning in biological brains, which manage to learn new tasks continually without catastrophically interfering with previous knowledge. On the other ha…