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Publication

2026

Math-PT: A Math Reasoning Benchmark for European and Brazilian Portuguese

PROPOR 2026·
Publication Machine Learning Natural Language Processing
The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and benchmark datasets. However, most mathematical reasoning evaluations exhibit a significant linguistic bias, with the vast majority of benchmark datasets being exclusively in English or (at best) translated from English. We address this limitation by introducing Math-PT, a novel dataset comprising 1,729 mathematical problems written in European and Brazilian Portuguese. Math-PT is curated from a variety of high-quality native sources, including mathematical Olympiads, competitions, and exams from Portugal and Brazil. We present a comprehensive benchmark of current state-of-the-art LLMs on Math-PT, revealing that frontier reasoning models achieve strong performance in multiple choice questions compared to open weight models, but that their performance decreases for questions with figures or open-ended questions. To facilitate future research, our benchmark dataset will be publicly released, along with the model outputs.

RSI @ ICLR 2026·
Publication Machine Learning AI Safety

Juan Belieni, Ana Carolina Erthal, Eliezer de Souza da Silva, Diego Mesquita

Machine unlearning enables the removal of specific knowledge from trained models without full retraining. While effective methods exist for single deletion requests, handling sequential requests in large language models (LLMs) remains underexplored. In this setting, we observe that gradient interference between successive unlearning steps degrades prior objectives. We propose ONPO (Orthogonal Negative Preference Optimization), which projects each step’s update onto the orthogonal complement of a low-dimensional subspace spanned by cached gradients from previous unlearning requests. This preserves prior unlearning objectives with minimal per-step overhead. On the TOFU benchmark, ONPO achieves a better trade-off between forgetting quality and model utility than existing methods.

ICLR 2026·
Publication Machine Learning

André Ribeiro, Ana Luiza Tenorio, Juan Belieni, Amauri Souza, Diego Mesquita

Sheaf neural networks (SNNs) leverage cellular sheaves to induce flexible diffusion processes on graphs, generalizing the diffusion mechanism of classical graph neural networks. While SNNs have been shown to cope well with heterophilic tasks and alleviate oversmoothing, we argue that there is further room for improving sheaf diffusion. More specifically, we show that SNNs do not allow nodes to independently choose how they cooperate with their neighbors, i.e., whether they convey and/or gather information to/from their neighbors. To address this issue, we first introduce the notion of cellular sheaves over directed graphs and characterize their in- and out-degree Laplacians. We then leverage our construction to propose Cooperative Sheaf Neural Network (CSNN). Additionally, we formally characterize its receptive field and prove that it allows nodes to selectively attend (listen) to arbitrarily far nodes while ignoring all others in their path, which is key to alleviating oversquashing. Our results on synthetic data empirically substantiate our claims, showing that CSNN can handle long-range interactions while avoiding oversquashing. We also show that CSNN performs strongly in heterophilic node classification and long-range graph classification benchmarks.