arXiv subjects

André Martins

Publications and source records attributed to André Martins.

6 recordsLinked to original sources

arXiv2026

LanteRn: Latent Visual Structured Reasoning

While language reasoning models excel in many tasks, visual reasoning remains challenging for current large multimodal models (LMMs). As a result, most LMMs default to verbalizing perceptual content into text, a strong limitation for tasks requiring fine-grained spatial and visual understanding. While recent approaches take steps toward thinking with images by invoking tools or generating intermediate images, they either rely on external modules, or incur unnecessary computation by reasoning directly in pixel space. In this paper, we introduce LanteRn, a framework that enables LMMs to interleave language with compact latent visual representations, allowing visual reasoning to occur directly in latent space. LanteRn augments a vision-language transformer with the ability to generate and attend to continuous visual thought embeddings during inference. We train the model in two stages: supervised fine-tuning to ground visual features in latent states, followed by reinforcement learning to align latent reasoning with task-level utility. We evaluate LanteRn on three perception-centric benchmarks (VisCoT, V*, and Blink), observing consistent improvements in visual grounding and fine-grained reasoning. These results suggest that internal latent representations provide a promising direction for more efficient multimodal reasoning.

cs.CV↗

arXiv2026

EuroBERT: Scaling Multilingual Encoders for European Languages

General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models. Despite their wide applicability, encoders have been recently overshadowed by advances in generative decoder-only models. However, many innovations driving this progress are not inherently tied to decoders. In this paper, we revisit the development of multilingual encoders through the lens of these advances, and introduce EuroBERT, a family of multilingual encoders covering European and widely spoken global languages. Our models outperform existing alternatives across a diverse range of tasks, spanning multilingual capabilities, mathematics, and coding, and natively supporting sequences of up to 8,192 tokens. We also examine the design decisions behind EuroBERT, offering insights into our dataset composition and training pipeline. We publicly release the EuroBERT models, including intermediate training checkpoints, together with our training framework.

cs.CL↗

arXiv2026

AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese

Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translated benchmarks likely missing the variant's linguistic and cultural nuances. We introduce AMALIA, a fully open LLM that prioritizes pt-PT by using more high-quality pt-PT data during both the mid- and post-training stages. To evaluate pt-PT more faithfully, we release a suite of pt-PT benchmarks that includes translated standard tasks and four new datasets targeting pt-PT generation, linguistic competence, and pt-PT/pt-BR bias. Experiments show that AMALIA matches strong baselines on translated benchmarks while substantially improving performance on pt-PT-specific evaluations, supporting the case for targeted training and native benchmarking for European Portuguese.

cs.CL↗

arXiv2021

Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings

Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation. However, training non-monotonic models brings a new complication: how to search for a good ordering when there is a combinatorial explosion of orderings arriving at the same final result? Also, how do these automatic orderings compare with the actual behaviour of human translators? Current models rely on manually built biases or are left to explore all possibilities on their own. In this paper, we analyze the orderings produced by human post-editors and use them to train an automatic post-editing system. We compare the resulting system with those trained with left-to-right and random post-editing orderings. We observe that humans tend to follow a nearly left-to-right order, but with interesting deviations, such as preferring to start by correcting punctuation or verbs.

cs.CL↗

arXiv2021

Sparse and Structured Visual Attention

Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA). One drawback of softmax-based attention mechanisms is that they assign some probability mass to all image regions, regardless of their adjacency structure and of their relevance to the text. In this paper, to better link the image structure with the text, we replace the traditional softmax attention mechanism with two alternative sparsity-promoting transformations: sparsemax, which is able to select only the relevant regions (assigning zero weight to the rest), and a newly proposed Total-Variation Sparse Attention (TVmax), which further encourages the joint selection of adjacent spatial locations. Experiments in VQA show gains in accuracy as well as higher similarity to human attention, which suggests better interpretability.

cs.CL↗

arXiv2017

Asteroseismic constraints on Asymmetric Dark Matter: Light particles with an effective spin-dependent coupling

So far, direct detection searches have come empty handed in their quest for Dark Matter (DM). Meanwhile, asteroseismology arises as a complementary tool to study DM, as its accumulation in a star can enhance energy transport, by providing a conduction mechanism, producing significant changes in the stellar structure during the course of the star's evolution. The stellar core, particularly affected by the presence of DM, can be investigated through precise asteroseismic diagnostics. We modelled three stars including DM energy transport: the Sun, a slightly less massive and much older star, KIC 7871531 ($0.85 \, M_{\odot}$, $9.41 \, \text{Gyr}$), and a more massive and younger one, KIC 8379927 ($1.12 \, M_{\odot}$, $1.82 \, \text{Gyr}$). We considered both the case of Weakly Interactive Massive Particles, albeit with a low annihilation, and the case of Asymmetric DM for which the number of trapped particles in the star can be much greater. By analysing these models with asteroseismic separation ratios weighted towards the core, we found indications limiting the effective spin-dependent DM-proton coupling for masses of a few GeV. This independent result is very close to the most recent and most stringent direct detection DM constraints.

astro-ph.SR↗