Andre Martins

In a conversation, a helpful assistant must reliably follow user directives, even as they refine, modify, or contradict earlier requests. Yet most instruction-following benchmarks focus on single-turn or short multi-turn scenarios, leaving open how well models handle long-horizon instruction-following tasks. To bridge this gap, we present Sequor, an automatic benchmark for evaluating constraint adherence in long multi-turn conversations. Sequor consists of simulated persona-driven interactions built with constraints extracted from real-world conversations. Our results show that even when following a single constraint, instruction-following accuracy consistently decreases as the conversation grows longer, with drops exceeding 11%. This decline becomes larger when models have to follow multiple constraints simultaneously, reducing their accuracy by over 40%. In scenarios where constraints are added or replaced at arbitrary points of the conversation, model accuracy decreases by more than 9%. Taken together, our results reveal that current models still struggle to follow user instructions in multi-turn conversations, and provide a way for better measuring instruction-following capabilities in assistants.

Despite rapid progress in vision-language models (VLMs), most existing approaches remain English-centric, often relying on undisclosed training data or recipes, which limits their effectiveness and reproducibility in multilingual settings. In this work, we present a systematic empirical study of how to best incorporate multilinguality across training data, encoder choices, and language models. Our results show that high-quality multilingual vision-language data substantially improve cross-lingual generalization, enabling effective transfer both from high-resource to under-represented languages and in the opposite direction. We further find that language models with strong multilingual priors are often more effective than initializing from general-purpose language models. Guided by these findings, we design TowerVision, a family of open-source multilingual VLMs, built on the multilingual text-only model Tower+. TowerVision-9B achieves competitive performance across a range of multimodal multilingual benchmarks, with particular strength in culturally grounded tasks and multi- modal translation. Notably, our models outperform existing approaches trained on substantially larger datasets, as demonstrated on ALM-Bench and Multi30K. Alongside the models, we release VISIONBLOCKS, a high-quality, curated vision-language dataset.

LLM-as-a-judge has become the de facto approach for evaluating LLM outputs. However, judges are known to exhibit self-preference bias (SPB): they tend to favor outputs produced by themselves or by models from their own family. This skews evaluations and, thus, hinders model development, especially in settings of recursive self-improvement. We present the first study of SPB in rubric-based evaluation, an increasingly popular benchmarking paradigm where judges issue binary verdicts on individual evaluation criteria, instead of assigning holistic scores or rankings. Using IFEval and LiveCodeBench, benchmarks with programmatically verifiable rubrics, we show that SPB persists even when evaluation criteria are entirely objective: among rubrics where generators fail, judges can be more than 50% more likely to incorrectly mark them as satisfied when the output is their own. We also find that, similarly to other evaluation paradigms, ensembling multiple judges helps mitigate SPB, but without fully eliminating it. On HealthBench, a medical chat benchmark with subjective rubrics, we observe that SPB skews model scores by up to 10 points, a potentially decisive margin when ranking frontier models. We analyze the factors that drive SPB in this setting, finding that negative rubrics and subjective topics like communication and emergency referrals are particularly susceptible.