Aligning LLMs in low-resource multilingual settings faces a fundamental reward bottleneck: scalar rewards lack cultural generalization, while unstructured critiques remain noisy and unverifiable. To bridge this gap, we introduce a Structured Multilingual Reward Modeling Framework that extends Reinforcement Learning with Verifiable Rewards (RLVR) to subjective and open-ended tasks. The framework unifies three core components to transform abstract quality into concrete supervision: (1) a Structured Checklist Schema decomposing evaluation into granular universal reasoning steps and task-specific criteria; (2) Structured Generative Critique Modeling, which produces rubric-aligned critiques with grounded justifications; and (3) Adaptive Multilingual Reward Optimization, integrating reasoning quality and language consistency into a verifiable objective. We integrate this framework into a bootstrapped Group Relative Policy Optimization pipeline, augmented by length-aware normalization and variance stabilization to ensure stability. Extensive experiments on a newly constructed suite covering 7 subjective task categories across 50 low-resource languages demonstrate that this checklist-driven approach yields substantial improvements in reasoning capability and response quality, particularly in settings where traditional reward models exhibit significant degradation. We publicly release our models and the corresponding evaluation benchmark to facilitate further research. Our code is available at https://github.com/Shajiu/SGCM.

Low-resource multilingual OCR faces a dual challenge: complex script structures and severe data scarcity. In such settings, existing OCR models often struggle, as coarse visual representations combined with weak linguistic priors lead to frequent errors among visually similar characters.To address this, we present BASA (Beyond Atomic Sub-character Alignment), a OCR framework built upon high-resolution visual and language backbones with a novel glyph-aware interface. The core technical contribution is the Glyph-Aware Fine-grained Adapter (GAFA). Unlike standard linear projectors, GAFA employs learnable glyph prototypes to actively align sub-character structural primitives (e.g., strokes and radicals) with visual features, explicitly resolving topological ambiguities during vision–language alignment. To complement this, we introduce a two-stage curriculum learning strategy supported by a Glyph-Aware Reverse Synthesis pipeline, which generates large-scale multilingual training corpora with automatic, zero-cost component labels. Furthermore, we construct BASA-Bench, a representative benchmark spanning 11 languages with diverse script structures and 23 authentic scenarios. Experiments demonstrate that BASA achieves consistent improvements over strong OCR baselines, particularly on scripts with complex compositions. Our model and benchmark will be available at https://github.com/NcutLLM/BASA.

Text-Centric Visual Question Answering (TEC-VQA) is a critical research area that requires semantic interactions between objects and scene texts. However, most existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese. Although few works expanding multilingual QA pairs in non-text-centric VQA datasets through translation, which encounters a substantial “visual-textual misalignment” problem when applied to TEC-VQA. Moreover, the open-source nature of these benchmarks and the broad sources of training data for MLLMs have inevitably led to benchmark contamination, resulting in unreliable evaluation results. To alleviate this issue, we propose a contamination-free and more challenging TEC-VQA benchmark called Text-Centric Visual Question Answering in Multilingual Chinese Minority Languages(TVQACML), which involves eight languages, including Standard Chinese, Korean, and six minority languages. TVQACML supports a wide range of tasks, such as Text Recognition, Scene Text-Centric VQA, Document-Oriented VQA, Key Information Extraction (KIE), and Handwritten Mathematical Expression Recognition (HMER), featuring 32,000 question-answer pairs across 8,000 images. Extensive experiments on TVQACML across multiple MLLMs demonstrate the effectiveness of evaluating the MLLMs and enhancing multilingual TEC-VQA performance with fine-tuning.