Multimodal Large Language Models (MLLMs) have shown advanced performance in vision-language tasks. However, existing multimodal reasoning models often suffer from excessive reasoning steps, leading to high computational costs and inefficiency. In this paper, we propose the Multimodal Adaptive Reasoning Model (MARS), which enables adaptive adjustment of the reasoning strategy based on question difficulty. Specifically, MARS adopts a three-stage training framework based on our constructed training dataset (MART): 1) CoT Masking Learning to enhance reasoning logicality by predicting masked reasoning steps. 2) Adaptive Reasoning Instruction Learning to train the model to skip or keep reasoning steps according to difficulty levels. 3) CoT Lightweight Reinforcement Learning with the Information Bottleneck Principle based GRPO algorithm to reduce CoT length while maintaining performance and generalizability. Results on both in-domain and out-of-domain datasets show that MARS significantly reduces the CoT length (90.2% decrease) while improving accuracy (0.54%), outperforming existing SOTA open-source and proprietary MLLMs.

Question answering (QA) tasks serve as a key benchmark for evaluating generation systems.Traditional rule-based metrics, such as accuracy and relaxed-accuracy, struggle with openended and unstructured responses.LLM-based evaluation methods offer greater flexibility but suffer from sensitivity to instructions, robustness issues, and high computational costs.To overcome these challenges, we introduce QAEval, a hybrid framework combining rule-based reliability with LLM-based adaptability.QAEval utilizes two high-quality datasets: QAExtract for short-answer extraction and QAScore for scoring model training.By integrating a Mixture of Evaluators model with Dynamic Load Balancing Optimization, QAEval enables accurate, cost-effective QA evaluation.Experimental results show it outperforms models like GPT-4o and Claude-3, achieving 92.3% accuracy with only 0.6B parameters.

Figure -to-Text (F2T) tasks aim to convert structured figure information into natural language text, serving as a bridge between visual perception and language understanding.However, existing evaluation methods remain limited: 1) Reference-based methods can only capture shallow semantic similarities and rely on costly labeled reference text; 2) Reference-free methods depend on multimodal large language models, which suffer from low efficiency and instruction sensitivity; 3) Existing methods provide only sample-level evaluations, lacking interpretability and alignment with expert-level multi-dimensional evaluation criteria.Accordingly, we propose F2TEval, a five-dimensional reference-free evaluation method aligned with expert criteria, covering faithfulness, completeness, conciseness, logicality, and analysis, to support fine-grained evaluation.We design a lightweight mixture-of-experts model that incorporates independent scoring heads and applies the Hilbert-Schmidt Independence Criterion to optimize the disentanglement of scoring representations across dimensions.Furthermore, we construct F2TBenchmark, a humanannotated benchmark dataset covering 21 chart types and 35 application domains, to support research on F2T evaluation.Experimental results demonstrate our model's superior performance and efficiency, outperforming Gemini-2.0and Claude-3.5 with only 0.9B parameters.

Scientific Figure Analysis (SFA) aims to derive analytical insights from figures while incorporating background instructions. Unlike conventional tasks such as figure captioning or description generation, which focus on extracting surface-level information from the sole visual modality, SFA requires an intelligent system to summarize key patterns, infer implications, and contextualize scientific findings from visual and textual inputs. It demands not only visual recognition but also the integration of scientific knowledge, multimodal understanding, and contextual reasoning. In this work, we introduce an SFA dataset, AnaFig, comprising 2,000 high-quality samples across 56 domains. All samples are evaluated by using human-aligned five-dimensional scoring criteria, resulting 10,000 human-annotated score labels. The AnaFig dataset facilitates the assessment of three critical capabilities of multimodal large language models (MLLMs): adherence to complex instructions, multimodal perception, and analytical summarization. By building a new benchmark with widely used MLLMs, this study contributes to scientific knowledge discovery and reasoning, fostering the alignment of MLLMs and human experts in scientific analysis.

Sarcasm poses a challenge in linguistic analysis due to its implicit nature, involving an intended meaning that contradicts the literal expression. The advent of social networks has propelled the utilization of multimodal data to enhance sarcasm detection performance. In prior multimodal sarcasm detection datasets, a single label is assigned to a multimodal instance. Subsequent experiments often highlight the superiority of multimodal models by demonstrating their improvements compared to unimodal models based on these unified labels across multiple modalities. However, our investigation revealed that numerous instances of sarcasm cannot be identified using a single modality. Humans employ the conflict between a statement and factual information as a cue to detect sarcasm, and these cues can stem from different modalities. Then, a unified label for a multimodal instance may be not suitable for the associated text or image. In this work, we introduce SarcNet, a multilingual and multimodal sarcasm detection dataset in English and Chinese, consisting of 3,335 image-text pair samples. We provide annotations for sarcasm in visual, textual, and multimodal data, respectively, resulting in over 10,000 labeled instances. The separated annotation schema for unimodal and multimodal data facilitates a more accurate and reasonable assessment of unimodal and multimodal models.