Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao, Jie Dong, Yuchen Liu, Caijun Jia, Bihui Yu, Junnan Zhu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, Wentao Zhang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

We introduce the task of text-to-diagram generation, which focuses on creating structured visual representations directly from textual descriptions. Existing approaches in text-to-image and text-to-code generation lack the logical organization and flexibility needed to produce accurate, editable diagrams, often resulting in outputs that are either unstructured or difficult to modify. To address this gap, we introduce DiagramGenBenchmark, a comprehensive evaluation framework encompassing eight distinct diagram categories, including flowcharts, model architecture diagrams, and mind maps. Additionally, we present DiagramAgent, an innovative framework with four core modules—Plan Agent, Code Agent, Check Agent, and Diagram-to-Code Agent—designed to facilitate both the generation and refinement of complex diagrams. Our extensive experiments, which combine objective metrics with human evaluations, demonstrate that DiagramAgent significantly outperforms existing baseline models in terms of accuracy, structural coherence, and modifiability. This work not only establishes a foundational benchmark for the text-to-diagram generation task but also introduces a powerful toolset to advance research and applications in this emerging area.

Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models.While early approaches have shown promising performance by focusing on visual features or leveraging large-scale pre-training, most existing evaluations rely on rigid output formats and objective metrics, thus ignoring the complex, real-world demands of practical chart analysis.In this paper, we introduce ChartMind, a new benchmark designed for complex CQA tasks in real-world settings.ChartMind covers seven task categories, incorporates multilingual contexts, supports open-domain textual outputs, and accommodates diverse chart formats, bridging the gap between real-world applications and traditional academic benchmarks.Furthermore, we propose a context-aware yet modelagnostic framework, ChartLLM, that focuses on extracting key contextual elements, reducing noise, and enhancing the reasoning accuracy of multimodal large language models.Extensive evaluations on ChartMind and three representative public benchmarks with 14 mainstream multimodal models show our framework significantly outperforms the previous three common CQA paradigms: instruction-following, OCRenhanced, and chain-of-thought, highlighting the importance of flexible chart understanding for real-world CQA.These findings suggest new directions for developing more robust chart reasoning in future research.

Hand-drawn sketches are a natural and efficient medium for capturing and conveying ideas. Despite significant advancements in controllable natural image generation, translating freehand sketches into structured, machine-readable diagrams remains a labor-intensive and predominantly manual task. The primary challenge stems from the inherent ambiguity of sketches, which lack the structural constraints and semantic precision required for automated diagram generation. To address this challenge, we introduce SketchAgent, a multi-agent system designed to automate the transformation of hand-drawn sketches into structured diagrams. SketchAgent integrates sketch recognition, symbolic reasoning, and iterative validation to produce semantically coherent and structurally accurate diagrams, significantly reducing the need for manual effort. To evaluate the effectiveness of our approach, we propose the Sketch2Diagram Benchmark, a comprehensive dataset and evaluation framework encompassing eight diverse diagram categories, such as flowcharts, directed graphs, and model architectures. The dataset comprises over 6,000 high-quality examples with token-level annotations, standardized preprocessing, and rigorous quality control. By streamlining the diagram generation process, SketchAgent holds great promise for applications in design, education, and engineering, while offering a significant step toward bridging the gap between intuitive sketching and machine-readable diagram generation.

Understanding how scientific ideas evolve requires more than summarizing individual papers-it demands structured, cross-document reasoning over thematically related research. In this work, we formalize multi-document scientific inference, a new task that extracts and aligns motivation, methodology, and experimental results across related papers to reconstruct research development chains. This task introduces key challenges, including temporally aligning loosely structured methods and standardizing heterogeneous experimental tables. We present ResearchPulse, an agent-based framework that integrates instruction planning, scientific content extraction, and structured visualization. It consists of three coordinated agents: a Plan Agent for task decomposition, a Mmap-Agent that constructs motivation-method mind maps, and a Lchart-Agent that synthesizes experimental line charts. To support this task, we introduce ResearchPulse-Bench, a citation-aware benchmark of annotated paper clusters. Experiments show that our system, despite using 7B-scale agents, consistently outperforms strong baselines like GPT-4o in semantic alignment, structural consistency, and visual fidelity. The dataset are available in https://huggingface.co/datasets/ResearchPulse/ResearchPulse-Bench

Digital human generation is a forward-looking field in technology. Despite significant progress in the generation of speaking facial videos, many challenges remain unaddressed. Issues such as unnatural head movements, distorted expressions, artifacts in generated videos, and uncoordinated limb movements persist. Most current efforts are focused on specific individuals, with enhancements often limited to head movements without further advancing the overall behavioral actions of digital humans. In this context, we introduce a new dataset, CFMD, and a novel model, SAM-Wav2lip++, capable of generating consistent, audio-synchronized lip and behavior action videos from a single reference image of any identity. This work features three main innovative components: (1) a contrastive lip-sync discriminator for precise lip synchronization, (2) a generator for the synthesis of sound-action consistency, and (3) the SAM module for facial refinement operations. Through extensive experiments and user studies, our results demonstrate that our model can synthesize digital human videos of impressively high perceptual quality that accurately sync lip movements and behavioral actions with the input audio, substantially outperforming the state-of-the-art baselines evaluations.

In recent years, there has been significant progress in text-to-image generation models. However, text struggles to accurately describe abstract concepts like shapes and sizes. Some methods have been proposed to enhance text prompt by incorporating image prompts. While they have shown effective improvements, they either require substantial fine-tuning costs or struggle to effectively integrate text and image information. In our study, we delve into the issue of the difficulty in integrating text and image information in decoupled cross-attention and conduct visual analysis. We identify the presence of background-related tokens in image features as a key factor affecting text fidelity. To address this issue, we develop an algorithm to filter out these tokens. Additionally, we observe differences in the attention of Unet layers to text prompts and image prompts. Based on this finding, we optimize the flow of image information to reduce interference with text information. In summary, we introduce a new topic-customized method that requires no repeated training. It trains a plug-and-play image prompt adapter with only 417M parameters, lightweight yet powerful, surpassing existing models in both text and image consistency. Our code and pre-trained checkpoints will be available at https://github.com/YZBPXX/DDCA.